Understand how Technology is assisting towards Democratizing Development and making Innovation inclusive.

Low-code & No-code platforms

Everyone is talking about the innovative Low-code & No-code platforms, and being a tech-forward company, we here at Tarams have jumped on the bandwagon to establish ourselves as Caspio certified.

Caspio, the Low Code Application builder, provides a best-in-class, best-value platform for creating business applications with little to no coding.

These tools aren’t just changing how we build apps; they’re rewriting the rulebook, making app creation accessible to a broader audience.

Decoding the Tech Jargon

Low-Code vs. No-Code: The Tech Behind It

A Low-Code platform is a boon for developers seeking efficiency in the app development lifecycle. With its visual interface and automated processes, it minimizes the grunt work of manual coding, letting developers focus on the higher-level aspects.

On the flip side, a No-Code platform is a game-changer for those without coding skills. Its drag-and-drop simplicity and ready-made templates empower users to craft their apps without wrestling with code complexities.

Unraveling the Impact

Democratizing Development: Inclusive Innovation

These platforms break down traditional barriers, bringing a diverse range of contributors into the development fold. It’s not just for the coding elite; business analysts and project managers can now contribute to the creative process.

Agility Redefined: Swift Solutions for a Fast World

Business agility is a top priority, and no code platforms excel at speeding up app development. This agility allows businesses to adapt swiftly to market changes, user feedback, and competitive demands.

Cost Efficiency: Maximizing Resources Wisely

Beyond agility, these platforms make economic sense. By minimizing the need for extensive coding expertise, they optimize development costs, letting organizations allocate resources more strategically.

Fueling Creativity: Innovation Unleashed

Low-code & No-code platforms aren’t just tools; they’re catalysts for innovation. The ease of app creation encourages businesses to experiment without hefty investments, fostering a culture of creativity.

The Future Horizon: Tarams' Strategic Vision

AI Integration: Smartening Up the Platforms

Tarams envisions enhancing these platforms with AI, making them more intelligent and adaptable to the evolving demands of software development.

Beyond Basics: Handling Complex Applications

The future sees Tarams’ integrated platforms tackling even more complex applications, broadening their scope and underscoring their versatility.

Global Impact: Redefining Digital Innovation

Low-code & No-code platforms are revolutionizing global tech. As they evolve, businesses approach app development differently, making technology more accessible and ushering in an era of digital innovation.

Tarams sees the tremendous value Low-code & No-code platforms add to our services and we are currently certified in Caspio and were fortunate to be listed as one of their partners on their website.

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Google OAuth Review Process – for Restricted Scopes

What is OAuth ?

OAuth (Open Authorization) is an open standard authorization framework for token-based authorization on the internet. It enables an end user’s account information to be used by third-party services, such as Facebook and Google, without exposing the user’s account credentials to the third party.

Google OAuth Review Process

You are likely to receive an email as depicted here if you are an API developer.
The process can be broadly divided into two phases:

1. The OAuth review process
2. The security assessment

If your app accesses Gmail’s restricted scopes, you have to go through both these phases. More details here

1. The OAuth review process

It starts with initiating the review process on your Google Developer Console. You will have to go through a questionnaire which is mostly about helping Google understand the usage of the restricted scopes in your app. You only have to do this for the production version of the app. Lower environments can be marked as “internal” and they need not go through this process.

After you initiate the review, Google’s security team will reach out to you requesting a YouTube video to demonstrate the usage of restricted scopes in your app. Once you share the video, Google will either respond with an approval or a feedback email requesting more information/changes. We had some feedback from Google and we had to share a couple of videos before we got an approval from Google.

Listed below are a few pointers which might help you to reduce feedback from Google.

Google usually takes a long time to respond in this regard. Despite multiple follow ups we had to wait for a month or two to get response for some of these emails – Possibly because they had a lot of requests from app developers during that time.
Also, in general we felt there was some disconnect in their responses as it looked like every response from our end was reviewed by a different person at Google – we received an email stating that we have missed the deadline for initiating the security assessment weeks after we had initiated the process. However, Google did acknowledge the mistake on their end after we responded with the SOW that was already executed.

  • Follow the design guidelines given by Google for styling the sign in button https://developers.google.com/identity/branding-guidelines#top_of_page
  • Have a web page for your app which people can access externally, without having to sign in.
  • Ensure that users have access to your Privacy policy page from your home page. A link to this should be given on sign in and users should only be allowed to proceed on accepting the privacy policy.
  1. While recording the video, go through the “privacy policy” on sign in and demonstrate that users need to accept it before proceeding.
  2. Your policy should explicitly quote the use of all restricted scopes.
  3. The policy should also mention how and why the restricted scopes are being used. Who has access to this data and where is it stored? Can it be viewed by your support staff or it’s just used by the app and humans cannot access it.
  • While recording the video try to capture as much details as possible demonstrate the usage of Google’s restricted scope within your app.
  1. Code walkthrough wherever necessary Ex. Fetching OAuth token and its use
  2. Demonstrate the storage of sensitive data and usage of encryption

If Google is satisfied with all the details about your app and is convinced that your project is compliant with their policies, you will get an approval mail. You will also be informed if your app has to undergo a security assessment as depicted.

2. Security Assessment

The security assessment phase relatively involved more live discussions and meetings with the assessors and therefore the overall process is quicker. You have a dedicated team assigned to help you. Google gave us the contacts of 2 third-party security assessors.We reached out to both of them and felt that ‘Leviathan’ was better in terms of communication. They shared more information about the overall process and we were more comfortable going ahead with them.We had to fill in and sign a few documents before we got started, which involved

  • Filling up an SAQ(Self assessment questionnaire) – to understand about the app and the infrastructure.
  • Signing the SOW
  • Signing a mutual NDA

After which we made the payment and got started with the process. We had an initial introduction meeting where we were introduced to their team and our assessment process was scheduled. To give you a rough idea, our schedule was about 2 months after we had the initial discussions.As per the SOW, the assessment would include the following targets. These would possibly differ based on individual applications and the usage of the restricted scopes. For reference, our’s was an iOS app.

  • Website
  • RESTful APIs
  • Mobile Application (iOS)
  • External Facing Network
  • Developer Infrastructure
  • Policy & Procedure Documentation

The assessor would retest after we complete resolving all the vulnerabilities. The first retest is included in the SOW and additional retests are chargeable.The timeline we had before Google’s deadline was pretty tight and we wanted to understand from the assessor if we can do anything to increase our chances of getting it right on the first pass. The assessors were kind enough to share details about some of the tools they use for the penetration testing so that we could execute them ahead to understand where we stand and resolve as much as possible before the actual schedule.

Preparation for the assessment

As part of preparation for the assessment, you can use these tools which help you identify the vulnerabilities with your application and infrastructure. Also, ensuring that you have some basic policy documentation will save you some time.

Scoutsuite – It’s an open source multi-cloud security-auditing tool. You can execute this on your infrastructure. It will generate a report listing out all the vulnerabilities. Resolving as many as you can before the assessment would surely help.

Burpsuite – Burpsuite is not open source but you can either buy it or use the trial version. It’s a vulnerability scanner which scans all the API endpoints for security vulnerabilities. Executing Burpsuite and taking care of vulnerabilities marked as High or more will help significantly before going through the assessment. It’s recommended to run Burpsuite on your lower environments and NOT on production because Burpsuite tests every endpoint by calling it more than a thousand times. You will end up creating a lot of junk data on whichever environment you run Burpsuite on.

Policy Documentation – We were asked to share a whole set of documents before the assessment. We already had most of these documentations in place so it was not a problem for us. But, if you don’t have any documentation for your project, it would save some time to have some basic documentation for your project as a preparation. I have listed out a few here:

  • Software Development Guidelines
  • Network diagrams
  • Information security policy
  • Risk assessment policy
  • Incident response plan

We reached out to both of them and felt that ‘Leviathan’ was better in terms of communication. They shared more information about the overall process and we were more comfortable going ahead with them.

We had to fill in and sign a few documents before we got started, which involved

  • Filling up an SAQ(Self assessment questionnaire) – to understand about the app and the infrastructure.
  • Signing the SOW
  • Signing a mutual NDA

 

After which we made the payment and got started with the process. We had an initial introduction meeting where we were introduced to their team and our assessment process was scheduled. To give you a rough idea, our schedule was about 2 months after we had the initial discussions.

As per the SOW, the assessment would include the following targets. These would possibly differ based on individual applications and the usage of the restricted scopes. For reference, our’s was an iOS app.

  • Website
  • RESTful APIs
  • Mobile Application (iOS)
  • External Facing Network
  • Developer Infrastructure
  • Policy & Procedure Documentation

The assessor would retest after we complete resolving all the vulnerabilities. The first retest is included in the SOW and additional retests are chargeable.

The timeline we had before Google’s deadline was pretty tight and we wanted to understand from the assessor if we can do anything to increase our chances of getting it right on the first pass. The assessors were kind enough to share details about some of the tools they use for the penetration testing so that we could execute them ahead to understand where we stand and resolve as much as possible before the actual schedule.

Actual penetration testing from the assessor

The assessor initiated the process as per the schedule. The first thing they did was create a slack channel for communication with our team and theirs. We had to share with them the AppStore links, website details and necessary credentials for our infrastructure. They also shared a sharepoint folder for sharing all the documentation and reports. We started uploading all the necessary documents and in parallel they started the penetration testing and reviewing our infrastructure. Again, do NOT share the production environment for penetration testing as it will create a lot of junk data and may delete existing entities.

After two days of testing they shared an intermediate report and we started addressing the vulnerabilities. After about a week we got the final report of the vulnerabilities. We addressed all the vulnerabilities and shared the final report. Here are a few remediations that were suggested for us:

  • We had to add Contact details for users in our web page to report vulnerabilities
  • Enable Multi Factor authentication on our AWS logins
  • Requested for logs around Google OAuth token usage
  • Encryption on RDS, EBS volumes
  • Documentation demonstrating KMS(Key management system) usage.

Upon completion of the assessment, the assessor will provide a document containing the following components:

  • Executive summary, including a high-level summary of the analysis and findings and prioritized recommendations for remediation
  • A brief description of assessment methodologies.
  • A detailed discussion of analysis results, including relevant findings, risk levels, and recommended corrective action.
  • Appendices with relevant raw data, output, and reports from the analysis tools used during the engagement.

That was the end. Couple of days after the approval from the assessor, we got an approval email from Google.

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A Brief Overview of Quality Assurance and Testing

Quality Engineering or Software Quality Testing

Business success is achieved through the combined efforts of different teams working in cohesion within an organization. This success is a directly related to the individual success of each team and their roles.

A software product’s success also goes through the phases similar to those of an organization and each and every step – from conceptualization to release is essential and crucial towards its success. Quality Engineering or Software Quality Testing is one such crucial phase, however, sometimes it can be the most commonly disregarded and undervalued part of the development process.

We, here at Tarams – have a high regard towards quality engineering, and we believe the effort associated with testing is a justified investment and can ensure stability and reduce overall costs from buggy, poorly executed software. Highly qualified & intuitive quality testing engineers, who form the core of our team are well versed in different approaches of testing to further strengthen our resolve towards delivering healthy and error free software products.

This document explains in brief, the challenges faced during testing and our techniques to overcome them to deliver a high quality product.

Testing Life Cycle

A successful software product requires it to be tested thoroughly and consistently. At Tarams, we involve the Quality Engineering (QE) teams as early as the design phase. Our test architects start by reviewing the proposed software architecture and designs. They set up the test plans and test processes based on the architecture and technologies involved.

We emphasize using ‘Agile Development Methodology’. This methodology involves small and rapid iterations of software design, build, and test recurring on a continuous basis, supported by on-going planning. Simply put, test activities happen on an iterative, continuous basis within this development approach.

The above diagrams depicts the standard development life cycle. Quality Assurance (QA) through QE is involved in all the phases while, tailoring the main activities within the context of the system and the project is performed accordingly.

The stages below showcase the efforts towards ensuring quality of the product:

Test Planning

Test planning involves activities that define the objectives of testing and the approach for meeting test objectives within constraints imposed. Test plans may be revisited based on feedback from monitoring and control activities. At Tarams, our QA teams prepare the test plan and test strategy documents during this phase, which outlines the testing policies for the project.

Test Analysis

During test analysis, the business requirements are analyzed to identify testable features and define associated test conditions. In other words, test analysis determines “what to test” in terms of measurable coverage criteria. The identification of defects during test analysis is an important potential benefit, especially where no other review process is being used and/or the test process is closely connected with the review process. Such test analysis activities not only verify whether the requirements are consistent, properly expressed, and complete, but also validate whether the requirements properly capture customer, user, and other stakeholder needs.

Test Design

During test design, the test conditions are elaborated into high-level test cases, sets of high-level test cases, and other testware. So, while test analysis answers the question – “what to test?”, test design answers the question “how to test?”. As with test analysis, test design may also result in the identification of similar types of defects in the test basis. Also as with test analysis, the identification of defects during test design is an important potential benefit.

Test Implementation

During test implementation, the testware necessary for test execution is created and/or completed, including sequencing the test cases into test procedures in test management tools such as Zephyr, QMetry, TestRail etc. Test design and test implementation tasks are often combined. In exploratory testing and other types of experience-based testing, test design and implementation may occur, and may be documented, as part of test execution.

Test Execution

During test execution, test suites are run in accordance with the test execution schedule.

Test execution includes the following major activities:  

  1. Recording the IDs and versions of the test item(s) or test object, test tool(s), and testware
  2. Executing tests either manually or by using test execution tools
  3. Comparing actual results with expected results, analyzing anomalies to establish their likely causes (e.g., failures may occur due to defects in the code, but false positives also may occur
  4. Reporting defects based on the failures observed
  5. Logging the outcome of test execution
  6. Verifying and updating bi-directional traceability between the test basis, test conditions, test cases, test procedures, and test results

Test Completion

Test completion activities collect data from completed test activities to consolidate experience, testware, and any other relevant information. In the test completion phase, the QA team prepares the QA sign-off document, indicating if the release can be made to production, along with supporting data(for example test execution, defects found in release, open and closed defects, defects priority etc.).

Manual Testing

Manual testing is a ‘Software Testing Process’ in which test cases are executed manually without using any automated tool. Manual Testing is one of the most fundamental testing processes as it can find both visible and hidden defects of the software. This type of testing is mandatory for every newly developed software before automated testing. This testing requires great efforts and time, but it gives the surety of bug-free software.

The QA teams at Tarams starts testing either when testable (something which can be independently tested) part of the entire requirement is developed or when the entire requirement is developed. The first round of testing happens on small feature parts as they are ready, followed by an end-to-end testing round on another environment once all requirements are developed.

Mentioned below is an overview of the different testing approaches used at Tarams

Regression Testing

Software maintenance is an activity which includes enhancements, error corrections, optimization and deletion of existing features. These modifications may cause the system to work incorrectly. Therefore, Regression Testing is implemented to solve the problem. Regression test covers the end to end business use cases, along with edge use cases which may break application functionality if untested.

On every release, the QA team executes the regression test suite on the respective build manually, after having completed the testing for release items. QA team prepares the test execution report for each release. As the project grows in stability, we plan to automate these tests and get them executed as part of every build, and also plan to include that in the continuous integration pipeline.

Compatibility Testing

A mark of a good software is measured by how well it performs on a plethora of platforms. To avoid shipping a defective product which has not been tested rigorously on different devices the QA process will make sure that all features work properly across a combination of various devices, Operating Systems & Browsers.

This involves testing not only on different platforms but also on different versions of the same platform. This also includes the verification of backward compatibility of the platform.

Verification of forward & backward compatibility on different platform versions is smooth till the QA runs out of physical devices to test the product, this poses one of the major threats to the quality of any software as the device inventory cannot always be kept up-to-date with an ever increasing device models in the market.

This problem is overcome by looking into the usage analytics to comprehend all the platforms / browsers / devices used to access the product and using a premium cloud service such as SauceLabs to perform the testing. Both these services provide a virtual and physical device access for testing. However, there are some limitations that are inherent with the device farms such as – testing applications with video/audio playback functionalities, video/audio recording, lag in the actions and the responses over the network.

Whenever there are updates made to APIs, in the case of mobile applications QA team tests the older versions of the mobile application to ensure that those are also working smoothly with the updates in the API.

Performance Testing

Performance testing is a form of software testing that focuses on how a running system performs under a particular load. This is not about finding software bugs or defects. Performance testing is measured according to benchmarks and standards.

As soon as several features are working, the first load tests should be conducted by the quality assurance team. From that point forward, performance testing should be part of the regular testing routine each day for each build of the software.

Our QA teams have performed performance testing for a B2C mobile application which consisted of buying and getting an item delivered at doorstep. The major functionalities of the application were to search for a product across stores and be able to place an order for a product and get it delivered. While the delivery executive is on their way to deliver the product, the customer can track the delivery.

The following performance aspects were tested for the project

  • API/Server response
  • Network performance – under different bandwidths like WiFi, 4G, 3G
  • A range of reports is configured to be generated post the build runs, like, Aggregate graphs, Graph results, Response time, Tree results & Summary report.

We leverage the inbuilt performance analyzer in XCode (Instruments) and can also enable monitoring in ‘New Relic’.

Machine Learning Models Testing

Machine Learning (models) represents a class of software that learns from a given set of data and then makes predictions on the new data set based on its learning. The usage of the word “testing ” in relation to Machine Learning models is primarily used for testing the model performance in terms of accuracy/precision of the model. It can be noted that the word “testing” means different for conventional software development and Machine Learning models development.

Our QA team has been working on a B2C product discovery application, where all the purchases made by a user from multiple stores gets discovered and displayed on the application. There are multiple applications of machine learning in the application for the following aspects –

  1. Product recommendation
  2. Product Categorization
  3. Product Deduplication

When there are any failures in QA results where certain data couldn’t be successfully processed, that set of data is fed into the machine learning model with appropriate details. For example, if the system couldn’t categorize certain products, then the product details are fed into the machine learning model, so as to enrich the model in future categorizations.

Data Analytics Testing

Data Analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

The QA (with the help of developers) performs testing of the app to make sure that all the scenarios have sufficient analytics around them and capture accurate data. This user behavior data will be the basis for major product decisions around growth, engagement etc. This will also come in handy in debugging certain scenarios.

One of our projects that had the ‘Firebase Analytics’ implemented captured the user events on each page. The data gathered was then segregated and analysed to find the usage patterns to make the product better.

Automation Testing

Automated testing differs from manual testing by the simple difference of testing being done through an automation tool. In this form of testing, lesser time is needed in exploratory tests and more time is needed in maintaining test scripts while increasing overall test coverage.

As discussed earlier, the size of a regression test suite would be exhaustively large once the product achieved optimal stability. Manually executing the regression tests at this stage consumes a considerable amount of time & resources. To solve this problem we often look towards automating the testing process and inturn Automation Testing

Our automation design follows the below process

Test Tools Selection

The right ‘Test Tool’ selection, largely depends on the technology the ‘Application Under Test’ is built on. So here at Tarams, a thorough proof of concept is conducted before selecting the automation tool conclusively.

We have used Selenium to automate the testing of multiple web applications, while using different languages such as Java, Python, TypeScript etc.

Planning, Design & Development

After selecting a tool for automation, the QA moves towards planning the specifics required for implementation such as – Designing the Test framework, Test scripts, Test bed preparation, Schedule / Timeline of scripting & execution and the deliverables.

This phase also includes the QA sorting the test suite to find all the automation candidates that will eventually be automated. In some of the projects the QA team has achieved test automation coverage of approximately 70%.

Test Execution

Once automation test scripts are ready, they are added into the automation suite for execution using Jenkins on cloud devices or the Selenium grid while a collective report with the detailed execution status is generated.

The generation of automation reports is done by the tool itself or using some external libraries like ‘Extent Report’. This is a continuous process of developing and executing test cases.

Maintenance

As new functionalities are added to the System Under Test with successive cycles, Automation Scripts need to be added, reviewed and maintained for each release cycle. The process of updating the automation code to be relevant with application changes consumes around 5-10% of QA bandwidth on average.

Architecture

Our QA teams have developed generic automation framework, that can be used across multiple projects for Selenium automation. The framework is versatile in handling different possible exceptions and failures, at the same time provides the capability to connect with APIs of multiple external systems to be able to compare the data across the systems. Below are a few outlining functionalities of our test framework,

  • The framework is designed to generate any test data that may be required while automating the test.
  • Abstract reusable methods readily available to be implemented in any project.
  • Extendable to add any new features in the future if necessary.
  • Easy to read HTML test reports
  • Automated test status updation in test management tool

API Testing

While developers tend to test only the functionalities they are working on, testers are in charge of testing both individual functionalities and a series or chain of functionalities, discovering how they work together from end to end.

The re-usable API test harness which has been designed from ground-up can also be used while testing the front end, since Selenium library can only automate the UI, it creates a challenge where we need to fetch data from an external source.

API tests are introduced in the early stage of checking staging and dev environments. It’s important to start them as soon as possible to ensure that both endpoints and the values they return are displayed properly.

The QA uses several tools to verify the performance & functionality of the API’s such as Postman tool, RestAssured java library or pure java implementation of http methods.

Some of the tests performed on API are,

  • Functionality Testing — the API works and does exactly what it’s supposed to do.
  • Reliability Testing — the API can be consistently connected to and lead to consistent results
  • Load Testing — the API can handle a large amount of calls

QA in Production

Quality assurance team doesn’t end their responsibility with pre-release testing and release. The QA team keeps a close eye on the software running in production.

Since an application can be used by hundreds of thousands of users in vastly different environments and since there are a multitude of 3rd party integrations in-play, it is very critical to identify field issues and replicate them in house at the earliest.

Also, the usage statistics generated in production is used by the QA to enhance the test scenarios and check for extra use-cases which should be added to the test suite.

Test Data Management

There are different types of data required for effectively testing any software product. Effective management of test data plays a vital role in the testing of any application. This is critical in ensuring that testing is performed with the right set of data; and in ensuring that the testing time is well managed by pre-defining / storing / cloning test data. While data that does not have external dependencies are easier to generate/mock with the help of certain scripts, the other types of data are harder to generate.

Wherever possible, Tarams manages to get test data directly from the production by taking a dump of the database and using it as test data. Since some of the production databases may contain sensitive user information, we focus on data-security and ensure the data is not compromised.

Test Environments

Testing is primarily performed in QA and PROD environments. For stress / load testing, we use the STAGING environment which is a perfect replica of the production in it’s infrastructure.

Once a build is found to meet the expectations for the release, the build is then deployed on the next higher environment. Different environments are required for testing, so as to ensure that the activities in one of the environments doesn’t impact the data or the test environment required for other activities; for example, we need to ensure that the stress/load testing doesn’t impact the environment required to perform the functional testing of the application.

Source Code Management (SCM)

SCM allows us to track our code changes and also check the revision history of the code which is used when you need to roll back the changes. With Source Code Management, both the developers & the QA pushes the code into a cloud repository such as GitHub or on-premise servers such as Bitbucket.

Troubleshooting becomes easy as you know who made the changes and what were those changes. Source Code Management helps streamline the software development process and provides a centralized source for your code.

SCM is started as soon as the project is initiated from the point of initial commit till the application is fully functional with regular maintenance.

Continuous Integration

As the code-base grows larger, adding extra functional plugs raise the threat of breaking the entire system. This problem has been overcome by the introduction of ‘Continuous Integration (CI)’. With every push of the code, the CI tool such as Jenkins triggers an automation build to run smoke tests; which help in detecting errors if any, early in the process.

The QA also has several scheduled automation triggers which are configured and run according to requirements. The CI process will ensure that the code is deployable at any point or even automatically releasing to production if the latest version passes all automated tests.

Listed below are some of the advantages of having Continuous Integration:

  1. Reduces the risk of detecting bugs once the code is deployed to production
  2. Better communication when sharing a code to achieve more visibility and collaboration
  3. Faster iterations; as we release code often which reduces the gap between the application in production and the one the developer is working on will be much smaller

Conclusion

This paper gives a brief overview of our efforts in delivering high-quality software products through rigorous levels of testing in parallel with our development efforts.

Our QA expertise in – manual testing (full stack), End-to-End test automation, API automation and performance testing for both mobile and web applications, enhances the efficiency of the products while keeping the user in mind.

Authors

Chethan Ramesh

A Senior QA Engineer at Tarams with over 7 years of experience in full stack testing, and automation testing. Chethan has been associated with Tarams for more than 2 and a half years.

Pushpak Bhattacharjee

Pushpak Bhattacharjee is a QA manager at Tarams with over 9 and a half years of experience in full stack testing and automation testing and has been associated with Tarams for more than 2 and a half years now.

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Big Data for a huge change

Today, millions of users click pictures, make videos, send texts, and communicate with each other through various platforms. This results in a huge amount of data that is being produced, used and re-used everyday.

In 2013, the total amount of data was 4.4 zettabytes. This is likely to increase towards 44 zettabytes by 2020 (One zettabyte is equivalent to 44 trillion gigabytes)

All of this ‘Data’ is a precious resource, which can be harnessed and understood by deploying certain techniques and tools. This is the gist of Big Data and Data Analytics. Using Big Data and Data Analytics, many organizations are able to gain insights into the customer mindsets, trending topics, imminent next Big things, etc.,

Let us take a look at how Big Data Applications has influenced various industries and sectors, and also the ways in which they are benefited from the same.

Education

The education industry is required to upkeep and maintain, a significant amount of data regarding faculties, courses, students and results. Requisite analysis of this data can yield insights that enhance the operational efficiency of the educational institutions. This can be put to avail in numerous ways.

Based upon a student’s learning history, customized schemes can be put into place for him/her. This would enhance the student results in entirety. Similarly, the course material too can be reframed based upon what students learn quicker, and the components of the course material that are easier to grasp. As a student’s progress, interests, strengths, and weaknesses are grasped in an improved manner, it helps suggest career paths most lucrative for him.

Healthcare

Healthcare industry generates a significant amount of data and Big Data helps the industry make a prediction for epidemic outbreaks in advance. It may also help postulate preventive measures for such a scenario.

Big Data may help with the prediction of disorders at an early stage, which can act as a preventive measure against any further deterioration, and makes the treatment more effective as well.

Government

Governments of all nations come across a significant amount of data every day, as enabled by sources such as the various databases pertaining to their citizens and geographical surveys.

By putting Big Data Analytics to the best avail, the Governments can come to recognize the areas that are in need of immediate attention. Similarly, challenges such as exploitation of energy resources and unemployment could be dealt with better. Centering down upon tax evaders and recognizing deceit becomes easier as well. Big Data also makes occurrences of food-based infections easier to determine, presume, and work upon.

Transportation

There are various ways in which Big Data makes transportation more efficient and easier, and the technology withholds a vast potential in the field.

As an example, Big Data can be used to access commuters’ requirements of different routes and can help implement route planning which reduces the waiting times. Similarly, traffic congestion and patterns can be predicted in advance, and accident-prone areas can be identified and worked upon in a suitable manner.

Uber is a brand that puts Big Data Analytics to avail. They generate the data about their vehicles, each trip it makes, the locations and drivers. This can be used for making predictions about the demand and availability of cabs over a certain area.

Banking

Data in the banking sectors are huge and enhances each day. With a proper analysis of the same, it is possible to detect fraudulent activities such as misuse of debit or credit cards or money laundering. Big Data Analytics help with risk mitigation and bring business clarity.

As an example, Bank of America has been using SAS AML for over the past 25 years. The software is based upon data Analytics and is intended towards analysing customer data and identifying suspicious transactions.

Weather patterns

Weather satellites and sensors are located across the globe and collect a significant amount of data, which is then used to keep a tab on weather and environmental conditions as well. By use of Big Data Analytics, the data can be used for weather forecast and understanding the patterns of natural disasters in a better way. It can also come across as a resource for studying global warming.

The Governments can put in efforts in advance towards preparing themselves in the event of a crisis. It may even help determine the metrics related to the availability of drinking water across geographies.

Media and entertainment

People own and have access to digital gadgets that they use to stream, view, and download videos and entertainment based applications. This significant amount of data generated can be harnessed and some of the prime advantages that can be derived from putting this data to the best possible avail involve making a prediction of audience taste and preferences in advance. This can be further used towards making sure that scheduling of media streams is optimized or on-demand.

The data can also be used to study customer reviews and figuring out the factors that don’t delight them. Targeting advertisements over media become easier as well.

As an example, Spotify is a provider of on-demand music and uses Big Data Analytics to analyse data collected from the users across the globe. The data is then used to give some fine recommendations for a user to choose from. This is based upon the user’s browsing history and the most preferred videos seen by users of the same geographical region or the same demographics.

In terms of Big Data, it is important that the organizations are able to use the data collected to their best advantage in order to gain a competitive advantage. Merely a collection of the data is not enough.

In order to ensure efficient use of Big Data, Big Data solutions make the analysis easier. Application of Big Data expands further still to fields such as aerospace, agriculture, sports and athletics, retail and e-commerce.

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Insight into Big Data Trends and Future

Just at the start of the century, we came across a number of technologies such as wireless, web access and relational databases coming into more prominence than ever before. Analysis of huge databases came across as a challenge that was very real. The entire practice required a name.

It was in July 2013 that the name Big Data was adopted by the Oxford English Dictionary. But Big Data essentially is a term that has been around for a significant period of time.

Big Data essentially refers to data-sets that a very large and sometimes complicated to be processed by traditional forms of data processing.

With the advent of IoT and Mobile Technologies, Big Databecame more popular. This was aided by people using their digital devices and generating a significant amount of data, such as their geolocations, messages, images, videos, documents, etc., by using various applications to do so.

Big Data evolved to be known as a term for gathering, analyzing and using significant amounts of data in order to improve business operations. This data is growing at a rapid pace as more and more applications are becoming real time. In order to keep pace with the developments, Big Data and the processes are taking huge leaps in adapting to technology.

We live in a world where digital transactions are of great importance. A consumer is on a lookout for instant gratification. Things happen instantly, such as digital sales, providing feedback and making improvements as well. Correspondingly, a significant amount of data is produced as well. Putting this information to a requisite avail in real time offers access to the target audience. If a business fails to accomplish the same, the audience may move on to another brand. Here are some of the ways in which Big Data can transform an organization.

Business intelligence is the term used to define application and analysis of Big Data. It gives a competitive edge for business. By use of Big Data, difficult areas of operation and the most lucrative avenues and times for sales can be defined in advance. An organization can shape up its strategies accordingly.

By a deeper analysis of interactions and understanding the anomalies, certain patterns can be created. Big Data hence brings creative tools and products, which are new to the market.

Let us understand this by using an example.

If a certain appliance sells more than another one in warm weather, it may imply that heated conditions are adding to the sales. It can call for a study for markets most lucrative for sales of the gadget. Similarly, with a marketing campaign, brands can let consumers know about the availability of the gadget in places where it is likely to sell, and highlight it as the best selling product. This works towards boosting benefits to a significant extent.

5 Vs of Big Data

With reference to Big Data, the industry experts associate the 5 Vs. They should each be addressed distinctively in order to understand the effect that they lay on business cycles and profits, and how they interact with other Vs as well.

Volume

Upon dealing with Big Data, it is very important to presume the amount of data an organization is planning to use to gain insights. Similarly, the organization must be sure about where it is planning to store the data, and in what manner.

Variety

An organization must be comfortable in dealing with different types of data, and must be possessed with the right set of tools in order to ingest the information.

Velocity

If the Big Data technologies deliver outcomes fast, they make it easier for a business to put in continuous efforts towards improvement and streamline their work structures in real time. The results should be generated closer to in order to enhance their usability.

Veracity

The data input into the server should be accurate. The bigger picture should be considered in order to make sure that the outcomes are workable.

Value

In order to make sure that Big Data applications deliver actionable results, and a little bit of sorting over data collected too comes into the picture. This is because each of the bits of information collected is not of equal significance.

Role of Big Data Analytics

The essence of Big Datalies in use cases and insights, and not in voluminous data itself. Big Data analytics may be seen as a set of processes that are focused upon the examination of very huge sets of data, which are used to derive patterns which otherwise may not be visible. An analyst comes to discover of correlation, which helps with a prediction of market events before they occur and enables organizations to make corresponding strategies to deal with the events in the best possible way.

Market trends are highlighted by the use of Big Data, and the technology renders a higher degree of clarity for them. As a greater deal of information about customer preferences is derived, it makes way for market insights that can work towards enhancing a business.

An organization that puts applications of Big Data to avail is now positioned to come up with questions and queries. It puts even more insights at a business’s disposal. It is in the form of refined information that holds a potential for a business to get a competitive edge in their operations which makes way for higher profits.

Big Data applications hence come forth as the desired way to define Big Data and enhance the potential for its usability.

As per Industry Experts, Big Data will be placed better in the years to come. While it will be hardly visible and deliver tremendous business value, it won’t call for putting an end to manual labor or influence employment negatively at any level. The risk associated with security and compliance will be mitigated, while automation will allow staff to focus on tasks that deliver value. Big Data may give rise to new ways of working as well. Automation will facilitate even more effective management of Big Data.

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Applications of Big Data Analytics in real life

Today, millions of users click pictures, make videos, send texts, and communicate with each other through various platforms. This results in a huge amount of data that is being produced, used and re-used everyday.

In 2013, the total amount of data was 4.4 zettabytes. This is likely to increase towards 44 zettabytes by 2020 (One zettabyte is equivalent to 44 trillion gigabytes)

All of this ‘Data’ is a precious resource, which can be harnessed and understood by deploying certain techniques and tools. This is the gist of Big Data and Data Analytics. Using Big Data and Data Analytics, many organizations are able to gain insights into the customer mindsets, trending topics, imminent next Big things, etc.,

Let us take a look at how Big Data Applications has influenced various industries and sectors, and also the ways in which they are benefited from the same.

Education

The education industry is required to upkeep and maintain, a significant amount of data regarding faculties, courses, students and results. Requisite analysis of this data can yield insights that enhance the operational efficiency of the educational institutions. This can be put to avail in numerous ways.

Based upon a student’s learning history, customized schemes can be put into place for him/her. This would enhance the student results in entirety. Similarly, the course material too can be reframed based upon what students learn quicker, and the components of the course material that are easier to grasp. As a student’s progress, interests, strengths, and weaknesses are grasped in an improved manner, it helps suggest career paths most lucrative for him.

Healthcare

Healthcare industry generates a significant amount of data and Big Data helps the industry make a prediction for epidemic outbreaks in advance. It may also help postulate preventive measures for such a scenario.

Big Data may help with the prediction of disorders at an early stage, which can act as a preventive measure against any further deterioration, and makes the treatment more effective as well.

Government

Governments of all nations come across a significant amount of data every day, as enabled by sources such as the various databases pertaining to their citizens and geographical surveys.

By putting Big Data Analytics to the best avail, the Governments can come to recognize the areas that are in need of immediate attention. Similarly, challenges such as exploitation of energy resources and unemployment could be dealt with better. Centering down upon tax evaders and recognizing deceit becomes easier as well. Big Data also makes occurrences of food-based infections easier to determine, presume, and work upon.

Transportation

There are various ways in which Big Data makes transportation more efficient and easier, and the technology withholds a vast potential in the field.

As an example, Big Data can be used to access commuters’ requirements of different routes and can help implement route planning which reduces the waiting times. Similarly, traffic congestion and patterns can be predicted in advance, and accident-prone areas can be identified and worked upon in a suitable manner.

Uber is a brand that puts Big Data Analytics to avail. They generate the data about their vehicles, each trip it makes, the locations and drivers. This can be used for making predictions about the demand and availability of cabs over a certain area.

Banking

Data in the banking sectors are huge and enhances each day. With a proper analysis of the same, it is possible to detect fraudulent activities such as misuse of debit or credit cards or money laundering. Big Data Analytics help with risk mitigation and bring business clarity.

As an example, Bank of America has been using SAS AML for over the past 25 years. The software is based upon data Analytics and is intended towards analysing customer data and identifying suspicious transactions.

Weather patterns

Weather satellites and sensors are located across the globe and collect a significant amount of data, which is then used to keep a tab on weather and environmental conditions as well. By use of Big Data Analytics, the data can be used for weather forecast and understanding the patterns of natural disasters in a better way. It can also come across as a resource for studying global warming.

The Governments can put in efforts in advance towards preparing themselves in the event of a crisis. It may even help determine the metrics related to the availability of drinking water across geographies.

Media and entertainment

People own and have access to digital gadgets that they use to stream, view, and download videos and entertainment based applications. This significant amount of data generated can be harnessed and some of the prime advantages that can be derived from putting this data to the best possible avail involve making a prediction of audience taste and preferences in advance. This can be further used towards making sure that scheduling of media streams is optimized or on-demand.

The data can also be used to study customer reviews and figuring out the factors that don’t delight them. Targeting advertisements over media become easier as well.

As an example, Spotify is a provider of on-demand music and uses Big Data Analytics to analyse data collected from the users across the globe. The data is then used to give some fine recommendations for a user to choose from. This is based upon the user’s browsing history and the most preferred videos seen by users of the same geographical region or the same demographics.

In terms of Big Data, it is important that the organizations are able to use the data collected to their best advantage in order to gain a competitive advantage. Merely a collection of the data is not enough.

In order to ensure efficient use of Big Data, Big Data solutions make the analysis easier. Application of Big Data expands further still to fields such as aerospace, agriculture, sports and athletics, retail and e-commerce.

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Data Analytics and Big Data – An Overview

2019 has seen an advent of technologies and practices that were not of great importance or significance in the recent past. Talking on these lines, it is common practice for a large number of organizations to invest in Big Data Analytics in order to improve their operational efficiency. These analytics make way for improving revenue streams and provides the business a competitive edge over their rivals.

In order to pick the right Big Data Analytics applications best suited for its requirements, a business has many options at its disposal. A very commonly used choice among them is Descriptive Analytics which is primarily a way to visualize the historical data by means of querying. A more complex alternative to descriptive analytics is Predictive and Prescriptive modeling which is oriented towards the future. This essentially means to make decisions by anticipating business opportunities in advance. It is intended to make way for higher profits in targeted marketing campaigns and helps in customer retention. It can also help prevent eventualities such as equipment failure and such

Predictive analytics reveal patterns that indicate future situations based upon historical data sets while Prescriptive analytics work along with predictive analytics and stipulates the actions that will be most advantageous as per the scenarios predicted.

Using Big Data Analytics tools, analysis of massive amounts of data derived from varied source is made simpler and can be used for predictive and prescriptive analytics. These tools are essentially software products that render support for applications for predictive and prescriptive analytics, which run on Big Data computing platforms. They can be used for processing systems that run in parallel on servers, distributed storage which is scalable and technologies like NoSQL databases and Hadoop. They enable users to analyze large amounts of data, sometimes on a real-time window.

Alternately, Big Data analytical tools offer a framework for techniques of data mining. These techniques analyze data and bring certain patterns to fore. Correspondingly, a few analytical models can be created which may be used as a response to the identified patterns. As the models become a part of operations, business efficiency is enhanced.

As an example; A significant amount of shipping delivery data which pertains to traffic, weather, and vendor performance in the past can help make a model which enables selection of best-suited shipping contractors in a certain place. This helps minimize chances of delayed delivery or the goods coming across damage along the way.

Big Data Analytics tools can be used across different kinds of data types. This can include structured data which is stored within fields that are consistent and transactional data which is stored within relational databases. These tools can also use semi-structured data like mobile application web server log files or unstructured data like documents, text files, text messages, emails and posts on social media.

Talking about Big Data tools; some of the functionalities that must be included before a business makes the right choice are:

  • Analyst algorithms and models used should be current.
  • The tools must easily run over Hadoop, which is a Big Data platform.  Similarly, they must operate over high-performance analytics systems.
  • The tools must be adaptable and versatile. This would enable them to avail structured and unstructured data alike from many sources.
  • The tools should be scalable and must be in a position to analyze even more data as it is fed within the system.
  • Integration with data visualization and presentation tools must be simple for the analyst to accomplish.
  • The tools should enable easy integration with other technologies.

The tools must also have methods and algorithms necessary for supporting a characteristic suite of data mining techniques. This must include:

  • Methodologies for clustering and segmentation, which divides a huge selection of entities into smaller groups, based on characteristics or similarities which may not be initially anticipated. This can help create resources for targeted marketing.
  • The tools must also ease classification, or division of data into classes that are predefined. These attributes may either be pre-selected or defined based upon the results of the clustering model.
  • Regression analysis brings to fore the relationship between dependent variables, and a single or multiple independent variables. They help determine the ways in which the value of the dependent variable is dynamic, with respect to the independent variables. As an example, a prediction of the future value of a property can be made, using information such as its location, average temperature in summers, square footage and mean incomes.
  • Item set mining and the association may be used for the purpose of finding relevant relationships among variables as per statistics whenanalyzinga large data set. The relationships may be used by call center agents to offer schemes based upon caller’s segmentation, or upon the time period for which he has been associated with the organization, or on the nature of the complaint.
  • Similarity and correlation – The algorithms that score similar can be used to determine similarity among entities in a cluster.

Additionally, vendors also provide some algorithms which support each of these methods. Let us now take a short overview of Advanced Analytics Tools market.

Evolution of tools for advanced analytics markets has come a long way. Capability and ease of use of the tools are variable.

A few of the tools offered by well-placed vendors such as Oracle, IBM, and SAS have a history associated with them. Similarly, tools provided by Microsoft, Dell and Sap are relatively recent. Apart from them, a few of the top companies who offer products for Big Data Analytics are Alpine Data Labs, Predixion and Angoss.

Tarams has a strong Data Engineering and Analytics practice specializing in building enterprise data warehouses and data lakes starting from the ground up, both On-premise and Cloud.

Our engineers have extensive experience in building comprehensive analytics solution using various ETL & BI tools (like Pentaho, Tableau, Oracle), big data platforms and frameworks (like Kafka, Spark, HDFS), cloud services (like Snowflake, Redshift), web & mobile analytics (like Amplitude, Pyze) and business system integrations (like Salesforce, Zendesk).


Data Analytics Team
Tarams Software Technologies Pvt Ltd.

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Trapped in Cloud Migration Dilemma? 15 Factors to Consider to Take Wise Decision!

2017 was a roller coaster ride for enterprises in technology space. Recent IoT trends were the talk of the town and contributed to numerous transformations across industries. Consumer products such as connected devices and wearables were the bellwethers of these transformations.

The IoT industry size was valued at USD 800 billion in 2014. Technological proliferation and exponential increase in venture investments are expected to boost the global market over the next decade. Significant penetrations of internet and advancement in electronic industry have further propelled the growth of the Internet of Things (IoT) industry and gives a strong visibility to the compelling IoT technology trends in 2018.

The IoT market is expected to touch USD 6 trillion by 2021, at a Compound Annual Growth Rate (CAGR) of 26.9%. With this growth, market will witness new business models & use cases along with immense changes in improved customer experience, productivity & workflow.

Now that, we know IoT will be one of the primary crusaders to drive digital transformation in 2018 and beyond, let’s have a quick sneak peek on the top IoT trends in 2018.

In 2018, enterprises will be looking at the ‘cloud’ not just as a tool, but they will be exploring better ways to use it to accomplish their technology goals

Penetration of cloud technology into enterprise IT had brought remarkable transformations to the business realm in the past , and today cloud technology has opened new ways to maximize big data usage to optimize business revenue cycle management.

Cloud technologycontinues to skyrocket with advanced usage of cloud-based solutions for Analytics, Mobility with streamlined collaboration, IoT, etc., due to its cost-effectiveness and high-speed connectivity. As per IDC, 50% of all IT spending in 2018 will be cloud-based and Deloitte predicts that spending on IT-as-a-Service for data centres, software and services will reach $547 Billion by the end of 2018.

We have compiled a list of cloud trends that businesses need to be prepared for in the coming year.

The Rise of Saas, Paas, and IaaS market

Software as a Service (SaaS), where software applications are centrally hosted on cloud – meant to be licensed on a subscription basis, will rise to 18% CAGR by 2020 as per a survey.

Platform as a Service (PaaS) will be the most rapidly growing service that enables companies to develop, host and manage apps over a common platform that will grow to 52% adoption in 2020, as quoted by KPMG.

Infrastructure as a Service (IaaS) where virtualized computer resources are provided online, will grow with a market size of $17.5 Billion in 2018, as per a report by Statista.

2018 will be a golden year for cloud adoption where collaboration and social media democratization will become seamless and industries will witness exponential growth in adoption.

Cloud to Cloud Connectivity

The market is set to be flocked with multiple providers who are ready to share APIs to multiple cloud solutions and cross-functional applications. Enterprises should be looking forward to not limiting themselves to a single cloud service provider.

With the increase of consumer data inflow from disparate sources, consumers also expect faster data connection from network providers. 2018 will be the year when companies will show strong anticipation to move on to 5G networks.

Faster internet connectivity will compel users to demand fast-loading and high-responsive services and apps. Savvy enterprises will enforce highly responsive SaaS and PaaS in their application portfolio so as to ensure faster delivery that would eventually lead to higher traffic, new revenue generating models and value added service.

And all this becomes possible with embracing cloud-based platforms for products and services that allow businesses to gain agility through virtualization.

Pricing War leads to Vivid Cloud Usage

2018 will witness a growth in the volume of cloud service providers; while on the other hand, the market will display a drop in demand. However, it’s the law of nature- with the increase in supply, demand always goes down! Thus, with increased supply, the market will encounter a rigorous price war. Moreover, numerous providers will offer cloud space for free just to gain valuable consumer data.

Crowd Sourced Platform

Despite using insecure, costly and slow cloud space, users will start using crowdsourced platform to keep the cost low and avail optimum cloud benefits.

Sharing strangers’ and friends’ storage will be a common practice and people will start moving out of applications like DropBox and Google Drive. Similarly, businesses will also look ahead utilize crowdsourced platforms to maintain and build large-scale solutions.

Cloud will be the key to cost containment

Cost containment is a technique to cut down costs to essential expenses to limit within financial budgets. The growth of cloud adoption across the industry will be the main idea for long-term cost-cutting IT strategy by lowering the infrastructure expenses and improving ROI. It is predicted to be a norm in 2018.

Cloud Cost War will be at Pinnacle

Giants like Amazon and Google will be leading the war that actually will have substantial collateral damage to the mid-sized and small-scale service providers. AWS has already announced its lowered prices in the 2017 and Google has introduced its Committed Use Discounts (CUD) which gives the flexibility to the buyer to avail highly reasonable price for a committed use contract.

Cloud to On-Premise Connectivity

Businesses will move on to applications hosted on an on-premise based server while showing affinity towards shifting a chunk of their application portfolio on to the cloud to enable smooth customization and here are the reasons why:

  1. Although, on-premise deployment will ensure network security when it comes to data flow, numerous contemporary security solutions work best on the cloud.
  2. Over the years, the enterprise data has expanded at a multiplier level and transferring them on to the cloud remains a tough ordeal for most of the enterprises.
  3. Complete migration of the entire enterprise data takes a lot of time and does not display any short-term profitability.

Cloud Security Threats

Security concerns will become a major roadblock for numerous businesses to move their data onto a cloud. As per a report by Identity Theft Resource Center (ITRC), USA, 2017 saw 29% rise in security attacks as compared to 2016.

Today, user data is much more vulnerable than ever before, thus even Google brought in its 2-step verification process. As per IDC, global security revenue will grow up to $101.6 Billion in 2020. Another report reveals that security spending will touch $93 Billion in 2018. This will be the year when cybersecurity companies will be on their toes to engineer advance cloud security solutions.

It is predicted that, IT, security and cloud teams will associate to develop new working models to redefine cloud security services to reduce vulnerabilities. By bringing automation, speed, and integration with cloud security services, redefinition on how to approach cloud security for success will be implemented.

Cloud Security Threats

Cloud-based Containerization

Containerization in cloud computing will be implemented by most of the vendors. The phenomena allow the admins to create safe containers on the devices that enable smooth, safe and secure installation and deployment of the application.

Furthermore, cloud solution providers will offer independent container management system that would differentiate their platforms from another cloud container system.

It could reduce the vulnerability of data loss or threat and will be a popular trend in 2018 and beyond.

Cloud and the IoT

IoT as a technology completely depends on the cloud. IoT devices like electronic sensors, home appliances, cars, wearables, etc. communicate and store hefty information. With IoT devices becoming ubiquitous, cloud adoption will be on the rise.

Serverless will gain grounds

The adoption of serverless cloud architecture will enable CIOs to run applications without the burden of on-premise operating servers. Moreover, developers find it convenient to access and extend cloud services when it comes to addressing multiple use cases and application issues.

Serverless cloud architecture also needs less time and effort and simplifies software updates.

Edge computing: the next multibillion-dollar technology

Edge computing will leave no stones unturned, when it comes to operating close to IoT based devices and machinery such as automobiles, home appliances, turbines, industrial controllers, etc. and optimizing the cloud usage. Edge computing will be required to run the real-time services as it operates close to the sources and streamlines the inflow of traffic from these sources. Edge computing is an additional middle layer between the devices and the cloud that keeps the devices away from the centralized cloud computing.

Thus, the public cloud service providers will move towards IoT strategies that will include edge computing as an integral part.

Our Final Verdict

As far as technology advancement is concerned, possibilities are limitless. With evolving IT infrastructure, cloud adoption will be really fast. CIOs will be keen to consider the most advanced offering from the cloud space. However, security concerns will still haunt the CXOs and multiple enterprises will remain deprived of great opportunities.

The market will observe an affinity of enterprises towards hybrid cloud model. A handful of companies may also consider private cloud solution as an option.

At Tarams, we engineer solutions for intuitive visualization of cloud data keeping security and performance as the crux of our cloud-based solutions. Our cloud architects help you to efficiently mine data to develop better analytics, data mining best practices and improve decision making.

We forecast a strong proliferation of cloud technology in the upcoming years and recommend organizations to actively participate in its development, adoption and security.

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9 Emerging IoT Trends that will Disrupt Business in 2018 and Beyond

2017 was a roller coaster ride for enterprises in technology space. Recent IoT trends were the talk of the town and contributed to numerous transformations across industries. Consumer products such as connected devices and wearables were the bellwethers of these transformations.

The IoT industry size was valued at USD 800 billion in 2014. Technological proliferation and exponential increase in venture investments are expected to boost the global market over the next decade. Significant penetrations of internet and advancement in electronic industry have further propelled the growth of the Internet of Things (IoT) industry and gives a strong visibility to the compelling IoT technology trends in 2018.

The IoT market is expected to touch USD 6 trillion by 2021, at a Compound Annual Growth Rate (CAGR) of 26.9%. With this growth, market will witness new business models & use cases along with immense changes in improved customer experience, productivity & workflow.

Now that, we know IoT will be one of the primary crusaders to drive digital transformation in 2018 and beyond, let’s have a quick sneak peek on the top IoT trends in 2018.

Trend# 1

Fragmentation in IoT: Big Rock Challenge

While the venture capital is booting the IoT space, there has been a significant influx of network technologies & solutions into the market, developing a highly fragmented scenario within the IoT landscape.

First, well-known wireless networking technologies such as 5G, WiFi, Bluetooth, Zigbee are currently available to support IoT based solutions. Such vivid internet technologies connecting different groups creates interoperability complexity across the various networks.

Second, attaining IoT-enabled automation and predictive analysis to design industry-specific applications, needs a suite of processes to derive & analyze data from disparate devices. Furthermore, the connected equipment with different form factors and operating systems magnifies the complexity.

Thus, the major roadblock in such a fragmented ecosystem that interconnects different technologies will be the interoperability and integration of these processes to achieve the desired end result.

Trend# 2

Window of Vulnerability will be Wide Open

Cybersecurity will be a hot issue in 2018. Fragmentation will lead to extreme integration and interoperability complexities. This threat will not only remain limited to network security, but it will be a major challenge in managing and controlling connected assets.

Moreover, securing all the assets in an ecosystem without any standard industry regulation will be a big challenge.

Finding a solution that can secure all data sources and keep the data safe from all the vulnerabilities will be main goal of the year and one of the hottest IoT Industry Trends in 2018.

Trend# 3

Edge Networking will be less of a Trend and More of a Necessity

An exponential rise in data glut via IoT results in enterprises needing to find cost-effective ways to monetize consumer data.

So, Edge Computing will leave no stones unturned when it comes to operating close to IoT based equipment and sensors with different form factor and OS such as wearable, automobiles, home appliances, turbines, industrial controllers, etc. and optimizing the cloud usage. Edge computing will be required to run the real-time services as it operates close to the sources and streamlines the inflow of traffic from these sources.

Edge computing will minimize a big chunk of complexity when it comes to the cloud handling, managing data from disparate sources and delivering real-time services.It will be one of the most required and in-demand IoT industry trend that will that will take the entire industry by storm.

Trend# 4

Enterprise Mobility: de Facto IoT Companion

The current trend of mobile platforms getting ubiquitous in enterprises, will result in enterprise mobility playing a crucial role in IoT device management.

Today’s mobility landscape is all about collaborating workforce with BYOD model but with increased addition of IoT based sensors and wearable technologies into the workflow. It is quite evident that IoT and enterprise mobility- as a team, will play a serious game that will matter to the business goals.

As known, mobility and IoT in enterprises, are still in their infancy and their mix will continually evolve. The next mobility projects will get developed with cutting-edge technology in compliance with the business that is built around deriving value from IoT based devices.

The first movers have already begun to refine while the late-comers will directly jump into the blend. IoT industry trends in 2018 will witness a strong convergence between IoT and enterprise mobility.

Trend# 5

The Year of Bells, Dings & Whistles

For retailers, current IoT trends will play a crucial role in improving customer engagement and salesin 2018, 2019 and beyond. Current year will be the year when customers will hear more alerts about offers, incentives and other news regarding office, home & shopping.

In-store beacons will flourish and help retailers to identify a nearby mobile app user and approach with a personalized message. Location-based IoT beacons, sensors and analytics solution will drive the retail space by empowering marketers to send direct personalized messages to customer’s phone, wearable or adaptive in-store digital signage display.

Plus, IoT will tell where customers are spending time inside the store, giving marketers valuable data about customer behaviour and preferences.

Industry experts say that about 79% of retailers will start to use the IoT technology to transform their business in better way that is going to one of the productive IoT industry trend that is going to stay forever.

Trend# 6

Data Deluge Ahead: the Rise of Machine Learning & Advanced Analytics

Just as the abundance of data will push enterprises to the edge computing, it will also push them to machine learning. Machine learning and AI will be the leading technologies to manage the data flowing from IoT devices.

The year will also witness a steady growth in advance analytics solutions to provide a real-time streaming of data from IoT devices. Multiple analytics solution providers will be seen making noise in the arena.

Undoubtedly, the data deluge will give birth to numerous new entrants in machine & IoT analytics space to churn and manage valuable data insights.

Trend# 7

Blockchain and IoT to Dominate Headlines in 2018

The IoT devices, as they increase in abundance, lack standard authentication protocols to secure enterprise data. The probability of intruders penetrating through the vast array of IoT devices into the infrastructure is way higher than ever before. Hence, for widespread adoption of IoT, it is crucial that the industry should look at establishing standardized trust & authentication across all aspects of its ecosystem.

This is where the distributed architecture of blockchain proves to be the lifesaver to tackle trust and security challenge.

The distributed ledger in Blockchain empower the IoT devices to maintain standard identification, authentication, seamless secure data transfer and prevent duplication with any other malicious data.

The operation costs of IoT can be cut down through blockchain since there is no intermediary. The convergence of blockchain and IoT will improve customer experience, simplify workflow, cater to limitless opportunities.

Trend# 8

IT and OT will Walk Together

Operational technology (OT) and Information technology (IT) are conventionally separate organizational units. But, this trend is likely to change in 2018. The extreme inception of IoT into shop floor has compelled OT and IT teams to work closely to deploy IIoT solutions.

Today, analytical tools are majorly used by end users such as plant operators and field workers. Thus, operational decisions can be derived real time to optimize future performance.

IoT solutions will be deployed and driven by business operational teams, more than IT teams. Meanwhile, the OT teams will take the IIOT charge in 2018 and will be one of the greatest IoT trends.

Trend# 9

Investors to Break the Bank

The market is going to see a lot of money in the IoT space. The past has shown eye-popping investments and 2018 will show a relative trajectory. And as said before by 2021 the business spending in IoT will touch $6 Trillion.

Enterprises will continue to invest in IoT hardware, services, software, and connectivity and its going persist as an evergreenIoT trends. Almost every industry will be benefited from its rapid growth.

The biggest slice of funding until 2021 will go into hardware making especially sensors and modules but is also predicted to be outstripped by the fast-growing services category.

IoT’s indisputable impact will allure more venture capitalists towards highly innovative projects. They will continue to break the banks with the promise of IoT by underlining its caliber to improve customer experience and revenue in almost every industry.

Tarams Final Verdict

As a technology consulting and product engineering firm we strongly believe that these 9 trends will play a game-changing role in 2018. However, we also expect new trends to unfold that aren’t on the horizon yet.

We also believe that 2018 won’t be a silver bullet year for IoT but will be a year of preparation for building a widespread and robust foundation for the technology in next five years.

Readers, if you have thoughts on these or if you think that we have missed on any other trends that you believe will propel IoT, please comment below, we would enjoy hearing from you.

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How Mobile APPs Play a Game-changing Role in Education Industry

Smartphones are getting ubiquitous and there is a surge of Educational Applications for Android, iOS, and Windows Devices. These Apps focus on student academic learning needs while utilizing smart gizmos like tablets and smartphones for classroom learning and school activities. Students are finding this trend helpful and undoubtedly are drawn towards using a smart gadget for everything. On the other hand, the educational apps can be the perfect way to motivate students and help them focus on quality learning.

According to a Pearson Student Mobile Device Report, the use of tablets made students perform better in their academics and 79% of the polled students also agree that tablets make learning more fun. Access to information is crucial and thanks to these apps, which are integrated on their tablets and smartphones, it is easily available.

These mobile applications have brought tremendous changes in the education industry, as most of the EdTechs, institutions, and educators are adopting mobility as their primary Digital Transformation strategy. Let us have a quick tour to how Mobility Technology can help reshape the Education Industry.

Portability

Smartphones are our constant companions today. We are all connected through various apps that have reduced the distances of our physical world tremendously. Students too are hooked onto smartphones constantly, whether to chat, play games, or watch a video on the move.

Maneuvering this addiction towards a healthy habit, educational apps are on the rise. They do not limit the classroom to walls and they move with you wherever you choose to go. This freedom of portability is a major advantage in the learning process and many students are reaping the benefits of this mode of learning.

Round the Clock Availability

Educational Apps have another glaring advantage over traditional teaching establishments. Unlike institutes & schools, the apps are active round the clock. Even when limiting ourselves to a time-bound learning, apps help us with relaxed & performance-based learning.

Educational Apps have another glaring advantage over traditional teaching establishments. Unlike institutes & schools, the apps are active round the clock. Even when limiting ourselves to a time-bound learning, apps help us with relaxed & performance-based learning.

Time-bound learning is not impactful, as students get distracted easily and are unable to focus consistently for long hours. Thus, educational apps are the best for this issue, as they are available 24/7, and the students can learn as per their convenience.

Interactive Learning

The gadgets of today, with the Educational Apps and other features, are fast becoming a staple feature in every student’s life. Reading reference books and visiting the library are slowly diminishing in importance. These gadgets and helping students learn in an efficient manner. Unlike traditional teaching techniques and methodologies, Interaction with the apps is designed to suit students of all skill levels and cater to a variety of teaching methods, such as webinars, video tutorials, and even educational games.

This interaction helps students fight monotony and urge them to visualize what they learn.

Effectively Utilizing Leisure

Learning on educational apps, is one of the smartest choices for capitalizing leisure productively. The student’s leisure time can be used to learn something new with the help of entertaining tools, like games and puzzles.

One need not feel the burden of sitting through classrooms/classes to learn, when they can use the leisure time in an efficient and constructive way.

Get Individualized Learning

A teacher plays a remarkable role in building a student’s career, however the teacher can’t give individualized attention to every student. A teacher can typically address 10-20 students effectively in a session. And it is a tough ordeal to ensure that each and every student is engaged in the session.

In an educational app, the student gets all the focus they need. The time they engage with the app is all their own.

Track Performance

Tracking the performance or progress of a student, is essential for the students and the concerned educators. Having a legitimate plan and tracking methodology will only benefit the student by indicating the next steps in the learning process.

The inbuilt analytics show the details of the learning hours, the topics covered, the status of the current topic, etc., This detailed analytics at such a granular level is essential for proper guidance and the education apps are leading by example here.

Instant Personalized Updates

The Educational app is designed to send personalized messages, updates, etc., based on the student’s choices. It can also update on upcoming campus events, customized lessons, pending lessons, etc.

Online Study Material

Educational Apps on Mobile/Tablets offers students the opportunity to access thousands of reference and educational material online. This is possible owing to the digitalisation effort by numerous institutes and organizations.

This is a welcome change to many students who do not have access to good quality libraries or the economic freedom to own expensive books and study materials. Physical storage too is diminished and the geographic location of any student is immaterial

Mobility in Education through the advent of educational applications, has more benefits to offer than those that have been listed above. A quiet revolution is in the making, with students and educators alike, gradually drifting towards an industry that is foraying into a paperless and well networked sector. Today, the world of education is more than a passive activity; educational apps are making phenomenally active improvements and thrives to change the face of education sector.

Undoubtedly, educational apps solve critical problems in the education management systems across the globe but many clients, educational institutions and concerned students have a common issue despite owning cutting-edge educational apps.

Considering the capability of a connected smartphone or a tablet, handing students such powerful unmonitored, unmanaged and unprotected devices is a major concern for EdTechs and Institutions. Students were found to be indulged more in tampering educational app settings, playing games, using social media apps, online stores which are potential distractions from the intended learning.

Such scenarios result in problems due to misuse of these devices. Efficient use of educational apps, must ensure and avoid the download of malware and illegal or inappropriate content on their devices. Such misuse of technology is a concern and EdTechs and institutes alike must address the issue

This is where the actual potential of mobility can be harnessed.

An obvious solution to avoid distractions and ensure focused learning is by restricting students to only prescribed learning applications and content. This can be done with advanced mobility solutions. It helps the learners to remain focused by:

Blacklisting irrelevant websites and apps

Blacklisting includes prohibiting students from all unwanted applications and URLs such as games, social networking sites and more. This feature prevents misuse of study time for non-productive purposes. It restricts the students to tamper with the device settings and secures the preconfigured settings required to run the educational apps smoothly.

Network and Geo-Fencing

Through network fencing, schools can apply policies to the student’s devices when they enter the school’s Wi-Fi network. The policies could comprise of allowing whitelisted apps to open in the device. Geo-fencing features allow schools and institutions to monitor the student device and prevent unnecessary use of data and alert the admin when a device crosses the school boundary without authorization.

Remote Access to the device

This mobility feature allows the EdTech client or Institute to remotely control and access student’s device to update additional device information, file sharing, message broadcasting or troubleshooting for any error.

Mobility solutions incorporate robust features to make the student’s handheld safer for academic learning and help the schools and institutions to get the most out of their pedagogy.

If you are an EdTech, an institute, a parent or an educator, it’s not only important to have a robust educational app that caters to an innovative learning experience but to create a platform that meant for dedicated and prescribed learning, this is where you unveil the real success in the learning industry.

At Tarams, we leverage our big data with deep analytics capabilities to develop interactive educational mobility solutions. With proficiency in developing custom educational mobile app solutions, we deliver cutting-edge learning experience to the student that creates a brand identity in your target market.

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