November 2022 - Page 2 of 3

Distributed Data Processing Using Databricks

Posted on November 21, 2022 by Indium

Distributed systems are used in organizations for collecting, accessing, and manipulating large volumes of data. Recently, distributed systems have become an integral component of various organizations as an exponential increase in data is witnessed across industries.

With the advent of big data technologies, many challenges in dealing with large datasets have been addressed. But in a typical data processing scenario, when a data set is too large to be processed by a single machine or when a single machine may not contain the data to respond to user queries, it requires the processing power of multiple machines. These scenarios are becoming increasingly complex as many applications, devices, and social platforms need data in an organization, and this is where distributed data processing methods are best implemented.

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Understanding Distributed Data Processing

Distributed data processing consists of a large volume of data that flows through variable sources into the system. There are various layers in that system that manage this data ingestion process.

At first, the data collection and preparation layer collects the data from different sources, which is further processed by the system. However, we know that any data gathered from external sources are mainly raw data such as text, images, audio, and forms. Therefore, the preparation layer is responsible for converting the data into a usable and standard format for analytical purposes.

Meanwhile, the data storage layer primarily handles data streaming in real-time for performing analytics with the help of in-memory distributed caches for storing and managing data. Similarly, if the data is required to be processed in the conventional approach, then batch processing is performed across distributed databases, effectively handling big data.

Next is the data processing layer, which can be considered the logical layer that processes the data. This layer allows various machine learning solutions and models for performing predictive, descriptive analytics to derive meaningful business insights. Finally, there is the data visualization layer consisting of dashboards that allows visualization of the data and reports after performing different analytics using graphs and charts for better interpretation of the results.

In the quest to find new approaches to distribute processing power, application programs, and data, distributed data engineering solutions is adopted to enable the distribution of applications and data among various interconnected sites to complement the increasing need for information in the organizations. However, an organization may opt for a centralized or a decentralized data processing system, depending on their requirements.

Benefits of Distributed Data Processing

The critical benefit of processing data within a distributed environment is the ease at which tasks can be completed with significantly lesser time as data is accessible from multiple machines that execute the tasks parallelly instead of a single machine running requests in a queue.

As the data is processed faster, it is a cost-effective approach for businesses, and running workloads in a distributed environment meets crucial aspects of scalability and availability in today’s fast-paced environment. In addition, since data is replicated across the clusters, there is less likelihood of data loss.

Challeges of Distributed Data Processing

The entire process of setting up and working with a distributed system is complex.

With large enterprises compromised data security, coordination problems, occasional performance bottlenecks due to non-performing terminals in the system and even high costs of maintenances are seen as major issues.

How is Databricks Platform Used for Distributed Data Processing?

The cloud data platforms Databricks Lakehouse helps to perform analytical queries, and there is a provision of Databricks SQL for working with business intelligence and analytical tasks atop the data lakes. Analysts can query data sets using standard SQL and have great features for integrating business intelligence tools like Tableau. At the same time, the Databricks platform allows working with different workloads encompassing machine learning, data storage, data processing, and streaming analytics in real time.

The immediate benefit of a Databricks architecture is enabling seamless connections to applications and effective cluster management. Additionally, using databricks provides a simplified setup and maintenance of the clusters, which makes it easy for developers to create the ETL pipelines. These ETL pipelines ensure data availability in real-time across the organization leading to better collaborative efforts among cross-functional teams.

With the Databricks Lakehouse platform, it is now easy to ingest and transform batch and streaming data leading to reliable production workflows. Moreover, Databricks ensure clusters scale and terminate automatically as per the usage. Since the data ingestion process is simplified, all analytical solutions, AI, and other streaming applications can be operated from a single place.

Likewise, automated ETL processing is provided to ensure raw data is immediately transformed to be readily available for analytics and AI applications. Not only the data transformation but automating ETL processing allows for efficient task orchestration, error handling, recovery, and performance optimization. Orchestration enables developers to work with diverse workloads, and the data bricks workflow can be accessed with a host of features using the dashboard, improving tracking and monitoring of performance and jobs in the pipeline. This approach continuously monitors performance, data quality, and reliability metrics from various perspectives.

In addition, Databricks offers a data processing engine compatible with Apache Spark APIs that speeds up the work by automatically scaling multiple nodes. Another critical aspect of this Databricks platform is enabling governance of all the data and AI-based applications with a single model for discovering, accessing, and securing data sharing across cloud platforms.

Similarly, there is support for Datbricks SQL within the Databricks Lakehouse, a serveless data warehouse capable of running any SQL and business intelligence applications at scale.

Databricks Services From Indium:

With deep expertise in Databricks Lakehouse, Advanced Analytics & Data Products, Indium Software provides wide range of services to help our clients’ business needs. Indium’s propreitory solution accelerator iBriX is a packaged combination of AI/ML use cases, custom scripts, reusable libraries, processes, policies, optimization techniques, performance management with various levels of automation including standard operational procedures and best practices.

To know more about iBriX and the services we offer, write to [email protected].

AWS Resilience Hub to Assess the Robustness of Your Software Application Built on AWS Platform

Posted on November 18, 2022 by Indium

Undisrupted, continuous service is a must in today’s world for customer satisfaction, even during calamities and disasters. Therefore, building and managing resilient applications is a business need, albeit building and maintaining distributed systems are just as challenging. And, being prepared for failures at a critical hour is just as essential. Not only should there be no downtime of the application, referring to the software or the code, but also the entire infrastructure stack consisting of networking, databases, and virtual machines, among others, needed to host the application.

Keeping track of the resilience of the system helps ensure its robustness even in case of disasters and other disruptions. There are two measures used to assess the resiliency of the apps. These include:

Recovery Time Objective (RTO): the time needed to recover from a failure
Recovery Point Objective (RPO): in case of an accident, the maximum window of time during which the data might be lost.

Based on the needs of the business and the nature of the application, the two metrics can be measured in terms the seconds, minutes, hours, or days.

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AWS Resilience Hub

With AWS Resilience Hub, the RTO and RPO objectives can be defined for each of the applications an organization runs. It facilitates assessing the applications’ configuration to ensure the requirements are met. Actionable recommendations and a resilience score help to finetune the application and track its resiliency progress over time. An AWS Management Console provides customizable single dashboard access that allows:

Running assessments,
Executing prebuilt tests
Configuring alarms to determine the issues
Alerting the operators

With AWS Resilience Hub, applications deployed by AWS CloudFormation, such as SAM and CDK, can be discovered, even across regions and in cross-account stacks. Applications can be discovered either from Resource Groups and tags or those already defined in the AWS Service Catalog AppRegistry

Check this out: Cloud Computing On Aws

Some of the benefits of AWS Resilience Hub include:

Assessment and Recommendations: AWS Resilience Hub uses AWS Well-Architected Framework best practices for resilience assessment. This helps analyze the application components and discover possible resilience weaknesses caused by:

– Incomplete infrastructure setup

– Misconfigurations

It also helps to identify additional configuration improvement opportunities. To improve the application’s resilience, Resilience Hub provides actionable recommendations.

Resilience Hub validates the Amazon Relational Database Service (RDS), Amazon Elastic File System (Amazon EFS) backup schedule, and Amazon Elastic Block Store (EBS) of the application to meet the RPO and RTO as defined in the resilience policy. If not, then it recommends appropriate improvements.

Resilience assessment facilitates recovery procedures by generating code snippets. As part of the standard operating procedures (SOPs), AWS Systems Manager creates documents for the applications. Moreover, a list of recommended Amazon CloudWatch monitors and alarms is created to enable quickly identifying any changing the application’s resilience posture on deployment.

Continuous Validation Resilience

Once the recommendations and SOPs from the resilience assessment are updated, the next step is to test and verify to ensure that the application meets the resilience targets before being released into production. AWS Fault Injection Simulator (FIS) is a fully managed service that allows Resilience Hub to run experiments on AWS to detect real-world failures, including network errors or several open connections to a database. Development teams can also integrate their resilience assessment and testing into their CI/CD pipelines using APIs available in the Resilience Hub for validating ongoing resilience. This prevents any compromise to resilience in the underlying infrastructure.

Visibility

The AWS Resilience Hub dashboard provides a holistic view of the application portfolio resilience status, enabling tracking of the resilience of applications. It also aggregates and organizes resilience events, alerts, and insights from services such as AWS Fault Injection Simulator (FIS) and Amazon CloudWatch. A resilience score generated by the Resilience Hub provides insights into the level of implementation for recommended resilience tests, recovery SOPs, and alarms. This can help measure improvements to resilience over time.

You might be interested in this: Using AWS for Your SaaS application–Here’s What You Need to Do for Data Security

Resilience Hub Best Practices On deploying an AWS partner application into production, Resilience Hub helps to track the resiliency posture of the application, notifies in case of an outage, and helps to launch the associated recovery process. For its effective implementation, the best practices include:

Step 1-Define: The first step is to identify and describe the existing AWS application that needs to be protected from disruptions and then define the resiliency goals. To form the structural basis of the application in Resilience Hub, resources need to be imported from:

– AWS CloudFormation stacks

– Terraform state files

– Resource groups

– AppRegistry

An existing application can be used to build an existing structure and then attach the resiliency policy. The policy should include information and objectives required to assess the application’s ability to recover from a disruption type, either software or hardware. The resiliency policy should include a definition of the RTO and RPO for the disruption types, which will help evaluate the application’s ability to meet the resiliency policy.

Step 2-Assessing: Run a resiliency assessment on describing the application and attaching the resiliency policy to it to evaluate the application configuration and generates a report. This report reveals how the application meets the resiliency policy goals.

Step 3-Recommendations: The Resilience Hub generates recommendations based on the assessment report that can be used to update the application and the resiliency policy. These could be regarding configurations for components, tests, alarms, and recovery SOPs. The improvement can be assessed by running another assessment and comparing the results with the earlier report. By reiterating this process, the RTO and RPO goals can be achieved.

Step 4-Validation: To measure the resiliency of the AWS resources and the time needed to recover from outages to application, infrastructure, Availability Zone, and AWS Region, run simulation tests such as failovers, network unavailable errors, stopped processes, problems with your Availability Zone, and Amazon RDS boot recovery. This can help assess the application’s ability to recover from the different outage types.

Step5-Tracking: Resilience Hub can continue to track the AWS application posture after deploying it into production. In case of an outage, it can be viewed in Resilience Hub and the associated recovery process launched.

Step 6-Recovery After Disruption: During application disruption, Resilience Hub can help detect the type of disruption and alert the operator, who can launch the SOP associated with the type for recovery.

Indium Software, an AWS partner, can help you ensure undisrupted application performance by implementing an effective AWS Resilience Hub for your applications based on your business objectives.

Tex.ai: Harvesting Unstructured Data in the Financial Services Industry to Garner Insights and Automate Workflows

Posted on November 18, 2022May 9, 2025 by Indium

The financial services companies differentiate themselves from competition by providing speed, ease and variety to their customers. Some of the key challenges the industry faces include complying with regulations, preventing data breaches, delighting consumers, surpassing competition, digitalizing operations, leveraging AI and data, and creating an effective digital marketing strategy.

While data analytics services play a key part in identifying areas of improvements and strengths, unstructured data provides a wealth of information, tapping into which the financial companies can accelerate growth and increase customer delight.

To know more about Tex.ai for financial services industry

For instance, a financial services company that provides Credit Score Ratings to its customers and helps many banks assess their customers’ credit scores wanted to improve its Know Your Customer process. The company had to process thousands of scanned bank statements to fulfill the KYC requirements for the applicants. The data had to be extracted from scanned images and digital PDFs.

Indium Software built a text extraction model employing its IP-based teX.ai product on 2000 bank statements. It created a scalable pipeline that could handle a large inflow of documents daily. As a result of the automation of the workflow, the processing of a single file took less than a minute, and the company experienced an 80% increase over the method the company employed previously. The accuracy also was nearly 90%.

In another instance, a leading holding conglomerate that capitalizes on fintech and provides financial services to the under-served in Southeast Asia required predictive analytics to be performed to evaluate the creditworthiness and loan eligibility of its customers. The data related to the loan information of the customer and their geographic details were stored in two separate PDFs for each customer, which needed to be merged. In case the customer had taken multiple loans, it had to be summarized at a row level

using business logic and Power BI used to create dashboards to get an overview of the kind of loans, repayment rates, customer churn rate, sales rep performance, and so on.

To predict whether a loan could be offered to a target customer, Indium leveraged tex.ai to extract customer-related loans and geographic details at the row level. This was used to custom-build business logic and summarize the customer-related information at the row level. As a result,

● The pull-through rate increased by 40%

● The loan cycle time decreased by 30%

● The customer acquisition rate went up by 25% within three months

● Application approval rate went up by 40%

● The cost of customer acquisition came down by 20%

Tex.Ai–For Insights from Unstructured Data

Financial services companies have access to many unstructured forms and information. This limits its use in data analytics and reduces efficiency unless it can be accessed in a format where analytics can be run on it to draw insights.

Indium Software’s Tex.ai is a trademark solution that enables customized text analytics by leveraging the ‘organization’s unstructured data such as emails, chats, social media posts, product reviews, images, videos, audio and so on to drive the business forward. It helps to extract data from text, summarize information, and classify content by selecting relevant text data and processing it quickly and efficiently to generate structured data, metadata, and insights.

These insights help to improve:

● Operational agility

● Speed of decision making

● Gaining customer insights

Secure Redaction and Automation

For the financial services industry, Tex.ai’s ability to identify text genres using the intelligent, customizable linguistic application and group similar content helps wade through millions of forms quickly and categorize them with ease. It helps to automate the extraction process, thereby increasing efficiency and accuracy. Tex.ai can also create concise summaries, enabling business teams to obtain the correct context from the right text and improve the quality of insights and decision-making.

Financial services is a sensitive industry regulated by privacy laws. Tex.ai’s redaction tool helps to extract relevant information while masking personal information to ensure security and privacy by masking all personal data.

Check this out: The Critical Need For Data Security And The Role Of Redaction

Tex.ai can also be used to extract insights from chatter and reviews, thereby helping financial institutions create customized products and services and focused promotions to improve conversions and enhance overall customer experience. It can help with fraud detection by analyzing past financial behavior and detecting anomalies, thereby establishing the credibility of the customers. This is especially important for processing loan and credit applications. An added advantage is the software’s ability to support several languages such as English, Japanese, Mandarin, all Latin languages, Thai, Arabic, and so on.

Further, teX.ai provides customizable dashboards and parameters that allow viewing and managing processed documents of customers. An interactive dashboard facilitates monitoring and improvement of processes by identifying and mitigating risks.

Using ok Indium’s Tex.ai solution can help financial services companies to deepen their customer insights, understand their requirements better, and provide bespoke solutions. This will help expand product base, customer base, and revenues while ensuring data privacy and security.

Indium’s team of data analytics experts can also help with customizing the solution to meet the unique needs of our customers.

Embedded Analytics: Is Your Product Designed to Deliver Data-driven Insights for Your Users?

Posted on November 18, 2022 by Indium

Gartner defines embedded analytics as a digital workplace capability that allows users with data analysis capabilities within their natural workflow instead of having to toggle to another application. Typically, the areas where embedded analytics is used include:

● Inventory demand planning

● Marketing campaign optimization

● Sales lead conversions

● Financial budgeting.

To know more about how Indium can help you embed analytics into your app

In the last few years, data generation and technological advancements have accelerated tremendously. For instance, bytes of data generated increased from 2.5 quintillion bytes every day in 2018 to nearly 1.7 MB every second by 2020. There has been rapid adoption of technologies such as IoT, cloud services, AI/ML, and data generation which has provided people with access and the ability to harness analytics across business applications. Users can view data in context and garner valuable insight to make informed decisions, which lead to better outcomes.

As a result, the embedded analytics market is also growing, from USD 36.08 billion in 2020 to USD 77.52 billion by the end of 2026, at a compounded annual growth rate of 13.6%.

Why Embed Analytics in Your App?

Data and analytics solutions are acknowledged as being critical components of digital transformation initiatives. Integrating them with the process workflows as embedded analytics

helps businesses experience significant benefits such as marketplace expansion, revenue growth, and competitive advantage.

With embedded analytics, customers are given access to the data they need in a timely manner, empowering them to analyze and make informed decisions. Users are also given the freedom to choose from a variety of dashboards, charts, graphs, and KPI widgets to visualize data in the most appropriate manner and draw their own conclusions. This helps them with improve customer experience by responding immediately and in the best possible way to their requirements. It helps identify strengths and weaknesses and increase operational efficiency. It also helps different teams to collaborate and work together on increasing efficiency and effectiveness of their improvement efforts.

By embedding analytics into their products, app developers can:

Enhance Application Value: Measure usage by number, depth, and session length to assess the value of your product. By embedding analytics into the app, users can access key metrics while using it, reducing exits and increasing session lengths. The insights also help with identifying strengths and areas for improvement. It can also be a key differentiating factor, enhancing the value of the application.

Facilitate Data-driven Decisions: Access to data visualizations within the app enables users to make informed decisions based on real-time data analytics. It helps uncover insights otherwise not easily available. It will also help to draw correlations and discover interrelationships between data.

Improve Pricing Strategy: Plugging in pre-built data visualizations when building new products can enhance the value of the app and increase its usefulness for the customers. This can help with pricing the product at a premium and improve profitability.

Benefits of Embedded Analytics

Data is the new oil that is helping businesses become more efficient and profitable. With embedded analytics, companies can increase their competitive advantage. Embedding analytics is proliferating across industries and functions. For instance, finance tools embed analytics tools to help customers analyze their income and outgoes. Utilities related tools help customers identify usage patterns and optimize consumption to lower energy bills. It can help discover new markets or build new features that customers seek. It can help serve customers better by anticipating their needs and providing timely service.

According to one Frost & Sullivan guidance, with embedded analytics, organizations can improve customer experiences, increase operational efficiencies, and reduce the time to market new products and services.

5 Kinds of Embedding

There are five levels at which analytics may be embedded. These are:

Web Embedding: This is the most basic form but highly effective, popular, and relevant. iFrames and HTML or JavaScript are used to embed the code needed to publish reports, dashboards, and data visualizations to websites.

Secure Custom Portals: The visualizations and reports are aggregated and published to a portal that could be meant for internal purposes or external, for customers and partners. Such portals are secure, with controls, and enable personalization, scheduling, and custom styling and branding.

SaaS/COTS Embedding: In this kind of embedding, two-way interactivity is possible with authentication and row-level controls for secure access to data. Typically, these are commercial off-the-shelf software (COTS), and so, it is essential to ensure that it does not need a separate analytical interface for running analytics.

Real-time Interactive: Also called context analytics, it can be accessed from specific areas or functions within enterprise software or a bespoke solution. This needs rich software development kits (SDKs) that can provide both interactive and predictive capabilities. Such a solution is cloud-friendly, is flexible and agile, and can be upgraded and customized..

Action-oriented Analytics: This is a very high level intelligent data application with low-code or no-code development capabilities that can learn and adapt. It can facilitate event triggers, automation, and workflows, triggering action and supporting scenarios even if analysis is not possible.

Which of these 5 levels of embedded analytics will go into an app will depend on the application needs and the development environment.

Indium for Embedded Analytics Solution

Indium Software is a digital engineering solution provider with capabilities in app engineering and data and analytics. The cross-domain expertise helps the team develop innovative solutions to meet the unique needs of its customers. The team works closely with the customers to understand their needs and offer solutions that can help them improve their competitive advantage and app value.

Why You Should Use a Smart Data Pipeline for Data Integration of High-Volume Data

Posted on November 18, 2022May 9, 2025 by Indium

Analytics and business intelligence services require a constant feed of reliable and quality data to provide the insights businesses need for strategic decision-making in real-time. Data is typically stored in various formats and locations and need to be unified, moving from one system to another, undergoing processes such as filtering, cleaning, aggregating, and enriching in what is called a data pipeline. This helps to move data from the place of origin to a destination using a sequence of actions, even analyzing data-in-motion. Moreover, data pipelines give access to relevant data based on the user’s needs without exposing sensitive production systems to potential threats and breaches or without authorization.

Smart Data Pipelines for Ever-Changing Business Needs

The world today is moving fast, and requirements changing constantly. Businesses need to respond in real-time to improve customer delight and become efficient to become more competitive and grow quickly. In 2020, the global pandemic further compelled businesses to invest in data and database technologies to be able to source and process not just structured data but unstructured as well to maximize opportunities. Getting a unified view of historical and current data became a challenge as they moved data to the cloud while retaining a part in on-premise systems. However, this is critical to understand opportunities and weaknesses and collaborate to optimize resource utilization at low costs.

To know more about how Indium can help you build smart data pipelines for data integration of high volumes of data

The concept of the data pipeline is not new. Traditionally, data collection, flow, and delivery happened through batch processing, where data batches were moved from origin to destination in one go or periodically based on pre-determined schedules. While this is a stable system, the data is not processed in real-time and therefore becomes dated by the time it reaches the business user.

Check this out: Multi-Cloud Data Pipelines with Striim for Real-Time Data Streaming

Stream processing enables real-time access with real-time data movement. Data is collected continuously from sources such as change streams from a database or events from sensors and messaging systems. This facilitates informed decision-making using real-time business intelligence. When intelligence is built in for abstracting details and automating the process, it becomes a smart data pipeline. This can be set up easily and operates continuously without needing any intervention.

Some of the benefits of smart data pipelines are that they are:

● Fast to build and deploy

● Fault-tolerant

● Adaptive

● Self-healing

Smart Data Pipelines Based on DataOps Principles

The smart data pipelines are built on data engineering platforms using DataOps solutions. They remove the “how” aspect of data and focus on the 3Ws of What, Who, and Where. As a result, smart data pipelines enable the smooth and unhindered flow of data without needing constant intervention or building or being restricted to a single platform.

The two greatest benefits of smart data pipelines include:

● Instant Access: Business users can access data quickly by connecting the on-premise and cloud environments using modern data architecture.

● Instant Insights: With smart data pipelines, users can access streaming data in real-time to gain actionable insights and improve decisions making.

As the smart data pipelines are built on data engineering platforms, it allows:

● Designing and deploying data pipelines within hours instead of weeks or months

● Improving change management by building resiliency to the maximum extent possible

● Adopting new platforms by pointing to them to reduce the time taken from months to minutes

Smart Data Pipeline Features

Some of the key features of smart data pipelines include:

Data Integration in Real-time: Real-time data movement and built-in connectors to move data to distinct data targets become possible due to real-time integration in smart data pipelines to improve decision-making.

Location-Agnostic: Smart Data Pipelines bridge the gap between legacy systems and modern applications, holding the modern data architecture together by acting as the glue.

Streaming Data to build Applications: Building applications become faster using smart data pipelines that provide access to streaming data with SQL to get started quickly. This helps utilize machine learning and automation to develop cutting-edge solutions.

Scalability: Smart data integration using striim or data pipelines help scale up to meet data demands, thereby lowering data costs.

Reliability: Smart data pipelines ensure zero downtime while delivering all critical workflows reliably.

Schema Evolution: The schema of all the applications evolves along with the business, ensuring keeping pace with changes to the source database. Users can specify their preferred way to handle DDL changes.

Pipeline Monitoring: Built-in dashboards and monitoring help data customers monitor the data flows in real-time, assuring data freshness every time.

Data Decentralization and Decoupling from Applications: Decentralization of data allows different groups to access the analytical data products as needed for their use cases while minimizing disruptions to impact their workflows.

Build Your Smart Data Pipeline with Indium

Indium Software is a name to reckon with in data engineering, DataOps, and Striim technologies. Our team of experts enables customers to create ‘instant experiences’ using real-time data integration. We provide end-to-end solutions for data engineering, from replication to building smart data pipelines aligned to the expected outcomes. This helps businesses maximize profits by leveraging data quickly and in real-time. Automation accelerates processing times, thus improving the competitiveness of the companies through timely responses.

The QA Benefits of Agile Methodology

Posted on November 17, 2022May 13, 2025 by Abilash K

The Agile methodology in software development is based on interactive and incremental
approach. In this methodology, the application will split into several phases on which
multiple cross functional team work together providing expeditious delivery. And it increases the customer satisfaction by rapid and continuous delivery of software.

The Top Five Benefits of Agile Testing Methodology

Following are the top 5 benefits of using agile Digital assurance solutions that every software teams should know:

Find out more on Indium’s Integrated QA Model for Modern AI based customer service application.

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1. A Time Saver

Agile is an iterative development methodology in which development and testing are performed at the same moment. In this process, crucial issues can be found and solved in the initial stages, saves a lot of time in the development and testing phase. The major benefit of this approach is its accurate unit testing, which is challenging to capture in the conventional waterfall methodology. Unit testing is executed more efficiently and successfully when testing is planned from the outset and included in the development process. Test cases are developed before the programming phase to expedite the process. And its helps in producing a well performing application in short span of time.

2. Better Collaboration and Communication

Agile testing enables team work and consistent contact between the development and testing teams. As a result, crucial issues can be Fend off or resolved quickly. In addition to the solid team, the testing team can be a part of the production procedure instead of entering just before release. They can be a great support to stay away from a lot of bugs and its save time by working together with the production team.

3. Consistent Sprints for Quicker, Better Results

Consistent sprints mean consistent advancement. Each iteration follows a specific operational code that allows the testers to work efficaciously. Different phases in the iterations include:

Planning
Developing test cases and screen mock ups
Coding and integration testing

These phases aid in identifying integration concerns, demonstrating the code to guarantee seamless business and technology management, and understanding the process’s positive and negative features. The purpose is to build user stories and discover defects in the code so that they can be fixed and the application’s performance optimized.

4. Satisfactory End-Results

For most of the application, the priority is always on providing the best user experience and making the application user friendly. This has always been the key to attracting new business by significantly raising the conversion rate. When both survey results and positive feedback from end users are steadily climbing, there is little question that agile development is the reason. Less time have been consumed on production and more on marketing, while focusing on the most crucial factors, yields superior outcomes and satisfies end customers.

5. Easier Application Maintenance

Involving the entire team, as opposed to a few individuals, reduces the likelihood of failure, making maintenance far more uncomplicated. Multiple developers and testers are involved in the agile methodology; hence, there are not too many but enough views to reduce the likelihood of coding or testing errors. Due to the limited time available with the agile methodology, testing is automated to save time and prevent duplication. Thus, agile testing becomes more precise, dependable, and efficient.

Check out this article: Uphorix – Test Automation Platform for End-to-End Agile SDLC

Wrapping Up

Deploying agile is similar to implementing any other transformation initiative; it does come with its hurdles. However, it also presents opportunities.

Agile testing should be on your radar if your company desires to accelerate its SDLC, provide quality, and outperform the competition. When properly utilized, agile testing may equip you with adaptability, the potential to be reactive, and the ability to provide quality quickly.

The Importance of Regression Testing

Posted on November 17, 2022 by Sarath Kumar H

Introduction

Regression testing is a type of black box testing method that ensures additions, deletions, modifications, or bug fixes do not affect features/functionalities that have already been implemented or are already in use. It enables the QA team to find bugs or mistakes in the build once a code change is made and to confirm that all features and functions are operating as intended after the application has undergone new code changes.

Indium helped a California based retail giant achieve 99% DRE (Defect Removal Efficiency) in every production release

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When to Use Regression Testing

Why Perform Regression Testing?

Regression testing is one of the most critical aspects of the product’s bug-free, long-term success. Thus, it should never be left to chance. Regression testing is now a necessary component of continuous development and delivery strategies. Although developing compelling regression test suites takes time and money, investing in them ensures that your product is free of errors and quality-related issues. When a strong plan that combines suitable regression testing and a better balance between manual and automated methods is used, the testing method will pay off with a reliable, functional software solution with a higher probability of producing positive results.

Challenges

Rerunning the test is a common component of regression testing; therefore, testers might not be very excited about the task.
Regression testing can be time-consuming to finish.
As products are updated, it might get quite complex, resulting in a vast list of tests in your regression suite.
Explaining the benefits of regression testing to non-technical business leaders is challenging.

Best Practices

Rather than attempting to test everything at once, concentrate more on the most common use cases for the software application.
The addition of automation tools will also cover more scenarios in less time, resulting in significant long-term cost and time savings.
If everything goes well, even we could do some exploratory testing.

Types of Regression Testing

Unit Regression Testing

Unit regression testing is carried out to test the code during the unit testing stage. In this, we will test only the changed unit, not the impacted area. It takes complicated dependencies and interactions beyond the concerned unit of code.

For example, when the developer changes the username and password text fields, if something goes wrong, the login page is blank, or the password is not accepted, the developer submits a solution, and the tester then tests these changes.

Regional Regression Testing

This testing method changes the code in a particular module so that it does not affect the other modules. We test the dependent module because the change may also impact different modules. For example, modifications made to module D by the developer are now affecting the other modules of A and C. All developers, test engineers, and product managers will gather for an impact analysis meeting led by a business analyst, test engineer, or product manager. They will determine which modules and features are impacted. Finally, we must test the modified component and the affected regions.

Full Regression Testing

Full regression testing is carried out when software updates or code modifications require deep root-level changes. It is also done when the current code undergoes several changes. It removes any sudden issues and provides a thorough overview of the system. Regression testing verifies that the build (new lines of code) has not been changed for a while. The end consumers then get access to this final version. For example, the developer modified modules B, C, D, E, and F in response to client demands and left module A alone. Testers must test an application’s updated and unaltered features to ensure the unchanging feature remains unaffected. We must perform a complete regression test if the developer has updated most of the application.

Real-Time Example of Regression Testing

In the product, there will be an existing code for every feature. When a new code or feature is added to the existing feature, we need to check if the behavior of the software is consistent and if the new code impacts the application. During regression testing, if any bug has been found, we will raise it and mark it as a regression bug. If the bug has been fixed, regression is performed to improve the quality of the application.

Some factors to keep in mind while doing regression testing:

Retest Everything

We must execute all the test cases in our regression suite. We must make the system bug-free, requiring significant effort and dedication to run the test suite.

Regression Test Selection

We use regression test selection to choose test cases from the test suite to determine if the updated code impacts the application or not. The test cases in the test suite were chosen based on code modifications.

Prioritization Of Test Cases

Many factors determine the priority of test cases in a test suite. It might be code coverage, important module functionality, or features. Prioritizing test cases based on risk and customer demands minimizes the number of test cases necessary for application testing. Early detection of bugs is simple to find.

Benefits of Regression Testing

It increases the likelihood of finding issues caused by modifications in the application.
It ensures that due to the code switches, it won’t reintroduce existing bugs.
The cost to fix bugs is reduced because it finds the issues/bugs early on.
Developers can concentrate on new features.
It improves user experience and helps keep the application up-to-date.

Conclusion

Regression testing can significantly improve the quality of the final product and the user experience. The proper regression testing technique can efficiently detect and solve flaws early in the process and saves organizations both time and money.

Test Managment with Agile

Posted on November 17, 2022 by Magesh S

Agile SDLC is considered one of the best SDLC methodologies, providing methods and procedures to execute a project in an agile manner. It includes the entire process of designing, building, testing and deployment.

Combining iterative and incremental process approaches, the Agile SDLC breaks down the development process into increments that are iteratively delivered. It prioritizes process flexibility and customer satisfaction by providing functioning software rapidly. It also enables testers, developers and stakeholders to work together to provide user stories that fully satisfy end-user requirements.

Here, we will see how the process of managing tests can impact Agile to make it more effective.

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What is Test Management?

Test management is a process that involves monitoring and managing software application testing.

Test management aims to prioritize major tasks and assign the appropriate expertise to accomplish them.

The process gathers the requirements of a project, creates a test plan, executes the tests and collects the execution result.

Test management is an activity that testers perform to test an application from end to end and ensure that high standards are maintained.

A few test management tools are listed below:

Test Rail
Zephyr Enterprise
Tuskr
Testo

Test management follows certain guidelines like planning, methodizing, controlling, estimating risk analysis, and confirming traceability.

The Need for Test Management

We need test management because it gives us a better image of keeping the whole process on track and coordinating the test activity.
It allows us to fine-tune the testing process by supporting, communicating and analyzing data.
It provides bug-free applications that meet the customer requirements of delivering the product within tight deadlines.

Effective Test Management Processes

Modern software teams adopt an agile workflow in test management to keep the report on track and improve software testing. The advantages of using agile test management are continuous flexible testing and testing in sprints, helping testers to work more productively. This process reduces design-related flaws and increases collaboration between teams.

To make Agile more effective, we can follow the user stories/tasks with some simple statuses, asdipicted below

Process Flowchart

Challenges in Agile Test Management

Communication
Communication plays a significant role in software development and testing. One of the biggest challenges is communication between team members and the off-shore team.

Frequent Changes in Requirement

In most cases, clients’ needs remain unclear and may change frequently. Screenshots and wireframe documents will not be provided for reference.

Focusing on Scrum Velocity

The Scrum master focuses extensively on scrum velocity. For example, if a team had worked on ten story points in the previous sprint, the Scrum master expects 15 story points to be completed in the upcoming sprint. When it comes to the test phase, there is a limited timeline for completing the sprint.

Delivering Good Products Within Timelines

When there is no proper plan, design and execution, it is hard to deliver the product within the timeline.

Why Test Management Plays a Critical Role

Prioritizes testing

The test management tool makes identifying and focusing on risky areas simple.

Reduces data duplication

When teams that work together fail to communicate, it results in data duplication. However, when it comes to testing management, we can review the tests that have been executed and the defects found, thus reducing data duplication.

Improves test coverage

When manual tests are performed with a wide range of data sets, it becomes challenging to understand what is done and what is not done. The best way to use the test management solution is to keep you on track.

Avoid Data Downtime and Improve Data Availability for AI/ML

Posted on November 11, 2022September 1, 2025 by Indium

AI/ML and analytics engines depend on the data stack, making high availability and reliability of data critical. As many operations depend on AI-based automation, data downtime can prove disastrous, bringing businesses to a grinding halt – albeit temporarily.

Data downtime encompasses a wide range of problems related to data, such as it being partial, erroneous, inaccurate, having a few null values, or being completely outdated. Tackling the issues with data quality can take up the time of data scientists, delaying innovation and value addition.

Data analysts typically end up spending a large part of their day collecting, preparing and correcting data. This data needs to be vetted and validated for quality issues such as inaccuracy. Therefore, it becomes important to identify, troubleshoot, and fix data quality to ensure integrity and reliability. Since machine learning algorithms need large volumes of accurate, updated, and reliable data to train and deliver solutions, data downtime can impact the success of these projects severely. This can cost companies money and time, leading to revenue loss, inability to face competition, and unable to be sustainable in the long run. It can also lead to compliance issues, costing the company in litigation and penalties.

Challenges to Data Availability

Some common factors that cause data downtime include:

Server Failure: If the server storing the data fails, then the data will become unavailable.

Data Quality: Even if data is available but is inconsistent, incomplete, or redundant, it is as good as not being available.

Outdated Data: Legacy data may be of no use for purposes of ML training.

Failure of Storage: Sometimes, the physical storage device may fail, making data unavailable.

Network Failure: When the network through which the data is accessed fails, data can become unavailable.

Speed of Data Transfers: If the data transfer is slow due to factors such as where data is stored and where it is used, then that can also cause data downtime.

Compatibility of Data: If the data is not compatible with the environment, then it will not be available for training or running the algorithm.

Data Breaches: Access to data may be blocked, or data may be stolen or compromised by malicious factors such as ransomware, causing data loss

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Best Practices for Preventing Data Downtime

Given the implications of data downtime on machine learning algorithms, business operations, and compliance, enterprises must ensure the quality of their data. Some of the best practices in avoiding data downtime include:

Create Data Clusters:As storage devices, networks, and systems can fail, creating clusters to spread data will improve availability in case of failures and prevent or minimize data loss. To enable responding to issues at the earliest, tracking and monitoring of availability is also important. The infrastructure should be designed for load balancing and resiliency in case of DDoS attacks.

Accelerate Recovery: Failures are inevitable, and therefore, being prepared for a quick recovery is essential. It could range from troubleshooting to hardware replacement or even restarting the operating systems and database services. It requires the right skills to match the technologies used to speed up the process.

Remove Corrupted Data: Incomplete, incorrect, outdated, or unavailable cause data corruption. Such data cannot be trusted and requires a systematic approach to identify and rectify the errors. The process should be automated and prevent new errors from being introduced.

Improve Data Formatting and Organization: Often, enterprises grapple with the issue of inaccurate data. It is difficult to access and use due to being formatted differently. Deploying tools that can integrate data onto a shared platform is important.

Plan for Redundancy and Backups: Back up data and store it in separate locations or distributed networks to ensure availability and faster restoration in case data is lost or corrupt. Setting up storage devices in a redundant array of independent disks (RAID) configuration is also one approach for this.

Use Tools to Prevent Data Loss: Data breaches and data center damages can be mitigated using data loss prevention tools.

Erasure Coding: In this data protection method, data is broken into fragments, expanded, and then encoded with redundant pieces of data. By storing them across different locations or storage devices, data can be reconstructed from the fragments stored in other locations even if one fails or data becomes corrupted.

Indium to Ensure Your Data Quality

Indium Software is a cutting-edge technology solutions provider with a specialization in data engineering, data analytics, and data management. Our team of experts can work with your data to ensure 24×7 availability using the most appropriate technologies and solutions.

We can design and build the right data architecture to ensure redundancy, backup, fast recovery, and high-quality data. We ensure resilience, integrity, and security, helping you focus on innovation and growth.

Our range of data services include:

Data Engineering Solutions to maximize data fluidity from source to destination BI & Data Modernization Solutions to facilitate data-backed decisions with insights and visualization

Data Analytics Solutions to support human decision-making in combination with powerful algorithms and techniques

AI/ML Solutions to draw far-reaching insights from your data.

FAQs

What is the difference between data quality and accuracy?

Data quality refers to data that includes the five elements of quality:

● Completeness

● Consistency

● Accuracy

● Time-stamped

● Meets standards

Data Accuracy is one of the elements of data quality and refers to the exactness of the information.

Why is data availability important for AI/ML?

Large volumes of high-quality, reliable data is needed to train artificial learning/machine learning algorithms. Data downtime will prevent access to the right kind of data to train algorithms and get the desired result.

Deploying Databricks on AWS: Key Best Practices to Follow

Posted on November 11, 2022 by Indium

Databricks is a unified, open platform for all organizational data and is built along the architecture of a data lake. It ensures speed, scalability, and reliability by combining the best of data warehouses and data lakes. At the core is the Databricks workspace that stores all objects, assets, and computational resources, including clusters and jobs.

Over the years, the need to simplify Databricks deployment on AWS had become a persistent demand due to the complexity involved. When deploying Databricks on AWS, customers had to constantly between consoles as given in a very detailed documentation. To deploy the workspace, customers had to:

Configure a virtual private cloud (VPC)
Set up security groups
Create a cross-account AWS Identity and Access Management (IAM) role
Add all AWS services used in the workspace

This could take more than an hour and needed a Databricks solutions architect familiar with AWS to guide the process.

To make matters simple and easy and enable self-service, the company offers Quick Start in collaboration with Amazon Web Services (AWS). This is an automated reference deployment tool integrating AWS best practices to leverage AWS Cloud Formation templates and deploy key technologies on AWS.

Incorporating AWS Best Practices

Best Practice #1 – Ready, Steady, Go

Make it easy even for non-technical customers to get Databricks up and running in minutes. Quick Starts allows customers to sign in to the AWS Management Console and deploy Databricks within minutes after selecting the CloudFormation template and Region by filling in the parameter values required for the purpose and deploy. Quick Starts is applicable to several environments and the architecture is designed such that customers using any environment can leverage it.

Best Practice #2 – Automating Installation

Deployment of Databricks involved installing and configuring several components manually earlier. This is a very slow process, prone to errors and reworks. The customers had to refer to a document to get it right and this was proving to be difficult. By automating the process, AWS cloud deployments can be speeded up effectively and efficiently.

Best Practice #3 – Security from the Word Go

One of the AWS best practices is the focus on security and availability. When deploying Databricks, this focus should be integrated right from the beginning. For effective security and availability, aligning it with the AWS user management to allow one-time IAM will provide access to the environment with appropriate controls. This should be supplemented with AWS Security Token Service (AWS STS) to authenticate user requests for temporary, limited-privilege credentials.

Best Practice #4 High Availability

As the environment spans two Availability Zones, it ensures a highly available architecture. Add a Databricks- or customer-managed virtual private cloud (VPC) to the customer’s AWS account and configure it with private subnets and a public subnet. This will provide customers with access to their own virtual network on AWS. In the private subnets, Databricks clusters of Amazon Elastic Compute Cloud (Amazon EC2) instances can be added along with additional security groups to ensure secure cluster connectivity. In the public subnet, outbound internet access can be provided with a network address translation (NAT) gateway. Use Amazon Simple Storage Service (Amazon S3) bucket for storing objects such as notebook revisions, cluster logs, and job results.

The benefits of using these best practices is that creating and configuring the AWS resources required to deploy and configure the Databricks workspace can be automated easily. It doesn’t need solutions architects to undergo extensive training to the configurations and can be an intuitive process. This will help them remain updated with the latest product enhancements, security upgrades, and user experience improvements without difficulty.

Since the launch of Quick Starts in September 2020, Databricks deployment on AWS has become much simpler, resulting in:

Deployment time takes only 5 minutes as against the earlier 1 hour
95% lower deployment errors

As it incorporates the best practices of AWS and is co-developed by AWS and Databricks, the solution answers the need of its customers to quickly and effectively deploy Databricks on AWS.

Indium – Combining Technology with Experience

Indium Software is an AWS and Databricks solution provider with a battalion of data experts who can help you with deploying Databricks on AWS to set you off on your cloud journey. We work with our customers closely to understand their business goals and smooth digital transformation by designing solutions that cater to their goals and objectives.

While Quick Starts is a handy tool that accelerates the deployment of Databricks on AWS, we help design the data lake architecture to optimize cost and resources and maximize benefits. Our expertise in DevSecOps ensures a secure and scalable solution that is highly available with permission-based access to enable self-service with compliance.

Some of the key benefits of working with Indium on Databricks deployments include:

More than 120 person-years of Spark expertise
Dedicated Lab and COE for Databricks
ibriX – Homegrown Databricks Accelerator for faster Time-to-market
Cost Optimization Framework – Greenfield and Brownfield engagements
E2E Data Expertise – Lakehouse, Data Products, Advanced Analytics, and ML Ops
Wide Industry Experience – Healthcare, Financial Services, Manlog, Retail and Realty

FAQs

How to create a Databrick in AWS?

In the free trial, you can sign up by clicking the Try Databricks button at the top of the page or on AWS Marketplace.

How can one store and access data on Databricks and AWS?

All data can be stored and managed on a simple, open lakehouse platform. Databricks on AWS allows the unification of all analytics and AI workloads by combining the best of data warehouses and data lakes.

How can Databricks connect to AWS?

AWS Glue allows Databricks to be integrated and Databricks table metadata to be shared from a centralized catalog across various Databricks workspaces, AWS services, AWS accounts, and applications for easy access.