Hadoop to Databricks Migration – Why Enterprises Are Moving to Modern Data Management Systems

In the realm of big data, there’s an ongoing shift that’s changing the way organizations handle their data processing and analytics needs. Hadoop, once considered a cost-effective solution for data storage, is gradually giving way to more efficient, scalable, and user-friendly alternatives. One such alternative that’s gaining traction in the industry is Databricks. In this blog, we will explore the reasons behind the growing trend of migration from Hadoop to Databricks and the benefits this transition offers.

The Evolution of Hadoop:

Hadoop was once hailed as an inexpensive storage solution, providing organizations with the ability to store and process vast amounts of data. It followed the traditional approach, consisting of multiple open-source projects and offering deployment options both on-premises and in the cloud. However, as the workload and data complexity increased, it became evident that Hadoop had its limitations. These limitations prompted the need for further evolution in the big data landscape, leading to the development of more versatile and scalable solutions to meet the growing demands of modern data processing.

Hadoop’s Challenges

Hadoop, once celebrated for its cost-effective data storage and processing capabilities, encountered challenges as it proved to be resource-intensive and demanded a high level of skill to manage effectively. It struggled to support the fundamental requirements of modern analytics. Furthermore, Hadoop required significant financial investment due to its fixed infrastructure, ongoing maintenance costs, and expenses associated with upgrades. These obstacles prompted the exploration of more agile and efficient alternatives in the dynamic landscape of big data.

The Importance of Performance:

In today’s data-driven landscape, the importance of performance cannot be overstated. The pace at which data is generated, processed, and analyzed has accelerated exponentially, and organizations are continually in search of ways to optimize their operations. A key aspect of this optimization is the need for shorter execution times.

Reducing execution times has several significant advantages. First and foremost, it helps organizations cut down on cloud costs. With many businesses relying on cloud services for their data infrastructure, any reduction in the time it takes to process data can directly translate to cost savings. Additionally, shorter execution times lead to enhanced efficiency, enabling faster decision-making and quicker response to real-time data insights.

The increased focus on performance has driven many organizations to explore alternative solutions that can deliver the speed and efficiency required in today’s competitive landscape. This quest for superior performance has catalyzed innovation in fields such as data processing, storage, and analytics, ultimately benefiting businesses seeking to stay ahead in the ever-evolving world of data-driven decision-making.

Databricks: The New Paradigm:

Enter Databricks, a game-changer in the world of data management. The Databricks Lakehouse Platform was purpose-built for the cloud, offering support for AWS, Azure, and GCP. It’s a managed collaborative environment that unifies data processing, analytics, data science, and machine learning, all while seamlessly integrating real-time streaming data. Databricks eliminates the need for multiple disjointed tools and ensures data resides securely in your cloud storage within Delta Lake. This approach maintains control over data and code while being easily accessible with open-source tooling.

Hadoop to Databricks Migration

Migrating from Hadoop to Databricks is a strategic decision that requires careful planning to ensure a seamless transition without disrupting current operations. Central to this process are considerations of data governance and security, which must be given top priority.

The decision to migrate is often driven by Databricks’ ability to deliver substantial performance improvements, enabling organizations to handle data processing and analytics at a larger scale, effectively meeting the demands of modern data requirements.

The migration itself is a complex task that demands a well-thought-out strategy. To maintain business continuity, it’s advisable to run workloads on both Hadoop and Databricks during the transition phase. Gradually decommissioning Hadoop after the transfer is complete ensures a smooth transition.

Looking ahead, the future of data management lies in the ability to harness data for informed decision-making. Databricks plays a crucial role in this by reducing the overhead associated with infrastructure maintenance and data management. This allows organizations to focus their efforts on building essential use cases, adapting to the fast-paced world of data-driven decision-making.

In conclusion, the migration from Hadoop to Databricks is a trend that reflects the evolving needs of organizations in managing and utilizing their data. Databricks offers a modern, efficient, and cost-effective solution that is essential for staying competitive in the data-driven era. Organizations that make the switch will not only improve their data processing capabilities but also empower their data teams to focus on building innovative use cases rather than managing complex infrastructure. As data continues to play a pivotal role in decision-making, Databricks represents a crucial step towards a more data-savvy and agile future.

Benefits of Big Data Consolidation in today’s business landscape

In today’s data-driven business landscape, organizations are constantly faced with the challenge of managing and extracting value from vast amounts of data. Big data consolidation offers a solution by integrating diverse data sources into a unified system, enabling businesses to uncover actionable insights and make informed decisions. In this blog, we will explore the benefits of big data consolidation in business and how it can drive success across various domains. 

One of the primary advantages of big data consolidation is the comprehensive view it provides of an organization’s operations. By consolidating data from various sources such as sales, marketing, customer feedback, and the supply chain, businesses can gain valuable insights into different aspects of their operations. This holistic perspective allows decision-makers to identify trends, patterns, and correlations that would remain hidden within individual data silos. Armed with this knowledge, businesses can make data-driven decisions that lead to improved efficiency, optimized processes, and ultimately, better outcomes. 

In the current customer-centric era, understanding customer behavior, preferences, and needs is crucial for business success. Big data consolidation plays a key role in achieving a 360-degree view of customers by integrating data from multiple touchpoints such as online interactions, purchase history, social media, and customer support. This consolidated customer profile provides a deeper understanding of individual preferences, allowing businesses to personalize their offerings, deliver targeted marketing campaigns, and enhance customer experiences. By leveraging big data consolidation, organizations can foster long-term customer loyalty and drive revenue growth. 

Managing data across multiple systems and platforms can be time-consuming and error-prone. Big data consolidation simplifies this process by centralizing data storage and management. By eliminating data silos and streamlining data access, organizations can reduce redundancy, enhance data quality, and improve operational efficiency. Employees across departments can access a single source of truth, minimizing the time spent searching for information and enabling faster decision-making. This streamlined approach empowers organizations to optimize their operations, allocate resources more effectively, and respond swiftly to market demands. 

Proactive risk management is essential for long-term success in today’s dynamic business environment. Big data consolidation enables organizations to identify potential risks and vulnerabilities by consolidating data from various sources. By uncovering patterns and anomalies that may indicate emerging risks such as fraudulent activities, cybersecurity threats, or supply chain disruptions, businesses can take timely actions to mitigate risks, protect their assets, and safeguard their reputation. 

Big data consolidation also serves as a foundation for data-driven innovation within organizations. By bringing together diverse data sets, organizations can uncover new insights, identify market trends, and discover untapped opportunities. These insights can fuel product innovation, inform business strategies, and drive competitive advantage. Leveraging advanced analytics techniques such as machine learning and predictive modeling, businesses can uncover hidden patterns and make accurate predictions. This empowers organizations to stay ahead of the curve, adapt to changing market dynamics, and innovate in a rapidly evolving business landscape. 

In conclusion, big data consolidation offers significant benefits to businesses, ranging from enhanced decision-making and improved customer understanding to streamlined operations, proactive risk management, and data-driven innovation. By harnessing the power of consolidated data, organizations can gain a competitive edge, drive growth, and navigate the complexities of the modern business landscape with confidence. 

Harnessing the Power of Big Data Analytics Dashboards

In the era of Big Data, businesses face the challenge of managing and deriving meaningful insights from large volumes of data originating from various sources. Big data analytics dashboards have emerged as indispensable tools that allow companies to consolidate, analyze, and visualize vast amounts of data within a single platform. In this blog post, we will explore the benefits of using big data analytics dashboards and how they enable businesses to harness the potential of diverse data sources for informed decision-making.

1. Consolidating Data from Multiple Sources:

Big data analytics dashboards provide a unified platform that integrates data from various sources, such as internal databases, external APIs, cloud storage, and IoT devices. This consolidation eliminates the need for manual data gathering and ensures that decision-makers have access to a comprehensive and up-to-date view of their data.

2. Streamlining Data Analysis:

Analyzing large datasets can be a daunting task without the right tools. Big data analytic dashboards simplify the process by providing intuitive visualizations and interactive charts that enable users to explore data effortlessly. By condensing complex information into digestible visuals, dashboards facilitate quick and accurate data analysis, saving time and effort.

3. Gaining Actionable Insights:

The primary goal of big data analytics dashboards is to extract actionable insights from data. These dashboards allow users to define key performance indicators (KPIs) and track them in real-time. By visualizing trends, patterns, and correlations, businesses can identify opportunities, detect anomalies, and make data-driven decisions that drive growth and efficiency.

4. Real-time Monitoring and Alerts:

One of the key advantages of big data analytics dashboards is their ability to provide real-time monitoring of crucial metrics. Users can set up alerts and notifications based on predefined thresholds, ensuring that any significant deviations or anomalies are immediately brought to their attention. Real-time monitoring empowers companies to take proactive measures and respond swiftly to changing business conditions.

5. Improved Data Accessibility and Collaboration:

Big data analytic dashboards foster a culture of data accessibility and collaboration within organizations. Instead of relying on data specialists or IT departments for data extraction and analysis, employees from various departments can access the dashboard to retrieve relevant information and gain insights independently. This democratization of data promotes cross-functional collaboration and empowers teams to make data-driven decisions collectively.

6. Enhanced Data Security and Compliance:

Big data analytics dashboards foster a culture of data accessibility and collaboration within organizations. Instead of relying on data specialists or IT departments for data extraction and analysis, employees from various departments can access the dashboard to retrieve relevant information and gain insights independently. This democratization of data promotes cross-functional collaboration and empowers teams to make data-driven decisions collectively.

Big data analytics dashboards have revolutionized the way companies manage and analyze data from diverse sources. By consolidating data, simplifying analysis, and providing actionable insights, these dashboards empower businesses to make informed decisions, drive efficiency, and stay ahead of the competition. Embracing big data analytics dashboards as a central platform for data visualization and analysis can unlock the full potential of your organization’s data assets and pave the way for data-driven success.

Ready to harness the power of big data analytics dashboards for your company? Contact Us to explore how our advanced dashboard solutions can help you consolidate, analyze, and visualize your data, enabling you to make informed decisions and propel your business to new heights of success.

How Automation Can Increase Efficiency

During a customer visit, I was asked about a solution, which can automatically remove the background of an image without any human intervention.

The customer is a technology company, developing solutions for the automotive and financial services verticals. They have millions of images of cars that their clients upload to their websites with different backgrounds. They had to manually correct each of these images which is labour intensive and time consuming.

They asked me about the possibility of an application that can automatically detect a car from an image and mask the background with any colour of their choice and add their watermark on it.

Analyzing Images Using Open CV & Edge Detection

Initially, we decided to explore some already available solutions and created a tool using Open CV, Python, and Edge Detection. But these tools were not providing accurate results. The tool worked in basic car models, but most of the time the tool was not accurate in identifying the car from the image or the angle of the car by which the image was taken. Also, the jagged edges of the primary object (car) in the images were a major concern.

Exploring Machine Learning

At Feathersoft we had already worked on AI Solutions and have created different models especially in the agtech industry where we had used image recognition for disease detection and crop measurements.

This led me to think about whether the same technology can be used to solve the jagged edges issue. While exploring multiple computer vision AI projects in Yolo and Deeplab, I got to the conclusion that we could resolve the background removal problem using deep learning, semantic segmentation, and encoder-decoder layers.

Initially, we planned to use pre-existing models available in the market to tackle the issue. But unfortunately, we could not find a single model that has the potential to give an accurate output. This left us with two options. Either we train a model from scratch or improve an existing model using transfer learning. Transfer Learning is a method of reusing existing weights and retrain the model. This will ultimately help in reducing time and effort for training the model.

The Data Set

Data is the foundation for any machine learning project. Fortunately, we found a data set of car images and its mask in Kaggle. But we faced a big challenge while working on the data set. The images from Kaggle have a similar background which was not suitable for our purpose.
The workflow of our requirement was like, a user would take a car picture and upload it to a car listing website, which should then remove the background of the car with transparent background.
To train our model, none of the backgrounds should be the same. To deal with this data set issue, we generated images manually using custom-made python scripts with different backgrounds and using different car models. We created around 1000 images and their masks to train the model.

The Training

The training nearly took four days to get completed. The results came after 2000 epochs over our training data. We tested the model and were very satisfied with the prediction accuracy.


We decided to host the solution (model) in Amazon SageMaker and for converting the millions of existing images, all images will be placed in an Amazon S3 bucket, from which the job could be scheduled, and run. This job triggers a lambda function and will convert all the images from the S3 bucket and deliver it to the output bucket.


The original scope was to find a background remover, to remove the background of the car images. However, the Model we created has the potential to be used in a variety of verticals including advertising, e-commerce, and real estate. The model can be customized to remove the background of any primary object. Automation of image background removal using machine learning, helped in displacing the labour-intensive process and improved efficiency.

Does your business have similar challenges? I am open to new challenges and experiences, and I would love to help you in finding the best solutions for your business problems.

The author, Sudhish Chandran is the Chief Technology Officer (CTO) of Feathersoft

Why Businesses Adopt Cloud As The Key To Fuel Digital Transformation?

The introduction of technologies like Machine Learning, Big Data, Artificial Intelligence, and IoT has affected traditional businesses redundant. Rapid changes in customer needs force firms to evolve and adapt continuously. Digital transformation is the new paradigm, that helps companies to stay ahead of the competition. Firms leverage the backing of the cloud to set up a new, robust digital transformation framework.

Cloud fuels innovation by giving an appropriate set of APIs for developers. Firms can reuse enterprise data and moreover, the cloud offers analytics, functional programming, and low code platforms. This guarantees the faster launch of enterprise-ready products.

Cloud as the key enabler of transformation

Businesses that enforce cloud computing and digital transformation report enormous growth and improved efficiency. It enables firms to stay relevant and thrive in a dynamic ecosystem. Digital transformation involves finding newer business models. Firms must continuously innovate to stay in the game, and the cloud is the catalyst that fuels innovation. As businesses begin to adapt their processes to a digital space, they require cost and work efficiency, agility, and scalability. The cloud provides all these features.
Here is how the cloud enables digital transformation:

1.Enhanced Business Agility

A firm needs to constantly reinvent its business models. Cloud provides the required infrastructure that allows, continuous optimization across the organization with flexibility which is not possible when using on-site physical solution platforms and computing abilities that helps firms stay agile ready, and willing for the transition.

2.Cost and Labor Effectiveness

Companies don’t have to go to the trouble of investing in and managing the required infrastructure. Cloud computing allows firms to scale up or down, so firms only have to pay for the resources that they use. When it comes to the time needed to deploy a storage solution, the cloud is, undeniably, more suitable as compared to on-site solutions. SaaS deployment is virtually non-existent compared to on-premise platforms, where the IT team must be spending more time to procure and install the infrastructure.

Automated updates made available through the cloud allow businesses to continually optimize their performance without relying on in-house servers, which are prone to outgrowing their capacity. It also alleviates the pressure on IT departments, who no longer have to worry about purchasing additional resources to maintain performance and install the latest security updates.


Moving a database to the cloud offers the advantage of increased protection from threats such as data breaches, disasters, and system shutdown. You can create multiple backups. There is a reduced risk of system failure, especially where large amounts of data are involved.

4.Faster Prototyping

Companies follow a cycle of innovating, testing, implementing, and repeating. Cloud helps in the efficient execution of this process without the need for complicated resources and infrastructure. Therefore, a company can test and deploy different applications in different types of platforms during experimentation.

Digital transformation involves replacing traditional work practices with processes that support agility. Data can be made available at any place and at any time. Authority and authentication can be determined for each user, this ensures potent delegation. There, an atmosphere of collaboration and teamwork in the company leads to it’s better productivity.

Cloud is Essential for Advancing Digital Transformation

As the world evolves and tech moves at headlong speed, digital transformation becomes a means of survival. Cloud becomes the catalyst for transformation and the hybrid cloud will have a key role to play. In an era where success is measured by customer experience, a cloud-enabled and cloud-delivered business model helps the organization discover newer channels to offer a superior customer experience.

Key Cloud Migration Challenges & How To Overcome It

It is a chaotic time for business today that a deluge of new technologies has subsequently emerged out and that continues to define a new business era. The organizations are flocking to the cloud to cut costs, stay nimble, and ensure future survival.

Each journey to the cloud is unique and the challenges along the way are prevalent. Businesses must look ahead to the journey and prepare accordingly for safe and controlled cloud migration. The cloud enables firms to effectively reduce IT costs, mitigate security risks, and agile innovation with continuity. Yet, despite the vast array of benefits, making the move is not a light task. It requires exquisite planning and execution, which explains why migration is often cited second to security on the cloud computing pain sequence.

Choosing the right Cloud Service Provider

The wide range of cloud services can make it difficult to choose the right cloud service provider. Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are among the giants, at the same time, many more niche players saturate the market with bespoke services. This often leaves IT leaders, to be stuck at the primary hurdle of digital transformation.

Although the budget plays a key part in the decision-making process, organizations need to consider their initial goals for moving to the cloud. Each business will have its own set of requirements and a unique justification for adopting the cloud. Security, architecture, manageability, level of services, support, and cost are all critical factors to consider before choosing a cloud provider. However, it is essential that the chosen vendor also understands the objectives of the business, as well as the motive behind their decision to migrate to the cloud. Without the right blend of technical expertise and service excellence, the partnership will not power up the business in the cloud. Therefore, the assessment of business goals and vendors’ services is a critical part of the cloud provider decision-making process.

Evaluating the current IT portfolio

Any drastic change within an IT environment demands a thorough analysis of current infrastructures before deciding on which elements to migrate. This is often the most challenging part of cloud migration. It takes time to unveil issues, document findings and turn them into actionable insights, especially for firms that spread their infrastructure across multiple data centers or teams. Yet, many firms rely on manual processes to assess their current environment. This often paves the way for making further delays and potential bottlenecks. By the time, the discovery process is complete, the organization’s infrastructure might be changed.

A successful cloud migration starts with understanding on-premise application workload profiles. A key part of this discovery process is figuring out analyzing the knock-on effect systems can potentially have on one another. Workload intelligence, combined with dependency mapping, therefore figures crucial insight into the decision-making, and significantly reduces the possibility of missing dependencies during the move.

What to modernize and what to leave behind?

Migration is not a simple process of lift and shift. The prioritization of what to move and when to move is fundamental to successful cloud migration. The applications for early migration are those with fewer dependencies. This ensures to take cautious measures towards unforeseen problems and can be dealt with ease. Starting small is, therefore, a key to successful cloud migration.

Businesses should also consider how moving the least critical applications will impact the overall IT ecosystem. Some applications are more suited in the cloud as compared to others, particularly those applications with variable usage patterns. Therefore, it is more important to align business goals with the benefits that the cloud could bring, like faster and frequent application releases and flexible auto-scaling.

Minimizing business downtime

Downtime is frequently cited as the biggest challenge of moving workloads to the cloud. The biggest hurdle between a successful and unsuccessful cloud migration is network connectivity. Even the minutest downtime can affect the business, both in terms of revenue and reputation. The priority, therefore, should be to plan to minimize disruption and ensure business continuity during the migration.

A live migration refers to the process of migrating a running virtual machine or any application between different physical machines without disconnecting the client or application. This guarantees long-term success, as it brings together to go live tools, delivery, integration testing, source control, and unit testing all whilst the source is still in production. This allows the business to test the migration offline before any cutover is made and make sure risk mitigation when moving from a physical to a virtual environment.

Automation Capabilities

Automation is fundamental for a successful cloud journey, not just during the initial migration, but throughout the optimization of the cloud environment. Productivity and efficiency are the primary motives behind automating cloud migration, which cultivates a level of consistency unmatched by manual processes. There’s also an added benefit of not having to add staff to the project to manually assess, fix, and convert the application for the virtual cloud platform.

To fully leverage the cloud, firms should automate application compatibility testing and fixing, since the cloud allows multiple virtual platforms to be tested and quick to fix it simultaneously. This lowers migration time requirements and reduces the cost of the individual workload.

Keeping data safe and compliant during the migration

The consequences of insufficient data security are immense. If there is any data loss, leakage, or if data is exposed to a cyber-attack during the migration, it could cause serious disruption. For highly regulated firms who are bound by law to protect personal customer data, concerns around IT governance and compliance can make the migration seem deeply unattractive.

An efficient and secure point-to-point migration ensures the company’s data is transferred via a preferred routable path and data is protected behind the firewall. This ensures that the company’s personal data is not visible to any third party during the migration processes, which eliminates the potential for non-compliance during the move.


Cloud migration is a complex process. It needs meticulous planning and impeccable expertise to architect, secure, and manage migration. Your success will depend upon the partner you choose and the extent of their knowledge, skillset, and experience to identify and manage the cloud migration process and execute it with the overall digital strategy of the business.

As you look ahead to the journey of movement, be sure to prepare for safe and controlled cloud migration. Talk to us about our deep experience and mix of capabilities and find out how you can move your workloads to the cloud without jeopardizing applications or data and, perhaps more importantly, day-to-day business operations.


Advantages of Big Data & Analytics in Business

When it is about establishing your business, you must lookout for something that can take your business to the next level. Well, data analytics services and big data management are the present and future of the business world and it has been influencing the business world for quite a long time. Companies must take advantage of the data to deliver the experiences customers are looking for. Businesses can benefit from data to drive the best outcomes for the business and its clients while maintaining and facilitating the highest level of data protection. Big Data solutions and Data Analytics can not only foster data-driven decision making but also empower your workforce by helping them to devote to tasks that add value to your business and that require cognitive skills.

Improve efficiency

Big Data tools can improve operational efficiency by leaps and bounds. Big Data tools can amass large amounts of useful customer data by interacting with customers or clients and collecting their valuable feedback. This data can then be analyzed and interpreted to extract meaningful hidden insights within the customer, like the taste, buying behavior, and preferences, which allows companies to create personalized products/services. Big Data Analytics can identify and analyze the latest trends in the marketplace, allowing you to keep the pace up with your competitors. These services can automate routine processes and tasks with which it ultimately frees up the valuable time of human employees.

Boost sales and retain customer loyalty

Big Data aims to gather and analyze vast volumes of customer data. The digital footprints that customers leave behind will reveal a great deal about their buying behavior, preferences, needs, and much more. This customer data offers the scope to design tailor-made products and services to cater to the specific needs of individual customer segments. The higher the personalization of a business, the more it will attract customers. Naturally, this will make considerable changes in sales. Personalization and the quality of product/service also have a positive impact on customer loyalty. If you offer products with topmost quality, at competitive prices along with personalized features or discounts, customers will keep coming back to you time and again.


Big Data Analytics and tools can dig into vast datasets to extract valuable insights, which can be transformed into actionable business strategies and insights. These insights are the key to innovation. The insights you gain can be used to tweak business strategies, develop new products/services (that can address specific problems of customers), improve marketing techniques, optimize customer service, improve employee productivity, and find radical ways to expand brand outreach.

Focus on the local environment

This is particularly relevant for small businesses that cater to the local market and its customers. Even if your business functions within a constrained setting, it is essential to understand your competitors. Big Data tools can scan and analyze the local market and offer insights that allow you to see the local trends associated with sellers and customers. Consequently, you can leverage such insights to gain a competitive edge in the local market by delivering highly personalized products/services within your niche, local environment.

Foster competitive pricing

Big Data Analytics facilitates real-time monitoring of the market and your competitors. You can not only keep track of the past actions of your competitors but also see what strategies they are adopting now. Big Data Analytics offers real-time insights that allow you to –

  • Calculate and measure the impact of price changes.
  • Implement competitive positioning for maximizing company profits.
  • Evaluate finances to get a clearer idea of the financial position of your business.
  • Implement pricing strategies based on local customer demands, customer purchasing behavior, and competitive market patterns.
  • Automate the pricing process of your business to maintain price consistency and eliminate manual errors.

Control and monitor online reputation

As an increasing number of businesses are shifting towards the online domain, it has become increasingly crucial for companies to check, monitor, and improve their online reputation. After that, what your clients are saying about you on various online and social media platforms can affect how your potential customers will view your brand. There are numerous Big Data tools explicitly designed for sentiment analysis. These tools help you surf the vast online sphere to find out and understand what people are saying about your products/services and your brand. When you can understand customer grievances, only then can you work to improve your services, which will ultimately improve your online reputation. To conclude, Big Data has emerged as a highly powerful tool for businesses, irrespective of their size, and the industry they are a part of. The biggest advantage of Big Data is the fact that it opens new possibilities for organizations. Improved operational efficiency, improved customer satisfaction, drive for innovation, and maximizing profits are only a few among the many, many benefits of Big Data. Despite the proven benefits of Big Data, we’ve witnessed so far, it still holds numerous untapped possibilities that are waiting to be explored.