ETL Migration

3 Real-World Customer Case Studies on Migrating ETL to Cloud


As businesses continue to evolve in the digital age, the management and processing of data play a pivotal role in shaping strategic decisions and operational efficiency. Legacy Extract, Transform, Load (ETL) systems such as SQL Server Integration Services (SSIS), Informatica, and IBM DataStage have long been the backbone of data integration and transformation. However, the rapid advancement of cloud technologies has presented new opportunities for organizations to elevate their data processing capabilities.

In this overview, we delve into three compelling case studies that exemplify the successful migration of legacy ETL workflows to cloud-based solutions. These ETL migrations not only address the challenges posed by aging ETL systems but also unlock the potential of the cloud to enhance scalability, flexibility, and performance.

The chosen case studies delivered by Bitwise, a leader in data management and cloud modernization solutions, provide a comprehensive perspective on different migration scenarios. Now, let’s look at three real-world customer case studies on migrating ETL to cloud.

ETL Migration Case Studies

1. Accelerated SSIS ETL Migration to Azure Data Factory

In this case study, Bitwise demonstrates how they assisted a client in migrating their existing SSIS ETL workflows to Azure Data Factory (ADF). The challenge was to ensure a seamless transition while optimizing performance and ensuring data integrity. Bitwise leveraged their expertise in both SSIS and ADF to streamline the ETL migration process. By rearchitecting and redesigning ETL workflows to fit the cloud-native ADF environment, they achieved increased scalability, flexibility, and reduced maintenance efforts. The success of the migration resulted in improved ETL performance and the client’s ability to harness the power of the cloud for data processing.

2. Migrate Legacy Informatica ETL Code to AWS Glue

This case study highlights Bitwise’s proficiency in migrating legacy Informatica ETL code to AWS Glue, a fully managed ETL service on Amazon Web Services. The client aimed to modernize their data processing by adopting cloud-based technologies. Bitwise tackled the migration by analyzing the existing Informatica workflows and transforming them into AWS Glue jobs. This involved optimizing the ETL logic to align with Glue’s serverless architecture, which offers benefits such as automatic scaling and cost efficiency. The successful ETL migration enabled the client to continue their data processing seamlessly in the cloud while taking advantage of AWS Glue’s capabilities.

3. Automated ETL Migration from DataStage to Azure Data Factory

In this case study, Bitwise showcases their expertise in migrating IBM InfoSphere DataStage ETL workflows to Azure Data Factory. The client’s goal was to transition from an on-premises DataStage environment to the cloud for enhanced agility and scalability. Bitwise facilitated the migration by thoroughly understanding the existing DataStage workflows and transforming them to fit the cloud-based ADF architecture. By utilizing its proprietary automation tools, Bitwise ensured a smooth transition without compromising data quality or performance. The outcome was a successful ETL migration that allowed the client to harness the benefits of cloud-based data processing with a solution architecture that minimizes Azure costs.

Using Automation to Accelerate ETL Migrations to Cloud

Considering the complexity of ETL jobs developed over time in legacy systems and the incompatibility between those systems and cloud-native services, a completely manual approach is generally not feasible to deliver successful migration projects. That’s why automation has emerged as a key enabler in the process of migrating ETL workflows to cloud-based platforms.

Automation plays a pivotal role in reducing manual effort, mitigating risks, and ensuring consistency during complex migrations. For example, Bitwise’s ETL Converter tool provides a systematic approach to transforming existing ETL logic, enabling it to seamlessly align with the requirements of cloud-native platforms. By automating much of the conversion process, organizations can achieve faster and more accurate migrations, reducing downtime and minimizing disruptions to critical data processing workflows.
Moreover, validation utilities contribute significantly to the reliability of these ETL data migrations. They help in verifying the accuracy and integrity of migrated data, ensuring that the transformed workflows continue to produce reliable results in the new cloud environment. This not only boosts confidence in the migrated solution but also reduces the chances of data discrepancies or inaccuracies post-migration.
The successful application of migration tools such as the ETL Converter and validation utilities underscores Bitwise’s commitment to delivering efficient and reliable migration solutions. By embracing automation, organizations can expedite the migration journey, reduce manual intervention, and maximize the benefits of cloud-based data processing.


In conclusion, the evolution of businesses in the digital era has spotlighted the critical role of data management and processing in shaping effective decision-making and operational efficiency. Traditional ETL systems like SSIS, Informatica, and IBM DataStage have long been instrumental in data integration and transformation.However, the rapid strides in cloud technology have ushered in new horizons for organizations to enhance their data processing capabilities.

The three real-world customer case studies presented here exemplify the successful migration of legacy ETL workflows to cloud-based solutions. These migrations not only address the challenges posed by aging ETL systems but also tap into the immense potential of the cloud to augment scalability, flexibility, and performance. Check out our automated ETL migration page for a complete solution overview.

Explore More

An automated approach to convert any ETL to any ETL

Watch this on-demand webinar to check out an automated approach to converting any ETL to any ETL.

The blog was originally posted on September 2023 and recently updated on October 2023 for accuracy.

Nathan Nickels

Builds growth partnerships with Microsoft, AWS, Google Cloud, Informatica, Databricks and Snowflake to help Enterprises efficiently modernize Data and Analytics in the cloud to ensure data readiness for AI innovation.

You Might Also Like


Machine Learning

5 Use Cases for Driving ROI with Machine Learning
Learn More

Cloud Data Migration

How to use AI to modernize your PL/SQL code in Synapse or Snowflake
Learn More

Cloud Migration

What is Microsoft Fabric? Data Platform Overview and Best Practices
Learn More