3 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.

Conclusion

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.