Modernize your ETL processes with StreamAnalytix - Blog

Modernize your ETL processes with StreamAnalytix

By Saurabh Dutta | Mar 09, 2020

Businesses are struggling with huge volumes of data to solve complex business problems while relying on their legacy data platform infrastructure. However, traditional ETL tools that were designed two decades ago are not equipped to solve the business problems of 2020.

Challenges with traditional ETL Tools:

  • Costly
  • Non-flexible
  • Non-scalable
  • Built for on-premise
  • Cannot transform before landing

To address these challenges, enterprises are looking to transform their ETL workloads from legacy data warehouses to the cloud. The trend has gathered momentum in recent times. According to Forbes, 80% of the data warehouse tools used by organizations are now cloud-based versus on-premise.

Modern ETL tools have evolved as an obvious choice as they come packed with features to extract value from huge datasets. These tools offer the following advantages:

  • Performant
  • Resilient
  • Scalable
  • Intuitive
  • Secure and compliant
  • End-to-end data processing and analytics

How StreamAnalytix fits the bill?

StreamAnalytix is a self-service ETL and analytics tool. The platform lets you easily create batch and streaming ETL pipelines using drag-and-drop operators on a visual IDE. StreamAnalytix has a wide array of built-in operators for data sources, transformations, machine learning, and data sinks.

StreamAnalytix is the most advanced ETL tool to run your workloads on a distributed cloud environment with support for a wide variety of cloud-native operators.

Migrating traditional ETL jobs to StreamAnalytix

StreamAnalytix not only provides the most cutting-edge environment to run your migrated ETL jobs but also helps in migrating ETL jobs in three-steps:

Step 1 – Assessment

Assessment is an important step to enable the following:

  • Highlight differences between the source and target systems
  • Examples of source system logic recreated in the target system
  • Stakeholder sign-off

Step 2 – Conversion

Conversion involves moving the existing ETL logic to the target system. Traditional ETL workloads are transformed into Spark-based distributed workflows to be executed on StreamAnlaytix.

Step 3 – Validation

Ensuring a successful migration is crucial for business continuity. The tool’s validation capability ensures all the existing workloads are successfully migrated, and there are no gaps in logic that can result in loss of data when jobs are executed in the new environment.

Benefits of using StreamAnalytix for migration

  1. Drastically reduced migration efforts
  2. Increase in developer productivity
  3. Automated validation
  4. One-to-one mapping of existing workflows

To know more about how to migrate from traditional ETL workloads to distributed Spark-based workflows in StreamAnalytix, write to us at

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