How LeapLogic simplifies legacy data platform migration to Iceberg-based data lakes

Authors:

Samiksha Saraf, Director of Technology

Ashish Kumar Dahiya, Senior Technical Architect

Gurvinder Arora, Senior Lead Technical Writer

In this third blog of our “Future-proofing Data Architectures with Iceberg” series, we’ll explore how LeapLogic makes the migration process seamless. With automation and optimization, LeapLogic ensures a smooth, risk-free transition of legacy data platforms. Missed our earlier posts? Check out the first blog on Iceberg’s architecture and the second blog on key migration challenges.

LeapLogic, an advanced migration accelerator by Impetus, automates everything – from migrating legacy data warehouse and Hadoop workloads and data pipelines to transforming analytics for cloud-native platforms like AWS, Microsoft Azure, Google Cloud, etc. It’s designed to help you move away from legacy systems without the usual fears of downtime, data loss, or performance issues.

Migrating to Iceberg isn’t just about switching table formats – it’s a strategic upgrade for managing large-scale data lakes. LeapLogic helps you unlock Iceberg’s full potential. Here’s how:

End-to-end automation: From data transformation to code conversion

LeapLogic handles the entire migration process, automating data transformation, code conversion, and final optimization. It simplifies:

Data pipelines: ETL workflows are migrated to cloud-native services like AWS Glue in Iceberg-compatible formats.
Schema and metadata: LeapLogic ensures seamless schema and metadata transformation for Iceberg compatibility.
Code conversion: Legacy codes (SQL, Python, Scala) are converted into Iceberg-supported formats, ensuring smooth functionality post-migration.

Ensuring zero data loss with comprehensive validation

Data integrity is non-negotiable when migrating large workloads. LeapLogic conducts thorough validation, ensuring zero data loss or corruption. It supports detailed and summarized testing to guarantee the migrated workloads perform flawlessly.

Minimizing risk with incremental migration

Worried about the risks of full-scale migration? LeapLogic enables incremental migration, allowing you to move workloads in phases. This phased approach keeps your critical business operations running smoothly throughout the migration.

The LeapLogic approach: Streamlined and strategic

Below is the migration reference architecture LeapLogic follows, detailing source systems, processes, and supported target architectures.

LeapLogic’s migration process is structured into four key phases:

1. Thorough assessment and planning

LeapLogic begins with an in-depth analysis of your workloads, code profiling, and dependencies, delivering actionable recommendations. It prioritizes workloads, optimizes your environment, and provides architecture blueprints along with capacity planning.

2. Pattern-based code transformation

LeapLogic uses a pattern-based approach to transform legacy code, queries, and business logic into modern equivalents. Key considerations during this phase include:

Schema migration: Adapting source schema to Iceberg’s supported data types.
Process transformation:
- Converting Insert/Update/Delete operations to Iceberg equivalents.
- Managing concurrency and transactions with Iceberg Rollback.
- Merging multiple source operations into streamlined target system processes.

3. Validation and performance optimization

LeapLogic validates the functional correctness of your migrated workloads and fine-tunes them for optimal performance in the Iceberg-based environment, ensuring improved efficiency post-migration.

4. Operationalizing with cloud-native integration

LeapLogic enables seamless integration of legacy tools with modern cloud-native services, providing a DevOps-ready environment and accelerating legacy system decommissioning.

Case study: Teradata migration to Iceberg-based data lake on AWS stack

A Swiss telecommunications provider leveraged LeapLogic to migrate from an on-prem Teradata data platform to AWS with Iceberg and Glue. With billions of rows and complex ETL pipelines, this migration was no small feat.
Here’s what they achieved:

Utilized Iceberg table format in conjunction with Parquet file format.
Used copy-on-write (COW) and merge-on-read (MOR) strategies based on table usage
Implemented Data Mesh leveraging seamless connectivity between Iceberg and Amazon Redshift
Integrated Apache Iceberg with Spark’s parallel processing and AWS Glue
Enabled auto-compaction to optimize rewrite strategies
Optimized traditional 3NF relational database data models as per the ETL logic
Implemented schema evolution on the table columns operations like adding, renaming, re-ordering, deleting, and changing type, ensuring data overwrites were handled via metadata updates.
Partitioned tables by date/month/year to improve query performance.
Customized snapshots using tagging and branching, persisting certain crucial versions for a longer duration for a few mission-critical use cases
Optimized the queries to AWS-native platform equivalent, which included Partitioning, copy-on-write (COW) or merge-on-read (MOR) strategy, Sorting keys, and Snappy compression for tables that are frequently queried and Gzip for archival tables

What was the impact?

This migration created a highly optimized Iceberg-based data lake on AWS, positioning the telecom provider for future innovation and growth.

LeapLogic: Your trusted partner for seamless migration to Apache Iceberg

Migrating to Apache Iceberg unlocks powerful benefits – enhanced performance, scalability, and cost efficiency. But without the right partner, it can get complicated. LeapLogic automates and optimizes the entire migration process, ensuring a fast, safe, and efficient transition to Iceberg.
If you’re planning to migrate to an Iceberg-based data lake, LeapLogic is the key to a smooth, risk-free journey.