Athena to Databricks Migration Accelerator
A data-driven enterprise modernized its analytics stack by migrating from Amazon Athena to a Databricks Lakehouse, delivering faster query performance, stronger data reliability, and unified governance in just 25 days.
Overview
A fast-growing, data-driven enterprise relied on Amazon Athena for serverless analytics on data stored in Amazon S3. While Athena enabled quick access to data early on, increasing data volumes and query complexity began to expose structural limitations. The linear pay-per-scan cost model drove rising and unpredictable analytics spend, while query latency varied significantly during peak usage hours.
At the same time, the lack of ACID transaction support introduced data reliability risks. Downstream consumers occasionally encountered inconsistent results and “dirty reads,” reducing trust in analytics outputs. Engineering teams spent increasing effort managing schema changes, optimizing queries, and maintaining workloads instead of focusing on innovation.
Without a scalable, governed analytics foundation, the organization faced growing cost pressure, operational inefficiencies, and limited confidence in data-driven decision-making.
Challenges
- Rising analytics costs driven by Athena’s linear pay-per-scan pricing model.
- Inconsistent query performance and limited concurrency during peak business hours.
- No ACID transaction support, leading to data inconsistencies and unreliable downstream reads.
- Schema changes requiring expensive, full-file rewrites and manual intervention.
- Fragmented metadata, access controls, and auditability across data assets.
- Excessive engineering effort spent on query tuning and platform maintenance instead of innovation.
Syren's Solution
Syren deployed its Athena to Databricks Migration Accelerator, combining automation, intelligent query refactoring, and Databricks-native best practices to minimize disruption.
Intelligent Query Migration
- Automated SQL migration using SQLGlot combined with LLM-assisted validation
- Refactored complex queries and custom UDFs to ensure Databricks compatibility
- Preserved business logic while improving execution efficiency
Delta Lake Modernization
- Converted S3 Parquet datasets into Delta tables
- Enabled ACID transactions, schema enforcement, and time travel
- Optimized table layouts and statistics for faster query execution
Unified Governance & Metadata
- Implemented Databricks Unity Catalog for centralized governance
- Standardized access controls, lineage, and auditability across workloads
Automation & Validation
- Automated DDL generation and validation scripts
- Ensured high query accuracy and data integrity with minimal manual intervention The end-to-end migration was completed in just 25 days, with minimal downtime and no disruption to business users.
Key Value Delivered
- Automated SQL migration accelerated go-live while minimizing manual effort.
- Delta Lake modernization delivered reliable, high-performance analytics with ACID guarantees.
- Automated validation ensured query accuracy and business continuity.
- Unity Catalog unified governance, access control, and compliance.
Impact
Conclusion
By partnering with Syren, the organization transitioned from cost-heavy, unreliable serverless analytics to a modern Databricks Lakehouse in just weeks.