Accelerate Enterprise Lakehouse Modernization — with Databricks

DataSwitch enables enterprises to transform legacy ETL, database, and analytics workloads into scalable Databricks Lakehouse architectures through deterministic AI-led engineering automation.

75–80%

Average Automation Rate

60–70%

Avg Cost Reduction Achieved

125,000+

Objects Migrated to Databricks

20+

Enterprise Implementations Delivered

Available on
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AWS
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Azure
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Google Cloud

Scalable Native Architecture

DataSwitch modernizes fragmented ETL, SQL, and warehouse-centric workloads into scalable Databricks-native architectures engineered for performance, governance, and enterprise-scale analytics.

Deterministic AI Automation

Leveraging deterministic Agentic AI automation, the platform accelerates migration, re-platforming, and re-engineering initiatives across modern Lakehouse ecosystems while reducing manual remediation effort and delivery timelines.

Beyond Lift-and-Shift

Beyond lift-and-shift migration, DataSwitch enables intelligent transformation into Databricks-native pipelines, Delta Lake architectures, Spark-based processing frameworks, and cloud-scale analytics engineering ecosystems.

Engineer Intelligent Lakehouse Transformation for Databricks

Go beyond legacy migration tools. With GenAI-led automation and graph-based engineering, DataSwitch delivers high-quality Databricks-ready code faster—reducing manual effort by up to 95%.

Data Platforms
Source DB Reader
teradata logo
netezza logo
oracle logo
mysql logo
sqlserver logo
db2 logo
greenplum logo
saphana logo
synapse logo
hive logo
sybase logo
Databricks
ETL tools
Source ETL Reader, Scheduler
informatica_pc logo
talend logo
ssis logo
datastage logo
sapbods logo
informatica_idmc logo
autosys logo
palantir logo
fabric logo
abinitio logo
sapbw logo
uc4 logo
odi logo
oracle_dq logo

Automate Your End-to-End Migration to Databricks

DataSwitch handles every phase of your Databricks migration — from legacy code discovery and assessment to automated conversion, validation, optimization, and production deployment — powered by DataSwitch's Agentic AI automation for high efficiency, speed, and accuracy.

Discover

Analyze ETL pipelines, SQL workloads, transformation dependencies, schemas, orchestration layers, and warehouse ecosystems to generate comprehensive modernization insights, automation feasibility, and migration readiness assessments.

Re-Engineer

DS Migrate intelligently transforms legacy ETL, warehouse, and analytics workloads into Databricks-native Spark SQL, PySpark, Delta Lake, and DLT-ready pipelines using deterministic AI-powered engineering frameworks.

Validate & Optimize

Automatically validate generated Databricks assets, optimize Spark execution logic, remediate transformation inconsistencies, and accelerate production readiness through built-in code validation, optimization, and self-healing capabilities.

Optimized for the Databricks Lakehouse Ecosystem

Databricks Lakehouse PlatformDelta LakeDatabricks SQLApache SparkAzure DatabricksDatabricks Delta Live Tables

Key Databricks Enablement Areas

Accelerating Databricks migrations with optimized execution structures and pipeline standardization.

Spark SQL & PySpark Engineering
Delta Lake & DLT Enablement
Lakehouse Architecture Modernization
Databricks-Native Pipeline Transformation
Metadata-Driven Re-Engineering
Automated Code Validation & Optimization

Frequently Asked Questions

DataSwitch enables enterprises to transform fragmented legacy ETL, standalone databases, and complex analytics workloads into scalable, Databricks-native Lakehouse architectures. Driven by deterministic, Agentic AI-led engineering automation, the platform executes comprehensive re-platforming and re-engineering initiatives while strictly ensuring target system performance, metadata governance, and cloud-scale analytics readiness.
DataSwitch delivers a stable 75% to 80% average code automation rate alongside a 60% to 70% average operational cost reduction. Across more than 20 delivered enterprise implementations, the platform has successfully migrated and refactored over 125,000 legacy data objects into the Databricks Lakehouse ecosystem.
Unlike standard tools that blindly lift code, DataSwitch leverages GenAI-led automation and graph-based engineering to deliver high-quality, Databricks-ready code faster, reducing manual remediation effort by up to 95%. It transforms legacy setups into intelligent Databricks-native pipelines, Delta Lake architectures, and highly optimized Spark-based processing frameworks.
The lifecycle is managed across three distinct automated phases: 1. Discover (analyzing ETL pipelines, SQL workloads, transformation dependencies, and orchestrations to assess automation feasibility); 2. Re-Engineer (using deterministic frameworks to intelligently compile legacy ETL into Databricks-native Spark SQL, PySpark, Delta Lake, and DLT-ready pipelines); and 3. Validate & Optimize (leveraging built-in code validation and self-healing to remediate inconsistencies and optimize Spark execution logic).
DataSwitch features automated source DB readers for platforms including Teradata, Netezza, Oracle, MySQL, SQL Server, IBM DB2, Greenplum, SAP HANA, Azure Synapse, Hive, and Sybase IQ. Concurrently, its source ETL readers ingest scheduler logic from Informatica PowerCenter (9.6+), Talend, Microsoft SSIS, IBM DataStage, SAP BODS, Informatica IDMC, Autosys, Palantir, Microsoft Fabric, Ab Initio, SAP BW, UC4, Oracle ODI, and Oracle Middleware.
DataSwitch accelerates enterprise migrations through six standardized technical enablement vectors: Spark SQL & PySpark Engineering, Delta Lake & DLT (Delta Live Tables) Enablement, Lakehouse Architecture Modernization, Databricks-Native Pipeline Transformation, Metadata-Driven Re-Engineering, and Automated Code Validation & Optimization.

Ready to Migrate to Databricks?

Tell us about your migration — we’ll map your source platform to Databricks, estimate automation rates, and give you a realistic delivery timeline. No obligation.