Accelerated migration to Databricks

Accelerate your ETL, analytics, Hadoop, and data warehouse migration and transformation to Databricks with LeapLogic

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    Explore the transformation potential in your business with Databricks

    / Assessment
    • Say yes to key questions
    • Will it make sense to design my future-state architecture using all cloud-native services (for orchestrating, monitoring, etc.)?
    • Will I know if I can meet my SLAs through Databricks Lakehouse or if I need cloud-native warehouses?
    • Data warehouse
    • Can I get schema optimization recommendations for partitioning, bloom filters, ZOrder indexing,, etc.?
    • ETL
    • Will my ETL processing SLAs impact my choice for an optimum Databricks cluster size?
    • Can I save provisioning and maintenance costs for rarely used workloads on Databricks?
    • Hadoop
    • Is my optimization strategy for Update/Merge on Databricks apt?
    • Analytics
    • Can I transform my analytics layer as well along with my data warehouse, ETL systems, and BI?
    • BI/Reporting
    • Can I use the processed data from my modern cloud-native data warehouse stack for my BI/reporting needs and leverage it with a modern BI stack?
    / transformation
    • Packaging and orchestration using Databricks-native wrappers
    • Intelligent transformation engine, delivering up to 95% automation for:
    • Data warehouse and ETL to Databricks migration – Databricks Lakehouse, Databricks Notebook, Databricks Jobs, Databricks Workflows, Delta Lake, Delta Live Tables
    • Analytics to Databricks migration – Databricks Lakehouse on AWS/Azure/GCP, PySpark
    • Hadoop to Databricks migration – Databricks Lakehouse on AWS/Azure/GCP, Presto query engine
    / validation
    • All transformed data warehouse, ETL, analytics, and/or Hadoop workloads
    • Business logic (with a high degree of automation)
    • Cell-by-cell validation
    • Integration testing on enterprise datasets
    / operationalization
    • Capacity planning for optimal cost-performance ratio
    • Performance optimization
    • Robust cutover planning
    • Infrastructure as code
    • CI/CD
    • Provisioning of Databricks Lakehouse and other required services


    Automated workload transformation from Informatica to Databricks

    Automated Teradata data warehouse migration to Databricks

    Automated workload transformation from SAS to Databricks

    Automated workload transformation from Hadoop to Databricks


    Explore resources to support your transformation initiatives


    30% performance improvement by converting Netezza and Informatica to Azure-Databricks stack


    20% SLA improvement by modernizing Teradata workloads on Azure


    Automated legacy ETL, Hadoop, analytics, and data warehouse platform migration to Databricks


    Learn more about these benefits and best practices for cloud-native transformation