Accelerated migration to Spark

With LeapLogic, your transformation to Spark (Hadoop) will happen faster, with more accuracy, thanks to superior analysis, automation, and validation

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    / Assessment
    • Say yes to key questions
    • Can I identify anti-patterns in my existing code and resolve as per Hadoop’s coding techniques and standards?
    • Will I know if I can meet my SLAs through Spark or if I need cloud-native warehouses?
    • Will I know which workloads can benefit from HDInsight vs. Synapse cloud data warehouses?
    • Can I save provisioning and maintenance costs for rarely used workloads on Azure?
    • Data warehouse
    • Can I get schema optimization recommendations for partitioning, bucketing, clustering, etc.?
    • ETL
    • Will my ETL processing SLAs impact my choice for an optimum Hadoop cluster size?
    • Analytics
    • Will it be beneficial to convert my analytical functions to Spark ML-based libraries?
    / transformation
    • Packaging and orchestration using Hadoop-native wrappers
    • Intelligent transformation engine, delivering up to 95% automation for:
    • Data warehouse –Hadoop (Spark SQL and HQL), Python/Scala/Java
    • ETL –Hadoop (Spark SQL and HQL), Python/Scala/Java, Amazon EMR/Azure HDInsight/Dataproc
    • Analytics – Hadoop (Spark SQL and HQL)
    / 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
    • Data warehouse – Provisioning on Hadoop and Hive, and of other services for orchestration, monitoring, security etc.
    • ETL – Provisioning on Hadoop/Hive/Amazon EMR/Azure HDInsight/Dataproc
    • Analytics – Provisioning on Hadoop



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