Your Migration Solution

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Select any source and target below

Explore the transformation potential

Legacy data warehouse, ETL, or analytics system
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Modern target platform

Discover the power of smarter, faster transformation from Teradata

LeapLogic assesses and transforms diverse Teradata scripts and ETL, so you can feel the freedom of the cloud quickly, with lower risk of disruption

/ Source scripts
  • Stored procedures
  • DML and DDL
  • Shell scripts and macros
  • BTEQ, TPT, MultiLoad, FastLoad, FastExport, etc.
/ ETL scripts
  • Informatica, DataStage, Ab Initio, ODI
/ Analytics scripts
  • SAS, etc.
/ Logs
  • Assessment of EDW execution and application
/ Orchestration
  • Control-M, AutoSys, EPS, Cron Shell, etc.

Discover the power of smarter, faster transformation from Netezza

LeapLogic assesses and transforms diverse Netezza scripts and ETL, so you can feel the freedom of the cloud quickly, with lower risk of disruption

/ Source scripts
  • Stored procedures
  • DML and DDL
  • Shell
  • NZ SQL, Export/Load, etc.
/ ETL scripts
  • Informatica, DataStage, Ab Initio, ODI
/ Analytics scripts
  • SAS, etc.
/ Logs
  • Assessment of EDW execution
/ Orchestration
  • Control-M, AutoSys, EPS, Cron Shell, etc.

Discover the power of smarter, faster transformation from Oracle

LeapLogic assesses and transforms diverse Oracle scripts and ETL, so you can feel the freedom of the cloud quickly, with lower risk of disruption

/ Source scripts
  • Stored procedures
  • DML and DDL
  • Shell
  • PLSQL, SQL Loader/Spool, etc.
/ ETL scripts
  • Informatica, DataStage, Ab Initio, ODI
/ Analytics scripts
  • SAS, etc.
/ Logs
  • Assessment of EDW execution
/ Orchestration
  • Control-M, AutoSys, EPS, Cron Shell, Airflow, etc.

Discover the power of smarter, faster transformation from SQL Server

LeapLogic assesses and transforms diverse SQL Server scripts and ETL, so you can feel the freedom of the cloud quickly, with lower risk of disruption

/ Source scripts
  • Stored procedures
  • DML and DDL
  • Shell scripts with embedded SQL Server blocks
  • TSQL, etc.
/ ETL scripts
  • Informatica, DataStage, Ab Initio, ODI
/ Analytics scripts
  • SAS, etc.
/ Logs
  • Assessment of EDW execution logs
/ Orchestration
  • Control-M, AutoSys, EPS, Cron Shell, Airflow, etc.

Discover the power of smarter, faster transformation from Vertica

LeapLogic assesses and transforms diverse Vertica scripts and ETL, so you can feel the freedom of the cloud quickly, with lower risk of disruption

/ Source scripts
  • DDL
  • Shell
  • VSQL, Load, Export
/ ETL scripts
  • Informatica, DataStage, Ab Initio, ODI
/ Analytics scripts
  • SAS, etc.
/ Logs
  • Assessment of EDW execution
/ Orchestration
  • Control-M, AutoSys, EPS, Cron Shell, etc.

Discover the power of smarter, faster transformation from Informatica

LeapLogic assesses and transforms diverse Informatica code formats, so you can feel the freedom of the cloud quickly, with lower risk of disruption

  • Assesses XML files
  • Converts workflows and mappings to:
  • Cloud-native ETL: AWS Glue Studio, Azure Data Factory, etc.
  • Cloud-native warehouses: Databricks Lakehouse, Amazon Redshift, Azure Synapse, Google BigQuery, Snowflake
  • Open collaboration–based languages: PySpark, PyScala
  • Converts schema and maps data types for migration to the cloud or Hadoop

Discover the power of smarter, faster transformation from Datastage

LeapLogic assesses and transforms diverse Datastage code formats, so you can feel the freedom of the cloud quickly, with lower risk of disruption

  • Assesses XML/DSX files
  • Converts jobs and components to:
  • Cloud-native ETL: AWS Glue Studio, Azure Data Factory, etc.
  • Cloud-native warehouses: Databricks Lakehouse, Amazon Redshift, Azure Synapse, Google BigQuery, Snowflake
  • Open collaboration–based languages: PySpark, PyScala
  • Provides comprehensive ETL conversion reports
  • Converts schema and maps data types for migration to the cloud or Hadoop

Discover the power of smarter, faster transformation from
‍Ab Initio

LeapLogic assesses and transforms diverse Ab Initio code formats, so you can feel the freedom of the cloud quickly, with lower risk of disruption

  • Assesses KSH, XFR files
  • Converts ETL scripts/jobs to:
  • Cloud-native ETL: AWS Glue Studio, Azure Data Factory, etc.
  • Cloud-native warehouses: Databricks Lakehouse, Amazon Redshift, Azure Synapse, Google BigQuery, Snowflake
  • Open collaboration-based languages like PySpark, Spark Scala
  • Provides comprehensive ETL conversion reports
  • Converts schema and maps data types for migration to the cloud or Hadoop

Discover the power of smarter, faster transformation from SAS

LeapLogic assesses and transforms diverse SAS analytical scripts, so you can feel the freedom of the cloud quickly, with lower risk of disruption

  • Scripts and procedures
  • Macros, scheduler jobs, ad hoc queries
  • Constructs, functions, keywords, etc.
  • SAS-purposed ETL/data prep/procedural logic
  • Assessment of execution logs

Discover the power of smarter, faster transformation from Hadoop

LeapLogic assesses and transforms diverse Hadoop workloads, so you can feel the freedom of the cloud quickly, with lower risk of disruption

  • Assessment of jobs running on the Hadoop platform - Hive, Impala, Presto, Spark, MapReduce, Oozie, and Sqoop
  • Report resource utilization, duration, and frequency of occurrences
  • Identify unique workloads and queries
  • Classify workloads into processing, ingestion, and orchestration workloads
  • Storage analysis of the source Hadoop platform
  • Data temperature analysis by classifying data into hot, warm, cold, and frozen categories based on access
  • Hive table detailed analysis
  • Migration inventory creation for all unique workloads
  • Complexity classification of workloads
  • Classification of workloads to rehost, refactor and rebuild categories based on target technology mapping
  • Actionable recommendations for target technology – Amazon EMR, Redshift, Databricks, Azure Synapse, GCP Dataproc, BigQuery, Snowflake, etc.
  • Assessment of SQL artefacts – Scripts and queries for Hive SQL, Impala, Presto, and Spark SQL
  • Assessment of Code artefacts - MapReduce, Spark, Oozie, and Sqoop
  • Workload auto conversion and migration to target native equivalent using intelligent and pattern-based transformation
  • Automated data-based validation of transformed code
  • Validation support for a limited sample as well as full historic volumes
  • Row and cell-level query validation
  • Detailed validation report with success and failure counts and failure details
  • Operationalizes workloads
  • End-to-end target-specific executable package
  • Optimal price-performance ratio
  • Parallel run execution enablement, production deployment, and support
Know more

Meet your accelerated migration to AWS

 

With LeapLogic, your transformation to AWS will happen faster, with more accuracy, thanks to superior analysis, automation, and validation

/ Assessment
  • Get answers to key questions
  • Can I identify anti-patterns in my existing code and resolve as per AWS coding techniques and standards?
  • Will it make sense to design my future-state architecture using all AWS-native services (for orchestrating, monitoring, etc.)?
  • Will I know which workloads can benefit from EMR vs. Redshift cloud data warehouses?
  • Can I save provisioning and maintenance costs for rarely used workloads on AWS?
  • Data warehouse
  • Can I get schema optimization recommendations for distribution style and distkeys, sort keys?
  • ETL
  • Will the assessment help me choose AWS services for meeting ETL SLAs?
  • Analytics
  • Will it be beneficial to convert analytical functions to Spark ML-based libraries?
  • Will my ETL processing SLAs impact my choice of an optimum Amazon EMR cluster size?
  • Hadoop
  • Is my optimization strategy for Update/Merge on AWS Redshift apt?
  • Can I get schema optimization recommendations for partitioning, bucketing, clustering etc.?
/ transformation
  • Packaging and orchestration using AWS-native wrappers
  • Intelligent transformation engine, delivering up to 95% automation for:
  • Data warehouse – Amazon EMR, Redshift Spectrum, Amazon S3, Databricks on AWS, Amazon Redshift, Snowflake on AWS
  • ETL – AWS Glue Studio (with Blueprint artifacts), Amazon EMR, PySpark/Spark Scala
  • Analytics – Amazon EMR, PySpark/Spark Scala
  • Hadoop – Amazon Redshift, Snowflake on AWS, Presto query engine
/ validation
  • Pipeline-based automated validation
  • Auto-generation of reconciliation scripts
  • Automated SQL/query and business level validation
  • Cell-to-cell validation reports
  • Data type and entity-level matching
  • File to file validation
/ operationalization
  • Optimal cost-performance ratio
  • Productionization and go-live
  • Infrastructure as code
  • Execution using cloud-native orchestrators
  • Automated DevOps including CI/CD, etc.
  • Target environment stabilization
  • Smooth cut-over

Meet your accelerated migration to Azure

With LeapLogic, your transformation to Azure will happen faster, with more accuracy, thanks to superior analysis, automation, and validation

/ Assessment
  • Get answers to key questions
  • Can I identify anti-patterns in my existing code and resolve as per Azure coding techniques and standards?
  • Will it make sense to design my future-state architecture using all Azure-native services (for orchestrating, monitoring, etc.)?
  • 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 distribution style, indexing techniques, partitioning etc.?
  • ETL
  • Will the assessment help me choose Azure services for meeting ETL SLAs?
  • Analytics
  • Will it be beneficial to convert my analytical functions to Spark ML-based libraries?
  • Will my ETL processing SLAs impact my choice of an optimum Azure HDInsight cluster size?
  • Hadoop
  • Is my optimization strategy for Update/Merge on Azure Synapse apt?
  • Can I get schema optimization recommendations for partitioning, bucketing, clustering etc.?
/ transformation
  • Packaging and orchestration using Azure-native wrappers
  • Intelligent transformation engine, delivering up to 95% automation for:
  • Data warehouse – Azure HDInsight, Azure Synapse, Databricks on Azure, ADLS, Snowflake on Azure
  • ETL – Azure Data Factory, Azure HDInsight, PySpark/Spark Scala
  • Analytics – Azure HDInsight, PySpark/Spark Scala
  • Hadoop – Azure Synapse, Snowflake on Azure, Presto query engine
/ validation
  • Pipeline-based automated validation
  • Auto-generation of reconciliation scripts
  • Automated SQL/query and business level validation
  • Cell-to-cell validation reports
  • Data type and entity-level matching
  • File to file validation
/ operationalization
  • Optimal cost-performance ratio
  • Productionization and go-live
  • Infrastructure as code
  • Execution using cloud-native orchestrators
  • Automated DevOps including CI/CD, etc.
  • Target environment stabilization
  • Smooth cut-over

Meet your accelerated migration to GCP

With LeapLogic, your transformation to GCP will happen faster, with more accuracy, thanks to superior analysis, automation, and validation

/ Assessment
  • Get answers to key questions
  • Can I identify anti-patterns in my existing code and resolve as per GCP coding techniques and standards?
  • Will it make sense to design my future-state architecture using all GCP-native services (for orchestrating, monitoring, etc.)?
  • Can I save provisioning and maintenance costs for rarely used workloads on GCP?
  • Data warehouse
  • Can I get schema optimization recommendations for partitioning, bucketing, clustering, etc.?
  • ETL
  • Will the assessment help me choose GCP services for meeting ETL SLAs?
  • Analytics
  • Will it be beneficial to convert my analytical functions to Spark ML-based libraries?
  • Will my ETL processing SLAs impact my choice of an optimum Dataproc cluster size?
  • Hadoop
  • Is my optimization strategy for Update/Merge on BigQuery apt?
/ transformation
  • Packaging and orchestration using GCP-native wrappers
  • Intelligent transformation engine, delivering up to 95% automation for:
  • Data warehouse – BigQuery, Dataproc, Databricks on GCP, Snowflake on GCP
  • ETL – Dataflow, Dataproc, PySpark/Spark Scala
  • Analytics – Dataproc, PySpark/Spark Scala
  • Hadoop – BigQuery, Snowflake on GCP, Presto query engine
/ validation
  • Pipeline-based automated validation
  • Auto-generation of reconciliation scripts
  • Automated SQL/query and business level validation
  • Cell-to-cell validation reports
  • Data type and entity-level matching
  • File to file validation
/ operationalization
  • Optimal cost-performance ratio
  • Productionization and go-live
  • Infrastructure as code
  • Execution using cloud-native orchestrators
  • Automated DevOps including CI/CD, etc.
  • Target environment stabilization
  • Smooth cut-over

Meet your accelerated migration to Snowflake

With LeapLogic, your transformation to Snowflake will happen faster, with more accuracy, thanks to superior analysis, automation, and validation

/ Assessment
  • Get answers to key questions
  • Can I identify anti-patterns in my existing code and resolve as per Snowflake coding techniques and standards?
  • Will it make sense to design my future-state architecture using all Snowflake-cloud–native services (AWS/Azure/GCP) for orchestrating, monitoring, etc.?
  • What should be the optimum auto-scaling rule for my Snowflake cluster based on my reporting needs?
  • 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 Snowflake cluster size?
  • Hadoop
  • Is my optimization strategy for Update/Merge apt for Snowflake on AWS/Azure/GCP?
/ transformation
  • Packaging and orchestration using Snowflake-native wrappers
  • Intelligent transformation engine, delivering up to 95% automation for:
  • Data warehouse – Snowflake on AWS/Azure/GCP
  • ETL – Snowflake on AWS/Azure/GCP
  • Hadoop – Snowflake, Presto query engine
/ validation
  • Pipeline-based automated validation
  • Auto-generation of reconciliation scripts
  • Automated SQL/query and business level validation
  • Cell-to-cell validation reports
  • Data type and entity-level matching
  • File to file validation
/ operationalization
  • Optimal cost-performance ratio
  • Productionization and go-live
  • Infrastructure as code
  • Execution using cloud-native orchestrators
  • Automated DevOps including CI/CD, etc.
  • Target environment stabilization
  • Smooth cut-over

Meet your accelerated migration to Databricks

 

With LeapLogic, your transformation to Databricks will happen faster, with more accuracy, thanks to superior analysis, automation, and validation

/ Assessment
  • Get answers to key questions
  • Can I identify anti-patterns in my existing code and resolve as per Databricks Lakehouse coding techniques and standards?
  • 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, bucketing, clustering, 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?
/ transformation
  • Packaging and orchestration using Databricks-native wrappers
  • Intelligent transformation engine, delivering up to 95% automation for:
  • Data warehouse and ETL – Databricks Lakehouse, Databricks Notebook, Databricks Workflows, Delta Lake
  • Analytics – Databricks Lakehouse on AWS/Azure/GCP, PySpark/Spark Scala
  • Hadoop – Databricks Lakehouse on AWS/Azure/GCP, Presto query engine
/ validation
  • Pipeline-based automated validation
  • Auto-generation of reconciliation scripts
  • Automated SQL/query and business level validation
  • Cell-to-cell validation reports
  • Data type and entity-level matching
  • File to file validation
/ operationalization
  • Optimal cost-performance ratio
  • Productionization and go-live
  • Infrastructure as code
  • Execution using cloud-native orchestrators
  • Automated DevOps including CI/CD, etc.
  • Target environment stabilization
  • Smooth cut-over

Meet your accelerated migration to Spark

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

/ Assessment
  • Get answers 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?
  • 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
  • Pipeline-based automated validation
  • Auto-generation of reconciliation scripts
  • Automated SQL/query and business level validation
  • Cell-to-cell validation reports
  • Data type and entity-level matching
  • File to file validation
/ operationalization
  • Optimal cost-performance ratio
  • Productionization and go-live
  • Infrastructure as code
  • Execution using cloud-native orchestrators
  • Automated DevOps including CI/CD, etc.
  • Target environment stabilization
  • Smooth cut-over

Please choose at least one specific source or destination

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Explore real results

CASE STUDY

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

CASE STUDY

20% SLA improvement by modernizing Teradata workloads on Azure

CASE STUDY

50% cost and time savings when transforming Informatica workflows and Oracle EDW to AWS

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