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YOUR MIGRATION SOLUTION

The time to experience the benefits of the cloud is now—and LeapLogic makes
migration possible, no matter your business workflows or needs

explore your benefits
 * WHY LEAPLOGIC?
 * HOW IT WORKS
 * SOURCE-TARGET SUPPORT
 * + PARTNERS PARTNERS
   * SYSTEM INTEGRATORS
   * + CLOUD INTEGRATORS Cloud INTEGRATORS
     * AWS
     * DATABRICKS
     * SNOWFLAKE
     * AZURE
     * Google Cloud
 * INSIGHTS
 * GET STARTED
 * Try now




SELECT ANY SOURCE AND TARGET BELOW


EXPLORE THE TRANSFORMATION POTENTIAL

Legacy data warehouse, ETL, or analytics system










ANY

ANY
Modern target platform
View details


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 analytics 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
 * Data steps, tasks, functions, etc.
 * SAS-purposed ETL/statistical/advanced algorithmic logic
 * Assessment of SAS scripts




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, Google Cloud 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
   
 * Will it make sense to design my future-state architecture using all
   AWS-native services (for data processing and storage, orchestrating,
   analytics, monitoring, etc.)?
 * Will I know which workloads can benefit from EMR vs. Redshift cloud data
   warehouses or any other?
 * 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-based
   libraries or AWS-native services?
 * How can I accurately transform my legacy analytical models?
 * How can I effectively transform thousands of conditional statements, macros,
   complex statistical and algorithmic logic to the new target service
   maintaining/enhancing the precision of the model?
 * Hadoop
   
 * Is my optimization strategy for Update/Merge on target AWS stack apt?

/ 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
 * Hadoop – Amazon Redshift, Snowflake on AWS, Presto query engine


/ validation
 * Pipeline-based automated validation
 * Auto-generation of reconciliation scripts
 * Cell-to-cell validation reports
 * Data type and entity-level matching
 * File to file validation
 * Assurance of data and logic consistency and parity in the new target
   environment


/ 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
   
 * Will it make sense to design my future-state architecture using all
   Azure-native services (for data processing and storage, orchestrating,
   analytics, monitoring, etc)?
 * Will I know which workloads can benefit from HDInsight vs. Synapse cloud data
   warehouses or any other?
 * 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 Azure-based
   libraries?
 * How can I accurately transform my legacy analytical models?
 * How can I effectively transform thousands of conditional statements, macros,
   complex statistical and algorithmic logic to the new target service
   maintaining/enhancing the precision of the model?
 * Hadoop
   
 * Is my optimization strategy for Update/Merge on target Azure stack apt?

/ 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
 * Hadoop – Azure Synapse, Snowflake on Azure, Presto query engine


/ validation
 * Pipeline-based automated validation
 * Auto-generation of reconciliation scripts
 * Cell-to-cell validation reports
 * Data type and entity-level matching
 * File to file validation
 * Assurance of data and logic consistency and parity in the new target
   environment


/ 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 GOOGLE CLOUD

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

/ Assessment
 * Get answers to key questions
   
 * Will it make sense to design my future-state architecture using all Google
   Cloud-native services (for data processing and storage, orchestrating,
   analytics, monitoring, etc.)?
 * Can I save provisioning and maintenance costs for rarely used workloads on
   Google Cloud?
 * Data warehouse
   
 * Can I get schema optimization recommendations for partitioning, bucketing,
   clustering, etc.?
 * ETL
   
 * Will the assessment help me choose Google Cloud services for meeting ETL
   SLAs?
 * Analytics
   
 * Will it be beneficial to convert my analytical functions to Google
   Cloud-based libraries?
 * How can I accurately transform my legacy analytical models?
 * How can I effectively transform thousands of conditional statements, macros,
   complex statistical and algorithmic logic to the new target service
   maintaining/enhancing the precision of the model?
 * Hadoop
   
 * Is my optimization strategy for Update/Merge on target Google Cloud apt?

/ transformation
 * Packaging and orchestration using Google Cloud-native wrappers
 * Intelligent transformation engine, delivering up to 95% automation for:
 * Data warehouse – BigQuery, Dataproc, Databricks on Google Cloud, Snowflake on
   Google Cloud
 * ETL – Dataflow, Dataproc, PySpark/Spark Scala
 * Analytics – Dataproc, PySpark
 * Hadoop – BigQuery, Snowflake on Google Cloud, Presto query engine


/ validation
 * Pipeline-based automated validation
 * Auto-generation of reconciliation scripts
 * Cell-to-cell validation reports
 * Data type and entity-level matching
 * File to file validation
 * Assurance of data and logic consistency and parity in the new target
   environment


/ 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
   
 * Will it make sense to design my future-state architecture using all
   Snowflake-native services (for data processing and storage, orchestrating,
   analytics, BI/reporting, etc.)?
 * What should be the optimum auto-scaling rule for my Snowflake cluster based
   on my reporting needs?
 * Can I save provisioning and maintenance costs for rarely used workloads on
   Snowflake?
 * Data warehouse
   
 * Can I get schema optimization recommendations for partitioning, clustering,
   and more?
 * ETL
   
 * Will my ETL processing SLAs impact my choice for an optimum Snowflake cluster
   size?
 * Analytics
   
 * Will it be beneficial to convert analytical functions to Spark libraries or
   some native AWS functions?
 * Will my ETL processing SLAs impact my choice of an optimum Amazon EMR cluster
   size?
 * Hadoop
   
 * Is my optimization strategy for Update/Merge apt for Snowflake?
 * Can I get schema optimization recommendations for partitioning, clustering,
   and more?

/ transformation
 * Packaging for and orchestration using Snowflake-native services
 * Intelligent transformation engine, delivering up to 95% automation for:
 * Data warehouse – Snowflake
 * ETL – Snowflake
 * Analytics – Snowpark on Snowflake
 * Hadoop – Snowflake, 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
 * File-to-file validation
 * Integration testing on enterprise datasets
 * Assurance of data and logic consistency and parity in the new target
   environment


/ operationalization
 * Productionization and go-live
 * Capacity planning for optimal cost-performance ratio
 * Performance optimization
 * Robust cutover planning
 * Infrastructure as code
 * Automated CI/CD
 * Data warehouse – Provisioning of Snowflake and other AWS services for
   orchestration, monitoring, security, etc.
 * ETL – Provisioning of Snowflake and other required services
 * Analytics – Provisioning of Snowflake and other required services
 * BI/Reporting – Provisioning of Snowflake
 * Hadoop – Provisioning of Snowflake and other required services





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.)?
   
 * 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 – Databricks Lakehouse, Databricks Notebook,
   Databricks Jobs, Databricks Workflows, Delta Lake, Delta Live Tables
 * Analytics – Databricks Lakehouse on AWS/Azure/Google Cloud, PySpark
 * Hadoop – Databricks Lakehouse on AWS/Azure/Google Cloud, 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
   
 * 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-based
   libraries?
 * How can I accurately transform my legacy analytical models?
 * How can I effectively transform thousands of conditional statements, macros,
   complex statistical and algorithmic logic to the new target service
   maintaining/enhancing the precision of the model?

/ 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
 * Cell-to-cell validation reports
 * Data type and entity-level matching
 * File to file validation
 * Assurance of data and logic consistency and parity in the new target
   environment


/ 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

/1


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

View Case Studies
/2


TRANSFORM YOUR WORKLOAD, TRANSFORM YOUR REALITY

book a demo
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