Automated Legacy Workload Migration to AWS Cloud

Experience 4x faster AWS migration at half the cost with end-to-end automation in four steps

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, BI/reporting, and more)?
Will I know which workloads can benefit from EMR vs. Redshift cloud data warehouses or AWS Glue, Lambda, Step Functions, etc.?
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 dist keys, sort keys, etc.?
ETL
Will the assessment help me choose AWS-native services for meeting ETL SLAs?
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 on Amazon Redshift apt?
Can I get schema optimization recommendations for distribution style and dist keys, sort keys, etc.?
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?

Packaging for and orchestration using AWS-native services
Intelligent transformation engine, delivering up to 95% automation for:

Data warehouse to AWS stack migration – Amazon EMR, Amazon Redshift, Snowflake on AWS, Databricks on AWS
ETL to AWS stack migration – AWS Glue, Amazon Redshift, PySpark
Analytics to AWS stack migration – Amazon EMR, PySpark
BI/Reporting to AWS stack migration – Amazon QuickSight
Hadoop to AWS migration – Amazon Redshift, Snowflake on AWS, Presto query engine

All transformed data warehouse, ETL, analytics, BI/reporting, 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

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 Amazon EMR/Amazon EC2/Amazon Redshift/Snowflake, and other AWS services for orchestration, monitoring, security, etc.
ETL – Provisioning of AWS Glue and other required services
Analytics – Provisioning of Amazon EMR and other required services
BI/Reporting – Provisioning of Amazon QuickSight
Hadoop – Provisioning of Redshift/Snowflake on AWS and other required services

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, BI/reporting, and more)?
Will I know which workloads can benefit from EMR vs. Redshift cloud data warehouses or AWS Glue, Lambda, Step Functions, etc.?
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 dist keys, sort keys, etc.?
ETL
Will the assessment help me choose AWS-native services for meeting ETL SLAs?
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 on Amazon Redshift apt?
Can I get schema optimization recommendations for distribution style and dist keys, sort keys, etc.?
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 for and orchestration using AWS-native services
Intelligent transformation engine, delivering up to 95% automation for:

Data warehouse to AWS stack migration – Amazon EMR, Amazon Redshift, Snowflake on AWS, Databricks on AWS
ETL to AWS stack migration – AWS Glue, Amazon Redshift, PySpark
Analytics to AWS stack migration – Amazon EMR, PySpark
BI/Reporting to AWS stack migration – Amazon QuickSight
Hadoop to AWS migration – Amazon Redshift, Snowflake on AWS, Presto query engine

Validation

All transformed data warehouse, ETL, analytics, BI/reporting, 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 Amazon EMR/Amazon EC2/Amazon Redshift/Snowflake, and other AWS services for orchestration, monitoring, security, etc.
ETL – Provisioning of AWS Glue and other required services
Analytics – Provisioning of Amazon EMR and other required services
BI/Reporting – Provisioning of Amazon QuickSight
Hadoop – Provisioning of Redshift/Snowflake on AWS and other required services

EXPLORE THE 4-STEP PROCESS

Trusted by Fortune 500 companies

Spotlight

Webinar

Veena Vasudevan

Senior Big Data Solutions Architect, AWS

Automated data and analytics workload modernization

Watch now

Customer perspective

Sarang Bapat

Director of Data Engineering, United Airlines

How United Airlines improved customer experience by modernizing its data and analytics platform

Watch now

Webinar

Yevgeniy Kravchenko

Sr. Worldwide GTM Specialist, AWS Glue AWS

Modernizing your ETL for new opportunities

Watch now

The LeapLogic advantage

4x

faster transformation

2x

lower cost

1.5x

faster validation

2x

less manual effort

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Our real-world AWS migration experience

USA Airline

90% auto-conversion of Teradata BTEQs to Redshift
22% time and 70% effort saved compared to manual efforts

US federal firm

80% automated transformation of Informatica workloads to AWS Glue
50% time and cost reduction with automated migration

Top 100 American bank

80% Informatica and Oracle workloads auto-transformed to an AWS-native stack
54% improvement in data ingestion and 93% improvement with Tableau extracts on Amazon Redshift vs. Oracle

Fortune 500 global hospitality firm