Automated Data and Analytics Workload Modernization
How United Airlines automated legacy data stack modernization with LeapLogic
Modernizing Your ETL for New Opportunities
/ 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, etc.)?
- 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 AWS 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/AWS 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
/2
Automated workload migration: Hadoop to Amazon EMR
Automated reporting migration to AWS: Tableau to Amazon QuickSight
Automated data warehouse migration to AWS: Oracle to Amazon Aurora
Automated reporting migration to AWS: OBIEE to Amazon QuickSight
/3
Explore resources to support your transformation initiatives
CASE STUDY 
Data platform migration and modernization on AWS significantly reduces passenger wait time for United Airlines
CASE STUDY
A bank’s analytics transformation journey - Automated assessment and transformation of Informatica workflows and Oracle EDW to AWS
CASE STUDY
Automated Netezza data warehouse to AWS migration for a Fortune 500 mortgage lender
