- Get answers to key questions
- 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, 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?
- Packaging and orchestration using Databricks-native wrappers
- Intelligent transformation engine, delivering up to 95% automation for:
- Data warehouse and ETL to Databricks migration – Databricks Lakehouse, Databricks Notebook, Databricks Jobs, Databricks Workflows, Delta Lake, Delta Live Tables
- Analytics to Databricks migration – Databricks Lakehouse on AWS/Azure/Google Cloud, PySpark
- Hadoop to Databricks migration – Databricks Lakehouse on AWS/Azure/Google Cloud, Presto query engine
- 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
- Capacity planning for optimal cost-performance ratio
- Performance optimization
- Robust cutover planning
- Infrastructure as code
- CI/CD
- Provisioning of Databricks Lakehouse and other required services
- Get answers to key questions
- 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, 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?
- Packaging and orchestration using Databricks-native wrappers
- Intelligent transformation engine, delivering up to 95% automation for:
- Data warehouse and ETL to Databricks migration – Databricks Lakehouse, Databricks Notebook, Databricks Jobs, Databricks Workflows, Delta Lake, Delta Live Tables
- Analytics to Databricks migration – Databricks Lakehouse on AWS/Azure/Google Cloud, PySpark
- Hadoop to Databricks migration – Databricks Lakehouse on AWS/Azure/Google Cloud, Presto query engine
- 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
- Capacity planning for optimal cost-performance ratio
- Performance optimization
- Robust cutover planning
- Infrastructure as code
- CI/CD
- Provisioning of Databricks Lakehouse and other required services
Webinar
Soham Bhatt
Lead Solutions Architect, EDW Migrations, Databricks
Hear Databricks and Impetus experts discuss how intelligent, automated modernization can help your data teams move faster, simplify processes, and speed up decision-making.
Watch nowCustomer session
Junjun (Robert) Yue
Director of Data Science, AARP Services, Inc.
Discover how AARP Services, Inc. (ASI) leveraged LeapLogic to automate the conversion of 10K+ SAS code lines to Databricks, accelerating their transformation.
Webinar
David Stodder
Senior Director of Research for BI, TDWI
Hear TDWI, Databricks, and Impetus experts on how automation accelerates cloud migration and analytics modernization while minimizing disruptions.
4x
faster transformation
2x
lower cost
1.5x
faster validation
2x
less manual effort
Innovative sustainable energy provider
- ~60% reduction in modernization time with automated conversion of MS SQL Server and SSIS to AWS Databricks
Leading data analytics company
- ~5K SAS scripts with 1.8 million lines of code migrated to PySpark
- 50% time and cost savings using cloud-native services
A leading global entertainment company
- 85% auto-transformation of Teradata and SAP BODS workload to Databricks, resulting in complete operational readiness within 14 months
F100 US-based retailer
- 25% cost reduction by sunsetting legacy platforms
- 30% performance improvement via automation
- Auto-conversion of 85% of 140 complex
Leading global provider of B2B data and insights
- ~60% auto-conversion of complex models , 80-100% auto-conversion of medium and simple SAS modules to PySpark
