Migration to azure databricks

With up to 95% automation spanning across four steps, eliminate unpredictable variables while transforming legacy logic and code.

Get answers to key questions
Data warehouse
Will it make sense to design my future-state architecture using all Azure Databricks-native services (for data processing and storage, orchestrating, monitoring, BI/reporting, etc.)?
Can I get schema optimization recommendations for distribution style, indexing techniques, partitioning, bloom filters, ZOrder indexing, etc.?
Will I know which workloads can benefit from HDInsight vs. Synapse analytics platform?
ETL
Will my ETL processing SLAs impact my choice for an optimum Databricks cluster size?
Will the assessment help me choose Azure services for meeting ETL SLAs?
Hadoop
Is my optimization strategy for Update/Merge on Azure on Databricks apt?
Can I get schema optimization recommendations for distribution style, indexing techniques, partitioning, etc.?
Analytics
Can I transform my analytics layer along with my data warehouse, ETL systems, and BI?
Will it be beneficial to convert my analytical functions to Spark libraries or some native Azure functions?
Will my ETL processing SLAs impact my choice of an optimum Azure HDInsight cluster size?
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 Azure Databricks-native wrappers/services
Intelligent transformation engine, delivering up to 95% automation for:

Data warehouse – Databricks Lakehouse, Databricks Notebook, Databricks Jobs, Databricks Workflows, Delta Lake, Delta Live Tables, Azure HDInsight, and Azure Synapse
ETL – Azure Data Factory, Azure Synapse, Databricks notebook, Databricks jobs, Workflows
Analytics – Databricks Lakehouse on Azure, Databricks notebook, Databricks jobs, Workflows
BI/Reporting – Azure Power BI
Hadoop – Databricks Lakehouse on Azure, Presto query engine, Databricks Notebook, Databricks Jobs, Databricks workflows, Databricks Lakehouse, Delta Live Tables

Business logic (with a high degree of automation)
File-to-file validation
Cell-by-cell validation
Integration testing on enterprise datasets
Assurance of data and logic consistency and parity in Azure Databricks

Capacity planning for optimal cost-performance ratio
Performance optimization
Robust cutover planning
Infrastructure as code
Provisioning of Databricks Lakehouse, ADLS/HDInsight/Synapse, and other Azure services for orchestration, monitoring, security, etc.
Automated CI/CD
Productionization and go-live

Assessment

Get answers to key questions
Data warehouse
Will it make sense to design my future-state architecture using all Azure Databricks-native services (for data processing and storage, orchestrating, monitoring, BI/reporting, etc.)?
Can I get schema optimization recommendations for distribution style, indexing techniques, partitioning, bloom filters, ZOrder indexing, etc.?
Will I know which workloads can benefit from HDInsight vs. Synapse analytics platform?
ETL
Will my ETL processing SLAs impact my choice for an optimum Databricks cluster size?
Will the assessment help me choose Azure services for meeting ETL SLAs?
Hadoop
Is my optimization strategy for Update/Merge on Azure on Databricks apt?
Can I get schema optimization recommendations for distribution style, indexing techniques, partitioning, etc.?
Analytics
Can I transform my analytics layer along with my data warehouse, ETL systems, and BI?
Will it be beneficial to convert my analytical functions to Spark libraries or some native Azure functions?
Will my ETL processing SLAs impact my choice of an optimum Azure HDInsight cluster size?
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 Azure Databricks-native wrappers/services
Intelligent transformation engine, delivering up to 95% automation for:

Data warehouse – Databricks Lakehouse, Databricks Notebook, Databricks Jobs, Databricks Workflows, Delta Lake, Delta Live Tables, Azure HDInsight, and Azure Synapse
ETL – Azure Data Factory, Azure Synapse, Databricks notebook, Databricks jobs, Workflows
Analytics – Databricks Lakehouse on Azure, Databricks notebook, Databricks jobs, Workflows
BI/Reporting – Azure Power BI
Hadoop – Databricks Lakehouse on Azure, Presto query engine, Databricks Notebook, Databricks Jobs, Databricks workflows, Databricks Lakehouse, Delta Live Tables

Validation

Business logic (with a high degree of automation)
File-to-file validation
Cell-by-cell validation
Integration testing on enterprise datasets
Assurance of data and logic consistency and parity in Azure Databricks

Operationalization

Capacity planning for optimal cost-performance ratio
Performance optimization
Robust cutover planning
Infrastructure as code
Provisioning of Databricks Lakehouse, ADLS/HDInsight/Synapse, and other Azure services for orchestration, monitoring, security, etc.
Automated CI/CD
Productionization and go-live

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Spotlight

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 now

Customer 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.

Watch now

Webinar