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?
- Will I get recommendations for those BI reports which can be handled through conversational interfaces such as Power BI CoPilot or Databricks Genie?
- 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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