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TimeXtender with cloud scale analytics

Azure Analysis Services
Azure Data Lake Storage
Azure Databricks
Azure Synapse Analytics

Solution ideas

This article is a solution idea. If you'd like us to expand the content with more information, such as potential use cases, alternative services, implementation considerations, or pricing guidance, let us know by providing GitHub feedback.

This solution idea describes how to use the TimeXtender graphical interface to define a data estate.

Architecture

Diagram showing the dataflow for TimeXtender with cloud scale analytics solution.

Download a Visio file of this architecture.

Dataflow

  1. Combine all your structured and semi-structured data in Azure Data Lake Storage using TimeXtender's data engineering pipeline with hundreds of native data connectors.
  2. Clean and transform data using the powerful analytics and computational ability of Azure Databricks.
  3. Move cleansed and transformed data to Azure Synapse Analytics, creating one hub for all your data. Take advantage of native connectors between Azure Databricks (PolyBase) and Azure Synapse Analytics to access and move data at scale.
  4. Build operational reports and analytical dashboards on top of SQL Database to derive insights from the data and use Azure Analysis Services to serve the data.
  5. Run ad-hoc queries directly on data within Azure Databricks.

Components

Scenario details

You can use TimeXtender to define a data estate via a graphical user interface. Definitions are stored in a metadata repository. Code for building the data estate is generated automatically while remaining fully customizable. The results are a modern data warehouse that is ready to support cloud scale analytics and AI.

Potential use cases

  • No infrastructure issues or maintenance
  • Consistent performance
  • Deploy and manage both the architecture and the data pipelines, data models and semantic models

Next steps