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Rohit Agrawal - MSFT edited this page

Sep 30, 2019

ยท42 revisions

Learn how to use Data Accelerator step by step and get started setting up your big data pipeline in minutes. Data Accelerator provides all the tools necessary to go from simple to complex requirements, all within easy-to-use portal.

To unleash the full power Data Accelerator, deploy to Azure and check the tutorials for cloud mode below. We have also enabled a "hello world" experience that you can try out locally by running a docker container. When running locally there are no dependencies on Azure, however, the functionality is very limited and only there to give you a very cursory overview of Data Accelerator. Deploy locally using these instructions and then check out the tutorials of local mode below.

will walk you through both, the local mode as well as the cloud mode, step by step.

Local mode:

  1. Running samples
  2. Create a pipeline locally with no cloud dependencies in 5 minutes!
  3. Set up simple alert without writing any code
  4. Set up aggregated alert without writing any code
  5. Output to disk
  6. Tagging - Simple Rules
  7. Tagging - Aggregate Rules
  8. SQL queries - More powerful queries using SQL
  9. Create new Metric chart
  10. Debug jobs using Spark logs
  11. Use Reference Data to augment streaming data
  12. Windowing functions
  13. Use UDF and UDAF in your code
  14. Customize the schema
  15. Scale docker host

Cloud mode:

  1. Create a pipeline in 5 minutes!
  2. Live Query - Save hours by validating query in seconds!
  3. Set up simple alert without writing any code
  4. Set up aggregate alert without writing any code
  5. Set up new outputs without writing any code
  6. Tagging - Simple Rules
  7. Tagging - Aggregate Rules
  8. Tagged data flowing to CosmosDB
  9. SQL Queries - More powerful queries using SQL
  10. Create new Metric chart
  11. Windowing functions
  12. Using Reference data
  13. Use UDF, UDAF, Azure Functions in your query
  14. Use Accumulator to store data in-memory for jobs
  15. Scale up a deployment
  16. Diagnose issues using Spark logs
  17. Diagnose issues using Telemetry
  18. Inviting others and Roles based access
  19. Generate custom data with the Simulator
  20. Customize a Cloud Deployment
  21. Use input EventHub/IotHub in a different tenant
  22. Local Cloud Debugging
  23. Schedule a batch job
  24. Output data to Azure SQL Database
  25. Run Data Accelerator Flows on Databricks

Wiki pages Pages 53

Data Accelerator

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