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Tutorials

Dinesh Chandnani edited this page Apr 1, 2019 · 42 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 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 try out locally by running docker container. When running locally there are no dependencies on Azure, however, note that the functionality in local mode 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.

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

Local mode:

  1. Create a pipeline locally with no cloud dependencies in 5 minutes!
  2. Add an Alert
  3. Aggregated Alerts
  4. Output to disk
  5. Add SQL to add metrics
  6. Debug jobs using Spark logs
  7. Add a Tag to your data
  8. Add Aggregated Tags to your data
  9. Use Reference Data to augment streaming data
  10. Use Time Windowing to alert on larger window of data
  11. Use Accumulator to store data in-memory for jobs
  12. Use UDF and UDAF to call Scala code
  13. Customize the schema
  14. 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

Data Accelerator

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