Talk Description:
The Modern Data Warehouse architecture is a response to the emergence of Big Data, Machine Learning and Advanced Analytics. DevOps is a key aspect of successfully operationalising a multi-source Modern Data Warehouse.
While there are many examples of how to build CI/CD pipelines for traditional applications, applying these concepts to Big Data Analytical Pipelines is a relatively new and emerging area. In this demo heavy session, we will see how to apply DevOps principles to an end-to-end Data Pipeline built on the Microsoft Azure Data Platform with technologies such as Data Factory, Databricks, Data Lake Gen2, Azure Synapse, and AzureDevOps.
Resources: https://aka.ms/mdw-dataops
27. People. Process. Products.
DevOps is the union of people,
process, and products to enable
continuous delivery of value to
your end users.
“
”
Build
& Test
Continuous
Delivery
Deploy
Operate
Monitor
&
Learn
Plan
&
Track
Develop
28. DataOps is NOT Just DevOps for Data, Data Kitchen
Data Data Pipeline Value
29. DataOps is NOT Just DevOps for Data, Data Kitchen
Data Production Value
30. Cloud-hosted pipelines for Linux, Windows and
macOS, with unlimited minutes for open source
Azure Pipelines
Multi language, platform, and
cloud support
Extensible
Best-in-class for open source
https://azure.com/pipelines➔
Containers and Kubernetes
31. Deliver value to your users faster
using proven agile tools to plan,
track, and discuss work across your
teams.
Build, test, and deploy with CI/CD that
works with any language, platform, and
cloud. Connect to GitHub or any other
Git provider and deploy continuously.
Get unlimited, cloud-hosted private
Git repos and collaborate to build
better code with pull requests and
advanced file management.
Test and ship with confidence
using manual and exploratory
testing tools.
Create, host, and share packages with
your team, and add artifacts to your
CI/CD pipelines with a single click.
Azure Boards Azure ReposAzure Pipelines
Azure Test Plans Azure Artifacts
https://azure.com/devops
➔