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Getting Started with Databricks SQL Analytics
The document discusses getting started with Databricks SQL Analytics, highlighting the lakehouse architecture and its benefits for data engineers and BI developers. It covers key concepts such as SQL endpoint configuration, writing queries, building dashboards, and integrating with Power BI. Feedback from users is encouraged to improve the experience and functionalities.
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Overview of the presentation by Simon Whiteley on Databricks SQL Analytics.
Topics covered include The Lakehouse Challenge, SQL Analytics, and Key Concepts.
Exploration of the Lakehouse architecture and its components, emphasizing benefits for data engineers and users.
Discusses the challenges faced by BI Developers and Report Consumers when using SQL analytics.
Introduction to Databricks SQL Analytics and its purpose.
Description of Data Science and SQL Analytics workspaces, focusing on their features and operations.
Details on SQL Endpoint Configuration, Queries, Dashboards, Alerts, and History in SQL Analytics.
Steps to set up SQL analytics, including endpoint setup, query writing, dashboard building, and alerts.
Integration of Databricks SQL Analytics with Power BI.
Encourages BI community participation in the Lakehouse Architecture through Databricks SQL Analytics.
Encouragement for feedback on the session to improve future presentations.
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Getting Started with Databricks SQL Analytics
- 1. Getting Started with DatabricksSQL Analytics Simon Whiteley Director of Engineering, Advancing Analytics
- 2. Agenda § The LakehouseChallenge § SQL Analytics § Key Concepts
- 3.
- 4. RAW BASE PARQUET The ModernWarehouse Data Engineers & Data Scientists Report Consumers BI Developers
- 5. RAW BASE ENRICHED DELTADELTA The Lakehouse Delta brings huge benefits to data engineers, but what about the other users in our ecosystem?
- 6. Challenges Love writing SQL,Care about performance & modelling Love SQL typedown, code snippets, execution plans Not Spark Developers Just want their dashboards to work Minimal Technical barriers, Maximum Performance BI Developers Report Consumers
- 7.
- 8. Data Science & Engineering Workspace SQL Analytics Workspace •Full Technical Control • Many Languages • Notebook Development Environment • Simplified Controls • SQL-Only • Query Builder & Dashboarding
- 9. Data Science & Engineering Workspace SQL Analytics Workspace LakeData Acquire & Transform Data into Lake Surface Data as Hive Tables SQL Exploring, Shaping & Visualising the data via SQL
- 10. Key Concepts –SQL Endpoint Configuration
- 11. Key Concepts –Queries
- 12. Key Concepts –Dashboards
- 13. Key Concepts –Alerts
- 14. Key Concepts –History
- 15. SQL Analytics Workspace §Setting up an Endpoint § Writing Queries § Building Dashboards § Alerts & History
- 16. SQL Analytics &Power BI
- 17. SQL Analytics &Power BI
- 18. Completing Your LakehouseJourney
- 19. RAW BASE ENRICHED DELTADELTA Databricks SQL Analytics welcomes the BI community into the Lakehouse Architecture
- 20. Feedback Your feedback isimportant to us. Don’t forget to rate and review the sessions.