Get a firsthand look at what Databricks looks like from a practitioner’s perspective with these simple on-demand videos. Each of the demos below is paired with related materials — including notebooks, videos and eBooks — to help further your understanding of different Databricks products and common use cases. Wherever possible, we’ve provided the actual notebook used in the demo so that you can try it out for yourself in Databricks Community Edition.
In this demo, we walk through a high-level overview of the Databricks unified data analytics platform, including discussion of how open source projects including Apache SparkTM, Delta Lake, MLflow, and Koalas, fit into the Databricks ecosystem. We then cover the Data Science Workspace, launching Spark clusters, and collaborative notebook features, before shifting our focus to Delta Lake, time travel, and SQL Analytics.
With Delta Lake on Databricks, you can build a lakehouse architecture that combines the best parts of data lakes and data warehouses. This simple, open platform both stores and manages all of your data and supports all of your analytics and AI use cases. In this demo, we cover the main features of Delta Lake, including unified batch and streaming data processing, schema enforcement and evolution, time travel, and support for UPDATEs/MERGEs/DELETEs, as well as touching on some of the performance enhancements available with Delta Lake on Databricks.
In this demo, we walk through some of the features of the new Databricks SQL Analytics that are important to data analysts, including the integrated data browser, SQL query editor with live autocomplete, built-in data visualization tools, and flexible dashboarding and alerting capabilities. We also cover how SQL Analytics endpoints provide a high-performance, low latency, SQL-optimized compute resource that can power your existing BI tools like PowerBI and Tableau.
In this demo, we walk through a real-world data science and machine learning use case on Databricks, showing how different members of the data team can interact and collaborate on the Databricks platform. We also show how MLflow on Databricks simplifies and streamlines the end-to-end machine learning life cycle.
With Databricks Auto Loader, you can incrementally and efficiently ingest new batch and real-time streaming data files into your Delta Lake tables as soon as they arrive — so that they always contain the most complete and up-to-date data available. SQL users can use the simple “COPY INTO” command to pull new data into their Delta Lake tables automatically, without the need to keep track of which files have already been processed.