3/21/2023 0 Comments Databricks lakehouse![]() ![]() The terms bronze (raw), silver (validated), and gold (enriched) describe the quality of the data in each of these layers. This architecture guarantees atomicity, consistency, isolation, and durability as data passes through multiple layers of validations and transformations before being stored in a layout optimized for efficient analytics. Databricks recommends taking a multi-layered approach to building a single source of truth for enterprise data products. The medallion architecture describes a series of data layers that denote the quality of data stored in the lakehouse. And, to learn more about how Alation leverages Databricks Analytics, watch my presentation from Databricks: Data + AI Summit Europe.What is the medallion lakehouse architecture? See the presentation by Zurich North America at Spark+AI Summit 2020 to see how one of the largest providers of insurance solutions and services in the world implements a scalable self-service data science ecosystem with Databricks and Alation. Together, Alation and Databricks enable enterprises to empower more people to make data-driven decisions on trust data and drive data culture. No matter where the data resides it is easy to determine what data should move and what data should stay, and giving data consumers a consistent experience through the migration and beyond. The unified view that Alation provides can also help enterprises that are looking to move workloads to the Databricks Lakehouse. Data Governance Across the Databricks LakehouseĪlation provides an interface to guide users to the appropriate data consumption within the Lakehouse and across their other data sources - making data governance a part of the day-to-day activities of data consumers. With Alation, data consumers can easily find these assets and the colleagues who have created them and ask questions of one another - enabling people to build off the work of others and driving collaboration. While more people are finding and leveraging data in the Databricks Lakehouse with Alation, Alation goes a step further, giving them the tools to seamlessly collaborate with one another. Analysts can now use Alation to query the entire data set on Delta Lake via SQL with better performance with SQL Analytics endpoints that auto-scale seamlessly. With the release of SQL Analytics, Alation widens support of the Lakehouse paradigm making it easier for more people - from data analysts and data scientists to business users - to find data, build off of the work of others, and gain a unified view of data - no matter where it resides. Collaboration on the Databricks Lakehouse As data consumers query relevant data in Databricks Lakehouse, Alation surfaces recommendations to related data assets, projections, policies, and guidelines to ensure that anyone can find, understand, and use data correctly. Now Alation takes advantage of the new SQL Analytics high-performance Photon engine to make running queries even faster. Query Data FasterĪlation enables anyone to easily query data within Lakehouse with Alation Compose. Finally, Alation makes it easy to embed data governance into activities of data consumers, enabling enterprises to implement data governance across the Databricks Lakehouse. Alation also makes collaboration on data seamless, helping everyone from business users to data scientists to work together. Now, Alation takes advantage of the performance boost provided by Databricks SQL Analytics to make running queries even faster. ![]() Built on Delta Lake, SQL Analytics allows analysts, data scientists, and engineers to build dashboards and reports on their data lake and easily share those within their organization, without the need for separate BI tools.Īlation already made it easy for anyone to quickly and easily find data within the Databricks Lakehouse. Today we are excited to announce our support for SQL Analytics, the latest innovation from Databricks, and deepened support for Lakehouse, the Databricks architecture paradigm that combines the best of data lakes and data warehouses. ![]()
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