Tech

Databricks brings BI and SQL to the data lake

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Databricks announces SQL Analytics, the missing component in its so-called Lakehouse architecture, which is to combine the individual advantages of data lakes and data warehouses. Central elements in the Lakehouse paradigm are Databricks’ Delta Lakes, an open format that provides patterns for data quality, processing speed and structured transactions (Atomicity, Consistency, Isolation, Durability – ACID), and the Delta Engine, which was presented in the middle of the year, which queries the curated Aims to accelerate data lakes. SQL Analytics builds on both components.

While companies prefer to use the largely unstructured data in data lakes for machine learning and data science applications, classic business intelligence tools and SQL require structured, high-performance data storage in the data warehouse – usually combined with higher levels Costs. With the aim of being able to offer companies an attractive price-performance ratio for all data applications, Databricks’ Lakehouse architecture for data analysis in the cloud bridges the gap between the traditional BI world and current ML applications.

Databricks’ approach competes with cloud data warehouses such as those offered by AWS, Google and Microsoft. With separate data processing (compute) and data storage (storage), the cloud data warehouses promise faster query results even with larger amounts of data. With the Delta Lakes and the Delta Engine, Databricks is now laying a common access level over the data storage, which is intended to give companies more flexibility.

SQL Analytics has its own interface for queries and the visualization of results that can be configured individually. Using native connectors, SQL Analytics also creates a direct connection to widely used BI tools such as Tableau, Qlik and Microsoft Power BI, so that users can integrate the new service into existing BI workflows.

Databricks is now making the new service available to customers as part of its Unified Data Analytics Platform. Further information on SQL Analytics can be found on the provider’s homepage.


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