End-to-end platform solution for processing large amounts of data for data engineering & data science based on a further development of Apache Spark technology.
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Databricks, founded in 2013 and headquartered in San Francisco, focuses on simplifying and democratizing data and AI solutions. With offices worldwide, the company delivers a data platform designed to engage data and AI teams to solve complex enterprise challenges.
The vendor’s Data Intelligence Platform is built on an open lakehouse architecture. The platform unifies data and governance, integrating AI models tailored to each organization. It allows users in an organization to access data insights through automation and natural language, while technical teams can easily build and deploy secure data apps and data products.
Databricks is built on top of key technologies like Delta Lake, Apache Spark and MLflow. These open-source innovations allow for efficient and scalable data management. The platform uses Delta Sharing, an open protocol for secure data sharing across applications, minimizing data replication and enhancing data governance through centralized solutions for auditing and tracking data usage.
Through its Mosaic product, Databricks simplifies AI deployment, governance and evaluation. With integrated tools such as Vector Search, RAG (retrieval augmented generation), model serving and training features, it supports comprehensive AI workflows.
The platform supports real-time data processing via Delta Live Tables, workflows, data streaming and Unity Catalog. These features make it adaptable for various data-intensive applications.
Databricks has cultivated a strong partner ecosystem, with over 1,200 global partnerships, including cloud providers, ISVs and consulting firms.
BARC’s viewpoint on the product’s strengths and challenges.
The information contained in this section is based on user feedback and actual experience with Databricks Data Intelligence Platform.
The information and figures are largely drawn from BARC’s The BI & Analytics Survey, The Planning Survey, The Financial Consolidation Survey and The Data Management Survey. You can find out more about these surveys by clicking on the relevant links.
All key figures for Databricks Data Intelligence Platform at a glance.
The Spark SQL Engines makes it easy for SQL Developers to work with the platform.
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It's a powerful ecosystem for data & analytics.
Flexibilität in der Programmiersprache. Trennung von Storage und Compute.
Komplexität insbesondere in Bezug auf den Unity Catalogue.
Es braucht ein übergreifendes Konzept für die Verwendung (z.B. Data Lakehouse, Medaillon Architecture, etc.).
Zum Einsatz als zentrale Datenmanagement Plattform geeignet.
Unified data platform: Databricks can handle both structured and unstructured data, which is crucial in healthcare where patient information comes in various formats and this simplifies data management.
Data integration: While Databricks can handle various data types, integrating data from disparate healthcare systems can still be complex and require additional tools or expertise.
Ensure you have the resources appropriately trained to leverage its capabilities and understand aligning your use cases to the strengths of the platform.
The alignment with Microsoft Azure is second to none and this is the heart of my client's cloud architecture. This makes Databricks a critical platform and Databricks continues to deliver on needed features and functions.
Great, performant, stable tool. We switched over from Cloudera's Hive/Impala and it was a huge improvement.
The price. It is expensive and we need to keep a close eye on cloud costs.
Small POCs, validate its true cost, invest time in optimizing data-models and/or queries. Every cent spent in query optimization saves $$ on the medium long term.
Expensive but love it.
Einfachheit der Administration.
Integration mit anderen Services ist aufwendig.
Es gibt einen Unterschied zwischen Marketing und der sinnvollen Nutzung.
Die Nutzung von neuen Features vorsichtig einführen.
