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Product Information on Databricks Data Intelligence Platform
What is Databricks Data Intelligence Platform?
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Overall experience with Databricks Data Intelligence Platform
“Reliable Data Engineering Gains Balanced By Platform Complexity and Operational Demands”
“Databricks Platform Facilitates Workflow But Notebook Performance Remains Slow”
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Company Description
Databricks is a global company focusing on data and AI. At the core of Databricks is the Databricks Data Intelligence Platform which allows entire organizations to use data and AI to power a wide range of business use cases. It's built on a lakehouse to provide an open, unified foundation for all data and governance and is powered by a Data Intelligence Engine that understands the uniqueness of the organizations’ data. Databricks simplifies and accelerates enterprises' data and AI goals by unifying data, analytics and AI on one platform. Its key mission is to assist data teams in addressing some of the world's most challenging problems.
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Reviewer Insights for: Databricks Data Intelligence Platform
Performance of Databricks Data Intelligence Platform Across Market Features
Databricks Data Intelligence Platform Likes & Dislikes
unified platform with a single workflow. Strong performance for large scale processing, plus flexibility in compute choices. Collaborative development experience. Reliable lakehouse patterns. Scales well.
- Easy access to data tables (both writing and reading) - Ease of setting up scheduled jobs - Relatively easy to use interface
Databricks delivers a unified lakehouse platform that blends data lake flexibility with warehouse performance, enabling end-to-end pipelines from ingestion to AI-driven analytics. Built on open source foundations, it offers advanced features like delta live tables, Mosaic AI and seamless multi-cloud integrations - all secured by robust governance through unity catalog.
1. Cost management requires maturity: spending can rise quickly without guardrails. 2. Governance complexity at scale: possibility of inconsistent structures and duplicated datasets. 3. Operational overhead for production quality: Strong platform engineering practices needed.
The Databricks notebooks are very slow.
Steep learning curve for beginners, complex spark tuning, on-boarding challenges. High infrastructure costs at scale, UI may overwhelm non-technical users, and support for non-spark workloads and serverless features is still maturing.
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Peer Discussions
Databricks Data Intelligence Platform Reviews and Ratings
- BUSINESS ANALYST50M-1B USDIT ServicesReview Source
Reliable Data Engineering Gains Balanced By Platform Complexity and Operational Demands
Overall, the Databricks data intelligence platform has been a strong foundation for unifying data engineering, analytics, and ML in one place. It helped us move faster from raw data to reliable, shareable datasets and production ready pipelines. Collaboration improves because teams work in a consistent environment with shared governance and reusable patterns. Main tradeoff is that you get a lot of power and flexibility, but it requires clear standards and active platform ownership to avoid sprawl. - DBA AND ARCHITECT10B+ USDHealthcare and BiotechReview Source
Databricks Provides Unified Lakehouse Architecture With Advanced AI and Governance Tools
Databricks data intelligence platform excels as a unified, enterprise-grade lake house solution with advanced generative AI capabilities, robust governance and scalable performance. It's ideal for organizations ready to invest in a powerful integrated data analytics and AI platform. - ENGINEERGov't/PS/EdEducationReview Source
Natural Language To SQL Enhances Exploration While Governance Learning Curve Persists
it worked really well as a unified platform for data engineering and analytics, and support strong for semantic intelligence. Also collaboration on Databricks is smooth - DATA AND ANALYTICS - SR. ANALYST1B-10B USDConsumer GoodsReview Source
A unified platform with a powerful scalable analytics across the enterprise.
This platform has become a core part of our ecosystem, simplifying the way to integrate, prepare and analyze large-scale datasets across business domains. The flexibility and performance of the lakehouse architecture are excellent, though governance and cost optimization require thoughtful setup to get the best results. Its ability to unify data engineering, analytics and machine learning within a single environment has driven faster innovation and collaboration between data scientists and business analysts. - IA ENGINEERING MANAGER10B+ USDEnergy and UtilitiesReview Source
Databricks Simplifies Enterprise Data but Hybrid Architecture may adds Integration Complexity
Databricks is a very good product to make enterprise data understandable and usable by Humans and AI systems.