Databricks | FutureHurry
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Main Purpose

The main purpose of Databricks is to provide a data lakehouse architecture, which combines the benefits of data lakes and data warehouses, for integration, storage, processing, governance, sharing, analytics, and AI.

Key Features

  • Integration, storage, processing, governance, sharing, analytics, and AI in one architecture.
  • Support for structured and unstructured data.
  • End-to-end view of data lineage and provenance.
  • Toolset for Python and SQL, notebooks and IDEs, batch and streaming, and all major cloud providers.

Use Case

  • Scalable storage and processing capabilities for modern organizations.
  • Establishing a single source of truth.
  • Eliminating redundant costs.
  • Ensuring data freshness.
  • Real-time data processing.
  • Data integration and unification.
  • Schema evolution without disrupting existing data pipelines.
  • Data transformations with speed, scalability, and reliability.
  • Data analysis, reporting, and complex analytical queries.
  • Machine learning and AI on all data.
  • Data versioning, lineage, and governance.
  • Data sharing and collaboration across teams.
  • Operational analytics and monitoring.
Pricing Model:

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