Data Archive

Databricks

Databricks is a cloud-based data analytics and engineering platform built on top of Apache Spark. It provides a workspace for Data Engineers, data scientists, and analysts to collaborate on Big Data and machine learning projects.

Databricks simplifies large-scale data processing and enables real-time analytics by combining compute scalability, Delta Lake reliability, and integrated ML tooling.

Platform Integration

  • Cloud Compatibility: Supports all major providers - AWS, Azure, and GCP.
  • Technology Stack: Combines the functionality of:
    • Apache Spark for distributed data processing
    • Delta Lake for ACID-compliant storage and versioning
    • MLflow for machine learning lifecycle management
  • Data Lakehouse Architecture: Unifies data warehouse performance with data lake scalability.

Core Components

  1. Clusters: Provide the distributed compute power for running Spark jobs.
  2. Workspaces: Shared environments where teams collaborate using notebooks, libraries, and jobs.
  3. Notebooks: Interactive development interfaces supporting Python, SQL, R, and Scala. They can be scheduled as jobs for production workflows.
  4. Catalogs, Schemas, and Tables in Databricks: Hierarchical namespaces used to organize and govern data.
  5. Delta Table: Core storage abstraction providing ACID transactions, schema evolution, and time travel capabilities.

Scalability and Reliability

Databricks inherits scalability from the Hadoop ecosystem but offers significant improvements through:

  • Elastic clusters that auto-scale resources based on workload demand.
  • Fault tolerance for resilient job execution.
  • Delta Lake for consistent and recoverable data storage.

Typical Workflow

  1. Data Ingestion: Connect to external sources (e.g., APIs, databases, or Google Sheets).
  2. Transformation: Clean and prepare data using Spark DataFrames.
  3. Storage: Persist processed data into managed Delta tables.
  4. Analysis & Visualization: Query with SQL or connect to BI tools for reporting.
  5. Productionization: Convert notebooks into automated jobs for repeatable workflows.

Databricks

Databricks is a data platform built on Apache Spark, designed for:

  • Large-scale data processing (batch + streaming)
  • Data engineering, machine learning, and analytics
  • Managing data in a Lakehouse architecture (unifying data lakes and warehouses)
  • Storing data in Delta tables within a catalog (Unity Catalog)

It’s both a processing engine and a collaborative workspace for Python, SQL, R, and Scala.

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