Databricks Architecture


            Databricks Architecture

Here’s a breakdown of Databricks architecture, including core concepts and components:

The Lakehouse Paradigm

The Databricks Lakehouse Platform unifies the best aspects of data lakes and data warehouses into a single platform:

  • Data Lake Foundation leverages the flexibility and scalability of cloud storage (like AWS S3, Azure Blob Storage, and Google Cloud Storage) to store structured, semi-structured, and unstructured data.
  • Data Warehouse Capabilities: Ensures data reliability, quality, performance optimizations, and ACID transactions through technologies like Delta Lake.

Key Components

  1. Control Plane
    • Managed by Databricks.
      • Components: Web Application: The interface for managing Databricks.
      • Notebooks: Collaborative coding environments for Python, Scala, SQL, and R.
      • Job Scheduler: Automates the execution of data pipelines and workflows.
      • REST APIs: Enables programmatic interaction with the platform.
      • Metastore:  A managed Hive Metastore for storing table metadata.
  2. Data Plane
    • You deployed within your cloud account (AWS, Azure, GCP).
      • Components:Clusters: Groups of compute nodes (virtual machines) managed by Databricks. You choose the appropriate cluster configuration based on your workload.
      • Apache Spark: The core distributed processing engine.
      • Delta Lake: An open-source storage layer that brings ACID transactions, schema enforcement, versioning, and optimization to your data lake.
      • Photon: Databricks’ optimized, vectorized query engine built on top of Apache Spark, providing even faster performance.

Data Flow

  1. Data Ingestion: Databricks integrates with various data sources (databases, streaming sources, cloud storage, etc.) and loads data into the data lake (cloud storage).
  2. Data Transformation and Processing:
    • ETL/ELT Pipelines: To create reliable data pipelines, you can use Spark or Delta Live Tables (DLT).
    • Data is prepared & transformed: Structured into Delta Lake tables.
  3. Data Analytics & Exploration:
    • SQL Workspaces: Enable traditional SQL analytics.
    • Notebooks: Support data exploration and analysis in multiple languages.
  4. Machine Learning:
    • Databricks ML Runtime: Provides optimized libraries for machine learning.
    • Feature Store:  Centralized feature management.
    • MLflow: Manages the end-to-end machine learning lifecycle.

Security and Governance

  • Unity Catalog: A unified governance layer that manages metadata, permissions, and access control across the lakehouse.
  • Integration with Cloud Security Tools: Databricks integrates with your cloud provider’s security and compliance features (IAM, encryption, etc.).

Advantages of Databricks Architecture

  • Simplicity: A unified platform for data engineering, analytics, and machine learning.
  • Performance:  Delta Lake and Photon optimize batch and streaming workloads.
  • Scalability: Leverages the elasticity of cloud providers.
  • Reliability: Delta Lake ensures data consistency and integrity.
  • Openness:  Based on open-source technologies (Spark, Delta Lake) and supports diverse languages.

Databricks Training Demo Day 1 Video:

You can find more information about Databricks Training in this Dtabricks Docs Link



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