Databricks

Databricks Overview

Summary

Databricks is a cloud-based platform for big data processing built on Apache Spark. It provides an integrated workspace for collaboration among data engineers, data scientists, and analysts. Databricks on Azure simplifies Spark deployment by offering auto-scaling clusters, real-time analytics, and integration with various Azure services, such as Azure Data Lake for large-scale data storage.

Cloud Platform Compatibility:

• Supports the big three cloud providers (AWS, Azure, GCP).
• Integration with Other Technologies:
  • Combines capabilities of:
    • Apache Spark
    • Delta Lake
    • MLflow
• Data Lakehouse Architecture:
  • Represents a combination of a data warehouse and a data lake.

Core Components:

1. Tables:
   • Represents files and data sources.
2. Clusters:
   • Provides computing power for data processing.
3. Notebooks:
   • Similar to Jupyter notebooks; support multiple programming languages and allow for productionization of code.
4. Workspaces:
   • Collaborative environments for teams to work together.

Scalability

• Leverages the scalability of Hadoop while integrating advanced features for big data processing.

Difference between Databricks vs. Snowflake