What Is Azure Databricks?

Azure Databricks is a fully-managed, cloud-based Big Data and Machine Learning platform, which empowers developers to accelerate AI and innovation by simplifying the process of building enterprise-grade production data applications. Built as a joint effort by the team that started Apache Spark and Microsoft, Azure Databricks provides data science and engineering teams with a single platform for Big Data processing and Machine Learning.

By combining the power of Databricks, an end-to-end, managed Apache Spark platform optimized for the cloud, with the enterprise-scale and security of Microsoft's Azure platform, Azure Databricks makes it simple to run large-scale Spark workloads.

Optimized environment

To address the problems seen on other Big Data platforms, Azure Databricks was optimized from the ground up, with a focus on performance and cost-efficiency in the cloud. The Databricks Runtime adds several key capabilities to Apache Spark workloads which will increase performance and reduce costs by the maximum amount as 10-100x when running on Azure, including:

• High-speed connectors to Azure storage services, like Azure Blob Store and Azure Data Lake
• Auto-scaling and auto-termination of Spark clusters to minimize costs
• Caching
• Indexing
• Advanced query optimization

By providing an optimized, easy to provision and configure the environment, Azure Databricks gives developers a performant, cost-effective platform that enables them to spend more time building applications, and less time focused on managing clusters and infrastructure.

Who is Databricks?

Databricks was founded by the creators of Apache Spark, Delta Lake, and MLflow.

Over 2000 global companies use the Databricks platform across big data & machine learning lifecycle.

Databricks Vision: Accelerate innovation by unifying data science, data engineering and business.

Databricks Solution: Big Data Analytics Platform

What does Databricks offer that is not Open-Source Spark?

• Databricks Workspace - Interactive Data Science & Collaboration
• Databricks Workflows - Production Jobs & Workflow Automation
• Databricks Runtime
• Databricks I/O (DBIO) - Optimized Data Access Layer
• Databricks Serverless - Fully Managed Auto-Tuning Platform
• Databricks Enterprise Security (DBES) - End-To-End Security & Compliance

What is Apache Spark?

Spark may be a unified processing engine which will analyze big data using SQL, machine learning, graph processing, or real-time stream analysis:

• At its core is the Spark Engine.
• The DataFrames API provides an abstraction above RDDs while simultaneously improving performance 5-20x over traditional RDDs with its Catalyst Optimizer.
• Spark ML provides high quality and finely tuned machine learning algorithms for processing big data.
• The Graph processing API gives us an easily approachable API for modeling pairwise relationships between people, objects, or nodes in a network.
• The Streaming APIs give us End-to-End Fault Tolerance, with Exactly-Once semantics, and the possibility for sub-millisecond latency.

And it all works together seamlessly!

Azure Databricks

As a compute engine, Azure Databricks sits at the center of your Azure-based software platform and provides native integration with Azure Active Directory (Azure AD) and other Azure services.

Scala, Python, Java, R & SQL

• Besides being able to run in many environments, Apache Spark makes the platform even more approachable by supporting multiple languages:
  • Scala - Apache Spark's primary language
  • Python - More commonly referred to as PySpark
  • R - SparkR (R on Spark)
  • Java
  • SQL - Closer to ANSI SQL 2003 compliance
    • Now running all 99 TPC-DS queries
    • New standards-compliant parser (with good error messages!)
    • Subqueries (correlated & uncorrelated)
    • Approximate aggregate stats
• With the DataFrames API, the performance differences between languages are nearly nonexistent (especially for Scala, Java & Python).