Databricks is a unified data analytics platform founded by the creators of Apache Spark. It provides an open, scalable lakehouse architecture that combines data engineering, machine learning, and analytics to streamline data workflows and enable collaborative data science.
The Critical Role of Data Engineering
A recent MIT report highlighted that 88% of organisations are investing in or experimenting with generative AI (GenAI), and 71% plan to build their own models. This trend underscores a crucial reality: even the most sophisticated AI models are futile without high-quality data. Therefore, reliable data pipelines that efficiently ingest, process, and ensure data quality are fundamental to AI initiatives’ success.
The Databricks Data Intelligence Platform
Databricks’ mission is to democratise data and AI, enabling organisations to transform their unique data into valuable insights. The platform is built on a lakehouse architecture, combining the best features of data lakes and data warehouses to offer a unified, open foundation for all data and governance needs. This architecture is pivotal in supporting various workloads, from business intelligence and data warehousing to advanced AI and data science applications.
Key Components and Features
Delta Lake: This open-source storage format ensures data reliability and performance by addressing challenges related to data quality, compliance, and data modifications. It integrates seamlessly with other systems, allowing users to read Delta tables with Hudi and Iceberg clients, thereby maintaining control over their data.
Unity Catalog: This feature provides a comprehensive data catalogue, centralising permissions management, auditing, and data lineage tracking across the entire organisation. It supports secure data sharing across platforms and regions, enhancing governance and compliance.
DatabricksIQ: At the core of the platform, DatabricksIQ leverages AI to enhance all aspects of the data environment. It creates highly specialised AI models by analysing signals across the Databricks ecosystem, including Unity Catalog, dashboards, notebooks, and data pipelines.
Delta Live Tables (DLT): DLT simplifies ETL processes for both streaming and batch data by automating task orchestration, cluster management, monitoring, and error handling. It allows engineers to apply software engineering best practices like testing and documentation to data pipelines.
Databricks Workflows: This orchestration solution enables the definition of multi-step workflows for various purposes, such as ETL pipelines and ML training. It offers advanced observability, serverless compute options, and enhanced control flow capabilities, making it an essential tool for data engineers.