Understanding Lakehouse Architecture
A lakehouse is a data platform that merges the best aspects of data warehouses and data lakes into one data management solution. It’s designed to house both structured and unstructured data, enabling machine learning, business intelligence, and real-time analytics.
Key features of a lakehouse architecture include:
- Integration and Storage: It provides a unified platform for integrating, storing, and processing both structured and unstructured data.
- Scalability: Lakehouses are highly scalable, offering cost-efficient data storage and processing capabilities.
- Separation of Storage and Compute: Similar to some cloud data warehouses, the lakehouse architecture uses separate clusters for storage and computing, ensuring cost-efficiency and performance.
- Data Governance and Sharing: Lakehouses also provide robust mechanisms for data governance and sharing.
Microsoft Products for Building a Lakehouse
Microsoft offers several products that can be utilized to build a lakehouse architecture:
Azure Synapse Analytics
This integrated analytics service can be used to explore, clean, and transform data before storing it in your lakehouse. It also enables real-time analytics on big data directly from the Azure Data Lake Storage.
Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. It can handle massive amounts of data and integrates well with various data stores, making it ideal for building a lakehouse.
Azure Data Lake Storage
This scalable and secure data lake performance allows you to store and analyze large amounts of data of any kind, size, and ingestion speed in one single place.
Azure Purview is a unified data governance service that helps you manage and govern your on-premises, multi-cloud, and software-as-a-service (SaaS) data. It helps enforce data governance policies in your lakehouse.
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