Data Lakehouse using Azure

A "data lakehouse" is a modern data management architecture that combines elements of both data lakes and data warehouses. It provides the scalable storage capabilities of a data lake and the schema and performance management features of a data warehouse. When using Azure, there are several services you can leverage to build a data lakehouse.

Here are the key components for building a data lakehouse on Azure:

• Azure Data Lake Storage (ADLS):

  • ADLS Gen2 provides a highly scalable and cost-effective data storage solution that supports the data lake foundation. It is built on top of Azure Blob Storage and enhances performance, management, and security features necessary for analytics workloads.

• Azure Databricks:

  • Azure Databricks is an analytics platform optimized for the Microsoft Azure cloud services platform. It provides collaborative notebooks, data engineering tools, and a Spark-based analytics platform which makes it a core component for processing and transforming large volumes of data within a lakehouse architecture.

• Azure Synapse Analytics:

  • This service integrates big data and data warehousing into a single service, making it easier to query data across your entire data landscape. It allows you to run big data analytics directly on your data lake storage, blend it with your data warehouse, and handle both analytics and transactional data.

• Delta Lake:

  • Delta Lake is an open-source storage layer that brings reliability to data lakes. It provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. In an Azure-based lakehouse, Delta Lake runs on top of ADLS and can be seamlessly integrated with Azure Databricks and Synapse Analytics.

• Power BI:

  • For data visualization and business intelligence, Power BI can be connected directly to Azure Synapse Analytics, Azure Databricks, or directly to ADLS to create rich visualizations and reports from the data stored in the lakehouse.

• Azure Purview:

  • Azure Purview is a unified data governance service that helps manage and govern your on-premises, multi-cloud, and software-as-a-service (SaaS) data. It can be a crucial component for managing data governance and cataloging in a lakehouse architecture.

Implementing a data lakehouse on Azure involves integrating these components into a cohesive architecture that meets your specific data, analytical, and business requirements. This setup allows for scalable storage, powerful data processing, and the ability to handle both unstructured and structured data efficiently.

Image reference: https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/building-the-lakehouse-implementing-a-data-lake-strategy-with/ba-p/3612291