Azure Data Lake Reference Architecture

A big data architecture is designed to handle the ingestion processing and analysis of data that is too large or complex for traditional database systems.

Azure data lake reference architecture.

Azure data lake analytics is the latest microsoft data lake offering. When to use a data lake. 2 leverage data in azure blob storage to perform scalable analytics with azure databricks and achieve cleansed and transformed data. Microservices architecture on azure service fabric.

This kind of store is often called a data lake. Data for batch processing operations is typically stored in a distributed file store that can hold high volumes of large files in various formats. Data lake processing involves one or more processing engines built with these goals in mind and can operate on data stored in a data lake at scale. Use this reference architecture to see microservices deployed to azure service fabric.

3 cleansed and transformed data can be moved to azure synapse analytics to combine with existing structured data. Big data solutions typically involve a large amount of non relational data such as key value data json documents or time series data. Azure data lake storage massively scalable secure data lake functionality built on azure blob storage. It removes the complexities of ingesting and storing all of your data while making it faster to get up and.

Still part of the azure data factory pipeline use azure data lake store gen 2 to save the original data copied from the semi structured data source. Typical uses for a data lake. Azure netapp files enterprise grade azure file shares powered by netapp. Options for implementing this storage include azure data lake store or blob containers in azure storage.

1 combine all your structured unstructured and semi structured data logs files and media using azure data factory to azure blob storage. Azure data lake includes all the capabilities required to make it easy for developers data scientists and analysts to store data of any size shape and speed and do all types of processing and analytics across platforms and languages. These storage services are exposed to databricks users via dbfs to provide caching and optimized analysis over existing data. The data may be processed in batch or in real time.

This cluster configuration can be a starting point for most deployments. It is an in depth data analytics tool for users to write business logic for data processing. File storage file shares that use the standard smb 3 0 protocol. Azure data factory mapping data flows or azure databricks notebooks can now be used to process the semi structured data and apply the necessary transformations before data can be used for reporting.

Azure storage and azure data lake integration. The most important feature of data lake analytics is its ability to process unstructured data by applying schema on reading logic which imposes a structure on the data as you.