Azure Data Lake Storage
Massively scalable and secure data lake for your high-performance analytics workloads.
Build a foundation for your analytics
Limitless scale and 16 9s of data durability with automatic geo-replication.
Highly secure with flexible mechanisms for protection across data access, encryption, and network-level control.
Single storage platform for ingestion, processing, and visualization that supports the most common analytics frameworks.
Cost optimization via independent scaling of storage and compute, lifecycle policy management, and object-level tiering
Scale to match your most demanding analytics workloads
Utilize flexible security mechanisms
Build a scalable foundation for your analytics
Build cost-effective data lakes
Comprehensive security and compliance, built in
Microsoft invests more than USD$1 billion annually on cybersecurity research and development.
We employ more than 3,500 security experts who are dedicated to data security and privacy.
Flexible pricing for building data lakes
Choose from pricing options including tiering, reservations, and lifecycle management.
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Trusted by companies of all sizes
Rockwell Automation expands business insights
Industrial automation firm Rockwell Automation created a unified platform for its analytical needs to optimize storage, compute, and scalability, reducing its total cost of ownership.
"With Azure, we now have the capability to rapidly drive value from our data. The actionable insights from the data models we're creating will help us increase revenue, reduce costs, and minimize risk."
Ahmed Adnani, Director of Applications and Analytics, Smiths Group
"Microsoft Azure gives us good value when we need huge clusters for a couple of days to do a job, then lets us get rid of them to conserve, whereas the datacenter is almost completely unfeasible. That was a big, big game-changer for us."
James Ferguson, Product Manager, Marks & Spencer
"Operating on Azure, we have opportunities to improve of the speed, range, accuracy, and localization of our forecasts—it's just a question of what to prioritize first."
Brad Beechler, Senior Machine Learning Scientist
Frequently asked questions about App Center
Adding the Hierarchical Namespace on top of blobs allows the cost benefits of cloud storage to be retained, without compromising the file system interfaces that big data analytics frameworks were designed for.
A simple example is a frequently occurring pattern of an analytics job writing output data to a temporary directory and then renaming that directory to the final name during the commit phase. In an object store (which, by design doesn’t support the notion of directories), these renames can be lengthy operations involving N copy and delete operations, wherein N is the number of files in the directory. With the Hierarchical Namespace, these directory manipulation operations are atomic, improving performance and cost. Additionally, supporting directories as elements of the file system permits the application of POSIX-compliant access control lists (ACLs) that use parent directories to propagate permissions.
Data Lake Storage is primarily designed to work with Hadoop and all frameworks that use the Hadoop FileSystem as their data access layer (for example, Spark and Presto). See details.
In Azure, Data Lake Storage integrates with:
- Azure Data Factory
- Azure HDInsight
- Azure Databricks
- Azure Synapse Analytics
- Power BI
The service is also integrated into a massive and mature ecosystem around Azure Blob Storage.
Data Lake Storage provides multiple mechanisms for data access control. By offering the Hierarchical Namespace, the service is the only cloud analytics store that features POSIX-compliant access control lists (ACLs) that form the basis for Hadoop Distributed File System (HDFS) permissions. Data Lake Storage also includes capabilities for transport-level security via storage firewalls, private endpoints, TLS 1.2 enforcement, and encryption at rest using system or customer supplied keys.