What is Microsoft Azure Data Lake?

What is Microsoft Azure Data Lake?

Azure Data Lake is a cloud-based data repository service from Microsoft that enables organizations to store large amounts of multiple types of data and perform data processing and analysis across multiple platforms and programming languages. . This data lake is scalable, secure and supports massively parallel analytics — all of which enable enterprise teams to unlock more insights from their unstructured, semistructured and structured data.

Azure Data Lake explained

Azure Data Lake is a centralized cloud repository that can store large amounts of data in its original format. There is no need to convert unstructured or semi-structured data into a structured format to run different types of analytics or to power intelligent actions.

Organizations can store data at any size and speed in Azure Data Lake. They can process and analyze data as needed across many different platforms. They can also run both data transformation and processing programs on petabytes of data in many different programming languages, including U-SQL, R, Python and .NET. Because Azure Data Lake runs in the cloud, users don’t need to manage any hardware or software installations or upgrades.

In addition to its data storage and analysis capabilities, Azure Data Lake incorporates advanced features to simplify data management, administration and security. An organization can integrate it with existing operational stores and data warehouses to extend existing data applications. The Azure Data Lake analytics service is available on a pay-per-job basis.

Azure Data Lake is built on Yet Another Resource Negotiator technology and the open Hadoop Distributed File System standard. These architectural options enable business users — including developers, data scientists and analysts — to run massively parallel analytics on massive amounts of data. The service supports batch, interactive and streaming analytics and eliminates the complexities of data ingestion, conversion, storage, security and management common to on-premises data storage and analytics systems.

Example of a data lake architecture diagram.
This is a sample architecture diagram for a data lake that supports advanced analytics.

What can organizations do with Azure Data Lake?

Any organization can use Microsoft Azure Data Lake to store data and perform batch, streaming and interactive analytics on it. Azure Data Lake works with data of any size, including petabyte-sized files and trillions of objects.

Azure Data Lake is also suitable for other data-related activities, such as the following:

  • Debugging and optimizing big data programs.
  • Develop and run multiple parallel programs for data transformation and processing in different languages.
  • Protect data assets with enterprise-grade security and extend on-premises security and cloud management controls.
  • Encrypt sensitive data and protect it from unauthorized and malicious use with SSL (for data in motion) and service or backed by user-manged hardware security module (HSM) keys in Azure Key Vault (for at rest data).
  • Enable role-based access controls (RBAC) to authorize users and groups with refined POSIX-based access control lists (ACLs).
  • Auditing access or configuring system changes to maintain security and regulatory compliance.

Key features of Azure Data Lake

Azure Data Lake includes three components that enable teams to create data lakes for their specific data analytics needs and use cases. These components are Azure HDInsight, Azure Data Lake Analytics and Azure Data Lake Storage.

1. Azure HDInsight

Azure HDInsight is a fully managed Cloud Hadoop offering backed by a 99.9% service level agreement. This open source analytics platform and enterprise-grade service enables organizations to manage big data needs and provide cloud Hadoop, Spark and HBase clusters. HDInsight provides analytics clusters and optimized components for Apache Hadoop, Spark, Hive, MapReduce, HBase, Storm, Kafka and R Server, so users can process large amounts of any type of data in the cloud.

HDInsight doesn’t require users to install hardware or manage infrastructure to quickly spin up open source projects and clusters. Teams can deploy all big data technology and ISV applications as managed clusters and then secure and monitor them to protect data. After building a data lake, teams can integrate it with any number of Azure data storage tools and services, including Azure Synapse Analytics, Azure Cosmos DB and Azure Data Lake Storage.

2. Azure Data Lake Analytics

Azure Data Lake Analytics is a distributed analytics service to develop and run both transformation and processing programs on big data. Data Lake Analytics supports data transformation and processing programs in U-SQL, R, Python and .NET. U-SQL is particularly useful because it is a simple, expressive and extensible language that simplifies processing for various workload categories, including querying, machine learning, ETL and analytics.

Like HDInsight, Data Lake Analytics is a cloud-based service, which means business teams don’t need to manage or tune any infrastructure, such as servers, virtual machines or clusters. However, they can process data on demand within the cloud in seconds. They can also quickly measure the processing power required for the job (measured in Azure Data Lake Analytics Units or AUs).

Data Lake Analytics charges organizations per job, which simplifies pricing and enables better control of cloud analytics costs. The service includes an implementation environment that provides recommendations to improve the performance of big data programs, helping organizations reduce costs by up to 95%. Virtualizing analytics — moving processing close to the source data without moving the data — also improves performance and reduces costs.

3. Storage in Azure Data Lake

Azure Data Lake Storage is a secure data lake that enables organizations to build a scalable foundation for their analytics needs. This single storage platform for ingestion, processing and visualization eliminates data silos and simplifies data analytics. It also supports the most common analytics frameworks and high-performance analytics workloads while ensuring consistent performance regardless of analytics query size.

Data Lake Storage offers unlimited scale and automatic geo-replication for 16 9s of data durability. It provides features such as tiered storage and policy management to optimize costs, Azure Active Directory (Azure AD) and RBAC to authenticate users and data as well as data encryption, network level control and advanced threat protection.

Benefits of Azure Data Lake

As a “no-limits” data lake, Azure Data Lake allows organizations to store and analyze any type of data at any time, at any scale and in a cost-effective manner. . The service makes it easy to analyze petabyte-sized files and trillions of objects across platforms and languages, and derive useful insights that support operations and business decision-making. Teams can do all of this in one place, without artificial constraints and without having to worry about how to process and store large data sets.

The service simplifies the administration and management of data because it works with existing tools for identity, management and security, as well as operational stores and data warehouses. Companies can leverage their existing tech stack and data applications and enhance them with new data storage and analysis capabilities.

Azure Data Lake powers intelligent action from big data, provides optimized analytics clusters for multiple open source frameworks and runs massively parallel analytics on unstructured, semistructured and structured data. It also provides enterprise-level security and auditing, as well as 24/7 support to protect data assets and mitigate challenges. Microsoft monitors every Azure Data Lake deployment to guarantee that it is continuously running with the strongest security controls and cloud management.

Screenshot of Microsoft Power BI.
Microsoft Azure Data Lake seamlessly integrates applications such as Azure Synapse Analytics, Data Factory and Power BI.

Data Lake integrates seamlessly with Visual Studio, Eclipse and IntelliJ, so business teams can easily run, debug and tune their big data queries. They can visualize jobs to see how code runs and to identify performance and cost bottlenecks. The service also works with Azure Synapse Analytics, Power BI and Data Factory, which makes it easier for users to prepare data, perform interactive analytics on large data sets and reduce data latency.

Read about who manages data lakes and what skills are needed. Explore the 7 steps to a successful data lake implementation and how to build a robust data analytics platform architecture. Explore the 5 principles of a well-designed data architecture.

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