Here’s everything you need to know about Azure Data Lake
Data is the New Oil. It is the backbone of the information economy and one of the most valuable assets for businesses and enterprises. With the introduction of internet-connected products and IoT devices, we now receive large volumes of data from even the most mundane appliances. All this data needs to be stored somewhere for further analysis and use.
This is where Data Lakes come in handy. In the simplest terms, a data lake is a central storage repository where raw data from multiple sources is stored and analyzed. Data lakes have the flexibility to store structured, unstructured, or semi-structured data. Microsoft Azure Data Lake is one of the 200 products of the Azure public cloud platform. This platform supports big data analytics and processes petabytes and exabytes of data.
The Working of Azure Data Lake
- Azure Blob storage is Microsoft’s object storage solution for the cloud. Azure Data Lake is built on the Blob storage. Azure Data Lake synchronizes with current IT investments to identify, manage, and secure unstructured data. It also synchronizes with operational stores and data warehouses which allows users to work in harmony with existing data applications.
With Azure Data Lake, you can process, query, and analyze data using helpful tools such as Spark, NoSQL data models, MapReduce, SQL querying, and many more. Let us take a look at the different components of Azure Data Lake and how they function.
Components of Azure Data Lake
Azure comprises three major components which offer storage, analytics services, and cluster capabilities.
Azure Data Lake Storage
- Data Lake Storage is a high-performance and high-processing data lake where massive volumes of data are stored. It was previously known as Azure Data Lake Store. This scalable and secure data lake offers a single storage platform to businesses where all their data can be integrated. By using Data Lake Storage, data silos can be eliminated and the cost for data storage can be reduced considerably.
Single sign-on and role-based access controls can be utilized through the Azure Active Directory. The Hadoop Distributed File System (HDFS), upon which the Data Lake is based, allows users to access all other tools that are built on the HDFS platform.
Azure Data Lake Analytics
- This is an on-demand analytics platform that is built on Apache Hadoop YARN (Yet Another Resource Negotiator). Data Lake Analytics simplifies big data and allows users to develop and run parallel data transformation and processing programs in U-SQL, Python, R, and .NET. Data Lake Analytics can process big data jobs in very little time. The data does not have to go through any virtual machines, clusters, or servers for its management and fine-tuning, rather can be processed in its raw form.
Data Lake Analytics can perform analytics on large volumes of data that may range up to petabytes in size. It presents a cost-effective solution for businesses because the charges incur on a per-job basis whenever data is processed.
- This is a managed service that allows for the fast, easy, and cost-effective processing of massive volumes of data. Azure HDInsight enables users to run optimized, open-source analytic clusters for Apache Spark, Map Reduce, Hadoop, Spark, Kafka, and R-Server.
Using these clusters, users can access multiple functions such as ETL, Machine Learning, Data warehousing, and IoT. HDInsight synchronizes with Azure Active Directory which allows role-based access controls and single sign-on capabilities.
- Henson Group is an Azure Expert MSP. We focus on helping customers architect and implement Azure Data and AI services. To help organizations accelerate their data transformation, Henson Group has helped large enterprises build out their data lakes and data warehouses to support automated operation data processing and reporting across their business.
For more information on Henson Group’s Managed Service Provider services for Azure Data Lake, visit here: https://www.hensongroup.com/henson-protect