<img height="1" width="1" src="https://www.facebook.com/tr?id=152309752858854&amp;ev=PageView &amp;noscript=1">

Azure Data Lake: 4 key benefits

Looking for a better way to store your unstructured, semi-structured or structured data in your organisation?

The Azure Cloud data lake is specifically designed and flexible to run large scale data and analytics workloads in a scalable and cost effective manner. In this article, we breakdown what a data lake is, its benefits and how other data services on Azure can benefit your business.


What is a Data Lake?

A Data Lake includes all of the capabilities required to make it easy for developers, data scientists and analysts to store data of any size and shape and at any speed, and do all types of processing and analytics across platforms and languages.
Built on top of a storage account in Azure, Data Lakes allows raw data to be stored ready for processing in a scalable way.

A Data Lake works with existing IT investments for identity, management and security for simplified data management and governance. It also integrates seamlessly with operational stores and data warehouses so that you can extend current data applications.

Azure Data Lake Analytics

Azure Data Lake Analytics (ADLA) is the compute part of the service you use to move, process and transform your big data located in Azure Data Lake Store. Using linked analytic engines like Azure HDInsight, Hadoop and Spark, you can apply batch and interactive queries, move refined data to Azure Synapse Analytics to build a report and run real-time analytics and machine learning to your data to produce better, actionable insights.

Because it’s highly scalable, you can set how much processing power you need when you need it, making it extremely cost-effective. Ultimately, ADLA allows businesses to spend more time on transforming data, writing queries and gaining more insights.


4 key benefits to Azure Data Lake

Data Lake Azure


1. Consolidate your data into one place:

ADLS brings together all of your big data from disparate sources across cloud and on-premises environments into one central place. You can monitor and manage all stored data more easily, without having to go back and forth between more than one silo. If you’re looking to reduce the number of places you store your data or tools you use for data analytics, it’s an ideal solution for data consolidation.

2. A Cost-effective solution:

Azure Data Lake is a cost-effective solution to run big data workloads. You can choose between on-demand clusters or a pay-per-job model when data is processed. In both cases, no hardware, licences or service-specific support agreements are required. The system scales up or down with your business needs, meaning that you never pay for more than you need. It also lets you independently scale storage and compute, enabling more economic flexibility than traditional big data solutions. Data Lake minimises your costs while maximising the return on your data investment.

3. It’s secure and compliant:

Azure Data Lake is fully managed and supported by Microsoft, backed by an enterprise-grade SLA and support. Data Lake protects your data assets and extends your on-premises security and governance controls to the cloud easily. Data is always encrypted – in motion using SSL, and at rest using service or user-managed HSM-backed keys in Azure Key Vault. Capabilities such as single sign-on (SSO), multi-factor authentication is built in with Azure Active Directory.

4. Developer friendly platform:

Finding the right tools to design and tune your big data queries can be difficult. Azure Data Lake makes this easy through deep integration with Azure DevOps, Visual Studio, Eclipse and IntelliJ, so that you can use familiar tools to run, debug and tune your code. Visualisations of your U-SQL, Apache Spark, Apache Hive and Apache Storm jobs let you see how your code runs at scale and identify bottlenecks and cost optimisations. 



Because of its scalability and cost-effective nature, Azure Data Lake is increasingly being used to handle big data. Big data is generally high in volume and takes a long time to process and analyse for meaningful insights, so having a scalable and centralised solution to store massive amounts of raw, unstructured information without having to transform it first - while having native integration with powerful data analysis tools - is becoming an increasingly essential toolset for business that want to become more data driven in their decision making.


Visit our Azure Synapse Analytics   Expertise Page



Tags: Data & AI