• Blog
  • April 28, 2022

Azure Data Lakes for Modern Data Scientists

Azure Data Lakes for Modern Data Scientists
Azure Data Lakes for Modern Data Scientists
  • Blog
  • April 28, 2022

Azure Data Lakes for Modern Data Scientists

What is Azure Data Lake

Microsoft Azure Data Lake is a vastly scalable cloud service that allows its customers to gain insight from large, complex data sets. One can tailor Azure Data Lakes to store an unlimited amount of structured, semi-structured , or unstructured data from a variety of sources. Azure Data Lake consumers can use tools such as Microsoft’s Analytics Platform System or Azure Data Lake Analytics to query data sets or write their own code to perform customized operational or transactional data transformation and analysis tasks.

Azure Data Lake is built on the Apache Hadoop YARN cluster management platform and is intended to scale dynamically across SQL servers in Azure Data Lake, as well as servers in Azure SQL Database and SQL Data Warehouse. A consolidated approach within the Hadoop system enables the service to address the needs of big data projects, which are compute-intensive and often have dispersed data sources.

Why Azure Data Lake

The Data Lake in Azure solution is developed for organizations that want to take advantage of Big Data. It provides a data platform that can benefit developers, data scientists, and analysts to store data of any size, format them and perform all types of processing. Azure data lake also offers analytics across various platforms using numerous programming languages. It can also work with any of your existing solutions, such as identity management and security solutions. Moreover, it assimilates with other data warehouses and cloud environments and can be extremely valuable for organizations that need managed services such as-

  • Azure Active Directory: Azure Active Directory or AAD lets you provide Role-Based Access Control (RBAC) or identity within the solutions. These identities have numerous applications that can be managed by the service principal. The service principal stores the principal’s credentials if a service wants to connect to it, while managed identities are directly connected to the service, so there is no need to oversee credential storage.
  • Multi-protocol SDK: It is a new version of the Blob Storage SDK used with Azure Data Lake to manage reading and writing of the data from ADLS and retry if intermittent failure occurs.
  • Low-cost Storage: Azure storage has emerged as a cost-effective solution for data storage with various functions, such as data migrations from hot storage to cold storage, life-cycle management system, high power, archive storage, and much more.
  • Reliability: Azure Storage lets users make copies of their data to prepare for data center failure or a natural disaster. Also, the state-of-the-art threat detection system incorporates with the data storage and detects malicious programs or software that might impair the data or compromise your confidentiality.
  • Scalability: Azure is highly scalable with a current limit of up to 500 petabytes in various regions globally, (except the USA and Europe where the limit is 2 petabytes) it offers both linear and vertical scaling.

Components of Azure Data Lake

Storage, analytics service, and cluster capabilities are three key components of the Azure data lake. Let us look at each of them

  • Azure Data Lake Storage (ADLS) is a scalable and secure data lake for high-performance analytics workloads as discussed earlier. Designed to eradicate data silos, it provides a single storage platform that organizations can use to integrate their data.
  • Azure Data Lake Analytics is an on-demand analytics platform for Big Data. Users can develop and run parallel data conversion and processing programs in U-SQL, R, Python, and .NET over petabytes of data. U-SQL is a Big Data query language plotted by Microsoft for the Azure Data Lake Analytics service. With Azure Data Lake Analytics, consumers pay per job to process data on-demand in analytics as a service environment. It is a cost-effective analytics solution as you pay only for the processing power that you use.
  • Azure Databricks is often the preeminent choice for an enterprise running Azure Cloud Services as this is a Spark-based analytics platform particularly optimized for Microsoft Azure Cloud. It is ideal for enterprises that wish to grow the collaboration between their Data Scientists for running Spark-based workloads efficiently at a better performance. Azure Databricks works on a premium Spark cluster faster than the open-source Spark. Azure Databricks is a PaaS solution and doesn’t require a lot of work after the initial setup. It provides security thanks to the Azure Active Directory integration without any need for custom configuration bringing you all the pros that Databricks provides, only now in Azure.

Sample Usecase

British mathematician and data scientist Clive Humby once famously stated –

‘Data is the new Oil. Like oil, data is valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity.’

Azure and its various managed services are much akin to those distilleries that make your data profitable for you. Here are some examples of how Azure Databricks has helped some of the biggest names in the business across sectors.

  • Digital Payment Platform for HSBC – Reinventing mobile banking with ML


  • “We want to support anyone looking to keep fit and active with services that are as tailored as possible. Thanks to the Azure cloud, we now have everything we need to really personalize our user experience.” – Christoph Ferrari, Head of Data Engineering and Data Science, Runtastic


Finishing up

MSRcosmos being a Microsoft Solutions Partner with advanced specialization in Microsoft Azure, help businesses in strengthening their decision science capabilities through Azure data lakes. Our team of experts can help you deploy Microsoft Azure data lakes to accelerate your data transformation journey.