The cloud is the platform of choice for analytics; and hence organisations are moving massive amounts of raw or unprocessed data to the cloud. Organisations are moving away from their enterprise data lake to modern cloud architectures. When selecting a cloud platform, organisations will favour cloud vendors that make it easy to store and access this data.
Microsoft’s Data Lake cloud storage service, Azure Data Lake (ADL) is designed to support scalable cloud storage and big data analytics.
Here are some of the reasons that make the Azure Data Lake attractive to organisations that need to manage and analyse large volumes of data to create meaningful insights.
Building and maintaining a traditional enterprise data lake architecture is costly to run as it requires massive computing power. With Azure Data Lake, organisations can scale storage and compute resources independently as required by the needs of the business. Also, organisations only pay for the computing resources and storage they use.
Scaling up an on-premises deployed data lake – to add more users and big data – requires configuring additional servers. This restricts the way organisations can operate. However, they can overcome the scalability challenges by using Azure Data Lake Store. The Data Lake Store makes use of Azure’s built-in auto-scaling feature and allows storing virtually any number of files of any size, even greater than a petabyte.
Another big benefit of Azure Data Lake is its ability to seamlessly integrate with existing Azure technologies, such as Azure Data Factory, Azure Synapse, SQL Database, Power BI, etc. This makes the process of capturing, processing, and analysing data simple and efficient.
Azure Data Lake Analytics helps simplify big data, as it gives the ability to obtain huge volumes (petabytes) of data from anywhere and in any format. Further, it enables data to be accessed across different storage areas like Azure Storage Blog, SQL DB, Azure SQL data warehouses, etc. The data in the storage areas can be queried using different languages like R, .NET, U-SQL, or Python to extract value from it.
There’s no appropriate data governance built into the traditional data lake framework. The Azure Data Lake Store is integrated with the Azure Active Directory (AD), helping organisations to secure and govern access to data with intelligent policies, like Conditional Access. Also, Azure AD offers multi-factor authentication to safeguard users from cyberattacks.
All companies are moving to the cloud. Those that move quickly will be rewarded as they are able to tap into additional revenue streams and realize operational cost efficiencies. When considering a migration from a legacy data lake to Azure Data Lake cloud solution, an automated approach will naturally speed up the cut-over timelines, save money and reduce risk. Explore how Next Pathway’s SHIFT™ Migration Suite helps solve the end-to-end challenges organizations experience when migrating to modern cloud platforms.
Next Pathway is the Automated Cloud Migration company. Powered by the SHIFT™ Migration Suite, Next Pathway automates the end-to-end challenges companies experience when migrating applications to the cloud. For more information, please visit nextpathway.com.