Azure Data Lake Storage for Big Data Analytics, Now Offers lifecycle Management

If you are working with Big Data and running ML algorithms, you are probably using Azure Data Lake Storage (ADLS)- being the only cloud storage service that is purpose-built for big data analytics. With ADLS, you get maximized performance and the ability to scales and meet even the most demanding analytics workloads, while paying cloud object storage rates.

Previously: When deploying ADLS you had to choose one access tiers to put your data under- “Hot” or “Cold”. “Hot” meant your data was highly available, but you paid a higher price. “Cold”, meant you had some latency in accessing the data, but you paid less. So, what if at first you accessed the data often, but over time your need for access drops drastically? You had to manually change the access tiers from hot to called. And if you didn’t, you kept paying a higher price.

Now: Lifecycle management, which was available for blob storage, is now extending to ADLS. Now, you can create custom policies to manage your ADLS to automatically transition your data to the appropriate access tier and even delete it at the end of a predetermined retention period.

Azure Data Lake Storage lifecycle

With this new addition, you can optimize your data for both performance and cost.

Note- Although the lifecycle management feature is free of charge, you do need to go to the lifecycle management blade to determine, or change. the costume policies.

Lifecycle management blade (Picture source: Microsoft)

To learn more about pricing, visit the Azure Data Lake Storage pricing page.

For more information check out Azure documentation.

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