Azure Data Lake Storage vs. IBM Analytics for Apache Spark

Azure Data Lake is an absolutely essential piece of a modern data and analytics platform. Over the past 2 years, our usage of Azure Data Lake as a reporting source has continued to grow and far exceeds more traditional sources like MS SQL, Oracle, etc.

Incentivized

• Setting up Azure Data Lake Storage account, container is quite easy
• Access from anywhere and easy maintenance
• Integration with Azure Data Factory service for end to end pipeline is pretty easy
• Can store Any form of data (Structured, Unstructured, Semi) in faster manner

Incentivized

• study for the certifications also to have them as a reference for work when you have any questions about applying a configuration to the equipment.
• The Internet interface is simple and easy to use. Capacity is good and it's good that HP continues to innovate with this technology

Incentivized

Azure Data Lake Storage from a functionality perspective is a much easier solution to work with. It's implementation from Amazon EMR went smooth, and continued usage is definitely better. However, Amazon EMR was significantly cheaper overall between the high transaction fees and cost of storage due to growth. The two both have their advantages and disadvantages, but the functionality of Azure Data Lake Storage outweighed it's cost

Incentivized

• Instead of having separate pools of storage for data we are now operating on a single layer platform which has cut down on time spent on maintaining those separate pools.
• We have had more of an ROI with the scalability as we are able to control costs of storage when need be.
• We are able to operate in a more streamlined approach as we are able to stay within the Azure suite of products and integrate seamlessly with the rest of the applications in our cloud-based infrastructure

Incentivized