Azure Data Lake Storage vs. HPE Data Fabric

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

MapR is more well-suited for people who know what they are doing. I consider MapR the Hadoop distribution professionals use.

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

• MapR had very fast I/O throughput. The write speed was several times faster than what we could achieve with the other Hadoop vendors (Cloudera and Hortonworks). This is because MapR does not use HDFS, which is essentially a "meta filesystem". HDFS is built on top of the filesystem provided by the OS. MapR has their filesystem called MapR-FS, which is a true filesystem and accesses the raw disk drives.
• The MapR filesystem is very easy to integrate with other Linux filesystems. When working with HDFS from Apache Hadoop, you usually have to use either the HDFS API or various Hadoop/HDFS command line utilities to interact with HDFS. You cannot use command line utilities native to the host operation system, which is usually Linux. At least, it is not easily done without setting up NFS, gateways, etc. With MapR-FS, you can mount the filesystem within Linux and use the standard Unix commands to manipulate files.
• The HBase distribution provided by MapR is very similar to the Apache HBase distribution. Cloudera and Hortonworks add GUIs and other various tools on top of their HBase distributions. The MapR HBase distribution is very similar to the Apache distribution, which is nice if you are more accustomed to using Apache HBase.

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

• It takes time to get latest versions of Apache ecosystem tools released as it has to be adapted.
• When you have issues related to Mapr-FS or Mapr Tables, its hard to figure them out by ourselves.
• Sometime new ecosystem tools versions are released without proper QA.

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

I don't believe there is as much support for MapR yet compared to other more widely known products.

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

• Increased employee efficiency for sure. Our clients have various levels of expertise in their deployment and user teams, and we never receive complaints about MapR.
• MapR is used by one of our financial services clients who uses it for fraud detection and user pattern analysis. They are able to turn around data much faster than they previously had with in-house applications

Incentivized