Azure Data Lake Analytics Alternatives

Hadoop Distributions

Considering alternatives to Azure Data Lake Analytics? See what this market Azure Data Lake Analytics users also considered in their purchasing decision. When evaluating different solutions, potential buyers compare competencies in categories such as evaluation and contracting, integration and deployment, service and support, and specific product capabilities.

Check out real reviews verified by Gartner to see how Azure Data Lake Analytics compares to its competitors and find the best software or service for your organization.

Reviewed in Last 12 Months

How Alternatives Are Selected

1. Amazon Athena

By Amazon Web Services (AWS)

4.4

Athena has been easy to use to query our long term event data storage Read all insights and reviews for Amazon Athena

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2. Amazon EMR

By Amazon Web Services (AWS)

4.4

My organization has utilized Amazon EMR for about 45 days, and the overall experience has been great. We chose EMR due to our existing Amazon Heavy infrastructure, evaluating AWS solutions over external vendors. EMR provided more flexibility in cost management than auto-scaling groups, especially with its dynamic handling of node counts and mixing on-demand and spot instances. EMR addressed key business pain points: Firstly, it delivered significant cost savings. We reduced daily costs by 40-45% for a service previously spending on 100% on-demand instances, by implementing an 80/20 distribution of spot to on-demand nodes. This projects potential savings per month if all our Hadoop loads transition to EMR. Other teams have also started using EMR for heavy Hadoop loads due to its cost optimization. Secondly, EMR vastly improved our heavy reporting service for customers, which previously suffered from report queuing and throttling during peak morning hours. Our report response time improved dramatically, from approximately 30 mins to 2 mins. The system spins up additional nodes when reports are triggered and shuts them down during lean periods, preventing cost incurrence. This directly led to the retention of a significant FMCG giant customer in the US, a contract that could have resulted in over a 5% loss of our overall revenue, with minimal deployment effort. From an onboarding perspective, my prior experience with EMR made the process straightforward. AWS offers extensive use cases, design diagrams, and paid support typically responds within 2-24 hours. Customization was not a major concern, as EMR’s features and documentation meet most industry needs. EMR efficiently handles large-scale data processing, with configurable node limits and built-in fault tolerance. A caution regarding performance: for very large node during peak hours, it's advised to maintain at least a 50/50 split between spot and on-demand instances to avoid availability issues. Read all insights and reviews for Amazon EMR

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3. HPE Data Fabric Software

By Hewlett Packard Enterprise (HPE)

4.3

Nice optimization with MapR-FS (over old and limited HDFS) and cool feature of securing with encryption at directory level not the entire drive.Read all insights and reviews for HPE Data Fabric Software

Where Azure Data Lake Analytics Scored Higher

Easier to integrate and deploy
Better evaluation and contracting

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4. Azure Data Lake Store

4.3

Efficient and scalable platform for all our big data analytics needs. Read all insights and reviews for Azure Data Lake Store

Where Azure Data Lake Analytics Scored Higher

Easier to integrate and deploy
Better evaluation and contracting

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5. FusionInsight Big Data Platform

4.9

it is a fast and reliable solution that combines the great Data utility, storage and data analysis features.It supports multi-dimensional comparisons as well as high-performance analysis and querying.Read all insights and reviews for FusionInsight Big Data Platform

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6. Google Cloud Platform

4.6

GCP is very powerful distributed platform to share the files or store the file. We can easily store and share the file to other with privacy which one has access of the bucket. It is much easier while cluster job execution. Read all insights and reviews for Google Cloud Platform

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7. IBM BigInsights for Apache Hadoop

By IBM

4

this product helped out in creating ETL pipelines and in setting basic main node and data node architecture. Read all insights and reviews for IBM BigInsights for Apache Hadoop

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8. Hortonworks Sandbox

4.2

If you want to try or learn big data, this is an option you can consider.Read all insights and reviews for Hortonworks Sandbox

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