Compare Apache Hadoop vs. Microsoft Azure SQL Data Warehouse

Apache Hadoop is ranked 4th in Data Warehouse with 11 reviews while Microsoft Azure SQL Data Warehouse is ranked 2nd in Cloud Data Warehouse with 10 reviews. Apache Hadoop is rated 7.6, while Microsoft Azure SQL Data Warehouse is rated 7.8. The top reviewer of Apache Hadoop writes "We are able to ingest huge volumes/varieties of data, but it needs a data visualization tool and enhanced Ambari for management". On the other hand, the top reviewer of Microsoft Azure SQL Data Warehouse writes "A good solution for simple data warehousing that scales well, but it needs better technical support". Apache Hadoop is most compared with Snowflake, Pivotal Greenplum and Oracle Exadata, whereas Microsoft Azure SQL Data Warehouse is most compared with Snowflake, SAP BW/4HANA and Amazon Redshift.
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Most Helpful Review
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Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
The most valuable features are powerful tools for ingestion, as data is in multiple systems.The most valuable feature is the database.It's good for storing historical data and handling analytics on a huge amount of data.The ability to add multiple nodes without any restriction is the solution's most valuable aspect.What comes with the standard setup is what we mostly use, but Ambari is the most important.The best thing about this solution is that it is very powerful and very cheap.The most valuable features are the ability to process the machine data at a high speed, and to add structure to our data so that we can generate relevant analytics.Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing.

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The most valuable feature of the solution is the analytics and that it can connect with Power BI.The most valuable feature is the incremental load because we do not need to refresh the entire data on a daily basis.Azure elasticity allows us to scale as much as we want, and it is pay-as-you-go, so we can scale up as we need to.The features we've found most valuable for data warehouses is extracting data, SSIS packages, and the DBs.The main advantage of using this solution is its ability to scale and handle very large amounts of data, in the petabyte range.The most valuable feature is the scalability.The MPP (Massively Parallel Processing) architecture helps to make things a lot faster.The initial setup was really easy and straightforward.

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Cons
It would be helpful to have more information on how to best apply this solution to smaller organizations, with less data, and grow the data lake.It would be good to have more advanced analytics tools.The solution could use a better user interface. It needs a more effective GUI in order to create a better user environment.There is a lack of virtualization and presentation layers, so you can't take it and implement it like a radio solution.In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency.The upgrade path should be improved because it is not as easy as it should be.We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it.I would like to see more direct integration of visualization applications.

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I would like my team to be able to build pipelines that integrate with the Azure Data Factory.I would like to see version control implemented into the data warehouse.The configuration for things like high-availability could be more user-friendly for non-technical users.I'm not entirely happy with the billing model. I'm not entirely happy with how the enterprise services are pretty expensive, but that's about it.With respect to what needs to be improved, concurrent connectivity has some limitations.It's pay as you go, so you never know what your bill is going to be beforehand, and that's scary for customers. If you have someone who makes a mistake and the program's a loop that is running all night, you could receive a very expensive bill.This is a young product in transition to the cloud and it needs more work before it is both settled as a product and competitive in the market.It would be of interest to improve things like the web service integration and availability in terms of being easy to create internal web services in the database.

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Pricing and Cost Advice
This is a low cost and powerful solution.​There are no licensing costs involved, hence money is saved on the software infrastructure​.

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The licensing fees for this solution are on a pay-per-use basis, and not very expensive.When we are not using this solution we can simply shut it down saving us costs, which is a nice advantage.This solution starts at €1000.00 a month for just the basics and can go up to €300,000.00 per month for the fastest version.The pricing is okay. You can pay as you go.The price of this solution could be improved.

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Ranking
4th
out of 30 in Data Warehouse
Views
12,931
Comparisons
11,053
Reviews
10
Average Words per Review
427
Avg. Rating
7.5
2nd
Views
8,085
Comparisons
6,960
Reviews
10
Average Words per Review
496
Avg. Rating
7.7
Top Comparisons
Compared 34% of the time.
Compared 26% of the time.
Compared 13% of the time.
Also Known As
Microsoft Azure SQL DW, Azure SQL Data Warehouse
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Apache
Microsoft
Overview
The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

Azure SQL Data Warehouse is a Fast, flexible, and secure analytics platform for the enterprise. Azure SQL Data Warehouse lets you independently scale compute and storage, while pausing and resuming your data warehouse within minutes through a massively parallel processing architecture designed for the cloud. Seamlessly create your hub for analytics along with native connectivity with data integration and visualization services, all while using your existing SQL and BI skills.

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Learn more about Apache Hadoop
Learn more about Microsoft Azure SQL Data Warehouse
Sample Customers
Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web LabToshiba, Carnival, LG Electronics, Jet.com, Adobe,
Top Industries
VISITORS READING REVIEWS
Software R&D Company35%
Financial Services Firm15%
Comms Service Provider14%
Government8%
VISITORS READING REVIEWS
Software R&D Company42%
Financial Services Firm9%
Comms Service Provider9%
Manufacturing Company5%
Find out what your peers are saying about Snowflake Computing, Teradata, Oracle and others in Data Warehouse. Updated: February 2020.
398,050 professionals have used our research since 2012.
We monitor all Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.