We performed a comparison between Apache Hadoop and Microsoft Azure Synapse Analytics based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Synapse has a slight edge in this comparison. According to its users, it is more user-friendly and less expensive than Hadoop.
"As compared to Hive on MapReduce, Impala on MPP returns results of SQL queries in a fairly short amount of time, and is relatively fast when reading data into other platforms like R."
"Initially, with RDBMS alone, we had a lot of work and few servers running on-premise and on cloud for the PoC and incubation. With the use of Hadoop and ecosystem components and tools, and managing it in Amazon EC2, we have created a Big Data "lab" which helps us to centralize all our work and solutions into a single repository. This has cut down the time in terms of maintenance, development and, especially, data processing challenges."
"The most important feature is its ability to handle large volumes. Some of our customers have really large volumes, and it is capable of handling their data in terms of the core volume and daily incremental volume. So, its processing power and speed are most valuable."
"One valuable feature is that we can download data."
"Since both Apache Hadoop and Amazon EC2 are elastic in nature, we can scale and expand on demand for a specific PoC, and scale down when it's done."
"The most valuable feature is the database."
"Apache Hadoop can manage large amounts and volumes of data with relative ease, which is a feature that is beneficial."
"Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability."
"This is a stable solution with many functionalities."
"We've had a good experience with technical support in general."
"The ability to scale out services on-demand and scale them down when they are not required is most valuable. You are in control of your expenditures, and you are also in control of the horsepower that you need. That's a major advantage."
"Data can be stored any way you want in the data warehouse."
"The best thing about it is that it has integration at multiple places. It can talk to more than 90 types of data sources, which is one good thing about it."
"The most valuable features of Microsoft Azure Synapse Analytics are the interface and the agility of the on-cloud platform."
"It's scalable; you can scale up and scale down."
"I like SQL post, which is for storage and distributed computing. Another good feature is the copy activity."
"I think more of the solution needs to be focused around the panel processing and retrieval of data."
"The integration with Apache Hadoop with lots of different techniques within your business can be a challenge."
"The solution is very expensive."
"What could be improved in Apache Hadoop is its user-friendliness. It's not that user-friendly, but maybe it's because I'm new to it. Sometimes it feels so tough to use, but it could be because of two aspects: one is my incompetency, for example, I don't know about all the features of Apache Hadoop, or maybe it's because of the limitations of the platform. For example, my team is maintaining the business glossary in Apache Atlas, but if you want to change any settings at the GUI level, an advanced level of coding or programming needs to be done in the back end, so it's not user-friendly."
"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."
"I mentioned it definitely, and this is probably the only feature we can improve a little bit because the terminal and coding screen on Hadoop is a little outdated, and it looks like the old C++ bio screen. If the UI and UX can be improved slightly, I believe it will go a long way toward increasing adoption and effectiveness."
"The load optimization capabilities of the product are an area of concern where improvements are required."
"From the Apache perspective or the open-source community, they need to add more capabilities to make life easier from a configuration and deployment perspective."
"The initial setup process needs improvement. When you're moving to the cloud it takes a bit of time. It would be great if they could implement something that would make it faster."
"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."
"I am pretty sure that there are areas that need improvement but I just can think of them off the top of my head."
"One area that could be improved is the schema management."
"This solution needs to have query caching so that if the same query is run and the results are available, it will return the data from the cache without having to re-run the query."
"The initial setup is complex."
"Microsoft should develop an interface to make it easier to shift from on-premise to the cloud."
"The only concern for us is the cost part. When it comes to the implementation and the support and maintenance, we see high-cost implications."
More Microsoft Azure Synapse Analytics Pricing and Cost Advice →
Apache Hadoop is ranked 5th in Data Warehouse with 34 reviews while Microsoft Azure Synapse Analytics is ranked 2nd in Cloud Data Warehouse with 86 reviews. Apache Hadoop is rated 7.8, while Microsoft Azure Synapse Analytics is rated 7.8. The top reviewer of Apache Hadoop writes "Handles huge data volumes and create your own workflows and tables but you need to have deeper knowledge". On the other hand, the top reviewer of Microsoft Azure Synapse Analytics writes "No competitors provide the entire solution to one place ". Apache Hadoop is most compared with Azure Data Factory, Oracle Exadata, Snowflake, Teradata and BigQuery, whereas Microsoft Azure Synapse Analytics is most compared with Azure Data Factory, SAP BW4HANA, Snowflake, Oracle Autonomous Data Warehouse and AWS Lake Formation. See our Apache Hadoop vs. Microsoft Azure Synapse Analytics report.
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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.
