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.
"What comes with the standard setup is what we mostly use, but Ambari is the most important."
"It's good for storing historical data and handling analytics on a huge amount of data."
"The scalability of Apache Hadoop is very good."
"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."
"Most valuable features are HDFS and Kafka: Ingestion of huge volumes and variety of unstructured/semi-structured data is feasible, and it helps us to quickly onboard a new Big Data analytics prospect."
"The most valuable feature is scalability and the possibility to work with major information and open source capability."
"The best thing about this solution is that it is very powerful and very cheap."
"It's open-source, so it's very cost-effective."
"The most valuable feature is the level of processing power, and being able to complete tasks in parallel."
"It's quite quick for querying, even with large datasets, and it's scalable. It's also flexible to use, so it's easy to update and get data quickly without wasting time."
"They are very reliable and cost-effective."
"The product is very user friendly."
"The speed is great and the architecture is excellent."
"It's scalable; you can scale up and scale down."
"The MPP (Massively Parallel Processing) architecture helps to make things a lot faster."
"The solution operates like a typical SQL Server environment so there is no alienation in terms of technical knowledge."
"It would be good to have more advanced analytics tools."
"We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it."
"Based on our needs, we would like to see a tool for data visualization and enhanced Ambari for management, plus a pre-built IoT hub/model. These would reduce our efforts and the time needed to prove to a customer that this will help them."
"Real-time data processing is weak. This solution is very difficult to run and implement."
"The solution is not easy to use. The solution should be easy to use and suitable for almost any case connected with the use of big data or working with large amounts of data."
"The integration with Apache Hadoop with lots of different techniques within your business can be a challenge."
"The stability of the solution needs improvement."
"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 would like to see them provide the ingestion of images."
"The performance needs to improve in future releases."
"I'd like to see part of the service de-coupled."
"One area for improvement could be better integration with Power BI, as well as data integration with BW."
"Indicating what areas need improvement in this solution is a difficult question because the organizations that I am working for are really new in this area. However, an even better more simple interface, or perhaps an extension of a connector app store solution, would be helpful."
"The major challenge that we're seeing with Azure Synapse is around security concerns. The way it is working right now, it has Managed VNet by Microsoft option, similar to the implementation of Azure Databricks, which may pose a concern for financial institutions. For managed environments, the banks have very strict policies around data being onboarded to those environments. For some confidential applications, the banks have the policy to encrypt it with their own key, so it is sort of like Bring Your Own Key, but it is not possible to manage the resources with Microsoft or Databricks, which is probably the major challenge with Azure Synapse. There should be more compatibility with SQL Server. It should be easier to migrate solutions between different environments because right now, it is not really competitive. It is not like you can go and install SQL Database in some other environment. You will have to go through some migration projects, which probably is one of the major showstoppers for any bank. When they consider Synapse, they not only consider the investment in the actual service; they also consider the cost of the migration process. When you scale out or scale down your system, it becomes unavailable for a few minutes. Because it is a data warehouse environment, it is not such a huge deal, but it would be great if they can improve it so that the platform is available during the change of configuration."
"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."
"Could have more connectors and better integration for Hadoop."
More Microsoft Azure Synapse Analytics Pricing and Cost Advice →
Apache Hadoop is ranked 5th in Data Warehouse with 33 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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