Likelihood to Recommend In well-suited scenarios, I would recommend using Apache Flink when you need to perform real-time analytics on streaming data, such as monitoring user activities, analyzing IoT device data, or processing financial transactions in real-time. It is also a good choice in scenarios where fault tolerance and consistency are crucial. I would not recommend it for simple batch processing pipelines or for teams that aren't experienced, as it might be overkill, and the steep learning curve may not justify the investment.
Read full review Altogether, I want to say that Apache Hadoop is well-suited to a larger and unstructured data flow like an aggregation of web traffic or even advertising. I think Apache Hadoop is great when you literally have petabytes of data that need to be stored and processed on an ongoing basis. Also, I would recommend that the software should be supplemented with a faster and interactive database for a better querying service. Lastly, it's very cost-effective so it is good to give it a shot before coming to any conclusion.
Read full review Pros Low latency Stream Processing, enabling real-time analytics Scalability, due its great parallel capabilities Stateful Processing, providing several built-in fault tolerance systems Flexibility, supporting both batch and stream processing Read full review Handles large amounts of unstructured data well, for business level purposes Is a good catchall because of this design, i.e. what does not fit into our vertical tables fits here. Decent for large ETL pipelines and logging free-for-alls because of this, also. Read full review Cons Python/SQL API, since both are relatively new, still misses a few features in comparison with the Java/Scala option Steep Learning Curve, it's documentation could be improved to something more user-friendly, and it could also discuss more theoretical concepts than just coding Community smaller than other frameworks Read full review Less organizational support system. Bugs need to be fixed and outside help take a long time to push updates Not for small data sets Data security needs to be ramped up Failure in NameNode has no replication which takes a lot of time to recover Read full review Likelihood to Renew Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free
Read full review Usability Great! Hadoop has an easy to use interface that mimics most other data warehouses. You can access your data via SQL and have it display in a terminal before exporting it to your business intelligence platform of choice. Of course, for smaller data sets, you can also export it to Microsoft Excel.
Read full review Support Rating We went with a third party for support, i.e., consultant. Had we gone with Azure or Cloudera, we would have obtained support directly from the vendor. my rating is more on the third party we selected and doesn't reflect the overall support available for Hadoop. I think we could have done better in our selection process, however, we were trying to use an already approved vendor within our organization. There is plenty of self-help available for Hadoop online.
Gene Baker Vice President, Chief Architect, Development Manager and Software Engineer
Read full review Online Training Hadoop is a complex topic and best suited for classrom training. Online training are a waste of time and money.
Read full review Alternatives Considered Apache Spark is more user-friendly and features higher-level APIs. However, it was initially built for batch processing and only more recently gained streaming capabilities. In contrast, Apache Flink processes streaming data natively. Therefore, in terms of low latency and fault tolerance, Apache Flink takes the lead. However, Spark has a larger community and a decidedly lower learning curve.
Read full review Not used any other product than Hadoop and I don't think our company will switch to any other product, as Hadoop is providing excellent results. Our company is growing rapidly, Hadoop helps to keep up our performance and meet customer expectations. We also use HDFS which provides very high bandwidth to support MapReduce workloads.
Read full review Return on Investment Allowed for real-time data recovery, adding significant value to the busines Enabled us to create new internal tools that we couldn't find in the market, becoming a strategic asset for the business Enhanced the overall technical capability of the team Read full review There are many advantages of Hadoop as first it has made the management and processing of extremely colossal data very easy and has simplified the lives of so many people including me. Hadoop is quite interesting due to its new and improved features plus innovative functions. Read full review ScreenShots