Likelihood to Recommend 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 Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.
Read full review Pros 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 Its performance, ease of use, and simplicity in learning and deployment. Using this tool, we can quickly analyze large amounts of data. It's adequate for map-reducing large datasets and fully abstracted MapReduce. Read full review Cons 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 UDFS Python errors are not interpretable. Developer struggles for a very very long time if he/she gets these errors. Being in early stage, it still has a small community for help in related matters. It needs a lot of improvements yet. Only recently they added datetime module for time series, which is a very basic requirement. 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 It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
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 The documentation is adequate. I'm not sure how large of an external community there is for support.
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 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 Apache Pig might help to start things faster at first and it was one of the best tool years back but it lacks important features that are needed in the data engineering world right now. Pig also has a steeper learning curve since it uses a proprietary language compared to Spark which can be coded with Python, Java.
Read full review Return on Investment 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 Higher learning curve than other similar technologies so on-boarding new engineers or change ownership of Apache Pig code tends to be a bit of a headache Once the language is learned and understood it can be relatively straightforward to write simple Pig scripts so development can go relatively quickly with a skilled team As distributed technologies grow and improve, overall Apache Pig feels left in the dust and is more legacy code to support than something to actively develop with. Read full review ScreenShots