

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.
The technical support is quite good and better than IBM.
Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms.
Without a doubt, we have had some crashes because each situation is different, and while the prototype in my environment is stable, we do not know everything at other customer sites.
We faced challenges but overcame those challenges successfully.
Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;
Integrating with Active Directory, managing security, and configuration are the main concerns.
It can be deployed on-premises, unlike competitors' cloud-only solutions.
Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming.
The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
This is the only solution that is possible to install on-premise.
| Product | Market Share (%) |
|---|---|
| Apache Spark | 13.9% |
| Cloudera Distribution for Hadoop | 15.1% |
| Other | 71.0% |


| Company Size | Count |
|---|---|
| Small Business | 28 |
| Midsize Enterprise | 15 |
| Large Enterprise | 32 |
| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 9 |
| Large Enterprise | 31 |
Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory
We monitor all Hadoop 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.