

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop.
I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.
Google's support team is good at resolving issues, especially with large data.
The fact that no interaction is needed shows their great support since I don't face issues.
Whenever we have issues, we can consult with Google.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
Google Cloud Dataflow can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
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.
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
The job we built has not failed once over six to seven months.
The automatic scaling feature helps maintain stability.
Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns.
Dealing with a huge volume of data causes failure due to array size.
It is part of a package received from Google, and they are not charging us too high.
Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code.
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.
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
The integration within Google Cloud Platform is very good.
Google Cloud Dataflow's features for event stream processing allow us to gain various insights like detecting real-time alerts.
| Product | Market Share (%) |
|---|---|
| Apache Spark | 13.9% |
| Cloudera Distribution for Hadoop | 15.1% |
| HPE Data Fabric | 14.9% |
| Other | 56.1% |
| Product | Market Share (%) |
|---|---|
| Google Cloud Dataflow | 4.5% |
| Apache Flink | 12.3% |
| Databricks | 10.0% |
| Other | 73.2% |


| Company Size | Count |
|---|---|
| Small Business | 28 |
| Midsize Enterprise | 15 |
| Large Enterprise | 32 |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 2 |
| Large Enterprise | 10 |
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
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