"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"The solution has been very stable."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"Apache Spark can do large volume interactive data analysis."
"The main advantage is the storage is less expensive."
"Cloudera is a very manageable solution with good support."
"The product as a whole is good."
"The file system is a valuable feature."
"The solution is reliable and stable, it fits our requirements."
"CDH has a wide variety of proprietary tools that we use, like Impala. So from that perspective, it's quite useful as opposed to something open-source. We get a lot of value from Cloudera's proprietary tools."
"With a cluster available, you can manage the security layer using the shared SDX - it provides flexibility."
"We're now able to store large volumes of data through Cloudera Distribution for Hadoop. We're able to push large volumes of data to the platform, and that used to be a challenge, especially when storing a terabyte of information. This is the area where Cloudera Distribution for Hadoop improved the organization."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"The logging for the observability platform could be better."
"The initial setup was not easy."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"This is a very expensive solution."
"It could be faster and more user-friendly."
"Cloudera's support is extremely bad and cannot be relied on."
"The procedure for operations could be simplified."
"The initial setup of Cloudera is difficult."
"Cloudera Distribution for Hadoop has a limited feature list and a lot of costs involved."
"There are better solutions out there that have more features than this one."
"Currently, we are using many other tools such as Spark and Blade Job to improve the performance."
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Apache Spark is ranked 1st in Hadoop with 11 reviews while Cloudera Distribution for Hadoop is ranked 2nd in Hadoop with 10 reviews. Apache Spark is rated 8.0, while Cloudera Distribution for Hadoop is rated 7.4. The top reviewer of Apache Spark writes "Provides fast aggregations, AI libraries, and a lot of connectors". On the other hand, the top reviewer of Cloudera Distribution for Hadoop writes "Stores large volumes of data and makes log analytics, monitoring, and management easier, but its feature list is limited". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Lambda, AWS Batch and Amazon EMR, whereas Cloudera Distribution for Hadoop is most compared with Amazon EMR, HPE Ezmeral Data Fabric, SingleStore, InfluxDB and Cassandra. See our Apache Spark vs. Cloudera Distribution for Hadoop report.
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