IT Central Station is now PeerSpot: Here's why

Apache Spark vs Cloudera Distribution for Hadoop comparison

Cancel
You must select at least 2 products to compare!
Featured Review
Buyer's Guide
Apache Spark vs. Cloudera Distribution for Hadoop
May 2022
Find out what your peers are saying about Apache Spark vs. Cloudera Distribution for Hadoop and other solutions. Updated: May 2022.
610,045 professionals have used our research since 2012.
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"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."

More Apache Spark Pros →

"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."

More Cloudera Distribution for Hadoop Pros →

Cons
"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."

More Apache Spark Cons →

"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."

More Cloudera Distribution for Hadoop Cons →

Pricing and Cost Advice
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • "Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
  • "Spark is an open-source solution, so there are no licensing costs."
  • More Apache Spark Pricing and Cost Advice →

  • "When comparing with Oracle Sybase and SQL, it's cheaper. It's not expensive."
  • "The price could be better for the product."
  • "I haven't bought a license for this solution. I'm only using the Apache license version."
  • "Cloudera requires a license to use."
  • "Cloudera Distribution for Hadoop is expensive, with support costs involved."
  • "I wouldn't recommend CDH to others because of its high cost."
  • More Cloudera Distribution for Hadoop Pricing and Cost Advice →

    report
    Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
    610,045 professionals have used our research since 2012.
    Questions from the Community
    Top Answer:I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
    Top Answer: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.
    Top Answer:Spark is an open-source solution, so there are no licensing costs.
    Top Answer:The most valuable feature is that I can use CDH for almost all use cases across all industries, including the financial sector, public sector, private retailers, and so on.
    Top Answer:I wouldn't recommend CDH to others because of its high cost.
    Top Answer:Cloudera's prices are too high and are not competitive with other solutions. They could also improve the Data Science Workbench and add some more features, like wizard activities.
    Ranking
    1st
    out of 22 in Hadoop
    Views
    10,192
    Comparisons
    8,105
    Reviews
    11
    Average Words per Review
    447
    Rating
    8.1
    2nd
    out of 22 in Hadoop
    Views
    4,618
    Comparisons
    3,346
    Reviews
    10
    Average Words per Review
    319
    Rating
    7.4
    Comparisons
    Learn More
    Overview

    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

    Cloudera Distribution for Hadoop is the world's most complete, tested, and popular distribution of Apache Hadoop and related projects. CDH is 100% Apache-licensed open source and is the only Hadoop solution to offer unified batch processing, interactive SQL, and interactive search, and role-based access controls. More enterprises have downloaded CDH than all other such distributions combined.
    Offer
    Learn more about Apache Spark
    Learn more about Cloudera Distribution for Hadoop
    Sample Customers
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
    37signals, Adconion,adgooroo, Aggregate Knowledge, AMD, Apollo Group, Blackberry, Box, BT, CSC
    Top Industries
    REVIEWERS
    Financial Services Firm36%
    Computer Software Company27%
    Marketing Services Firm9%
    Non Profit9%
    VISITORS READING REVIEWS
    Computer Software Company21%
    Comms Service Provider19%
    Financial Services Firm13%
    Media Company7%
    REVIEWERS
    Financial Services Firm33%
    Computer Software Company22%
    Marketing Services Firm11%
    Insurance Company11%
    VISITORS READING REVIEWS
    Computer Software Company25%
    Comms Service Provider19%
    Financial Services Firm13%
    Government7%
    Company Size
    REVIEWERS
    Small Business43%
    Midsize Enterprise20%
    Large Enterprise38%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise15%
    Large Enterprise70%
    REVIEWERS
    Small Business26%
    Midsize Enterprise23%
    Large Enterprise51%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise12%
    Large Enterprise72%
    Buyer's Guide
    Apache Spark vs. Cloudera Distribution for Hadoop
    May 2022
    Find out what your peers are saying about Apache Spark vs. Cloudera Distribution for Hadoop and other solutions. Updated: May 2022.
    610,045 professionals have used our research since 2012.

    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.

    See our list of best Hadoop vendors.

    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.