Apache Spark vs. Cloudera Data Hub

Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.

Incentivized

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Cloudera excels at seamless migrations and upgrades.



Cloudera supports self-healing and data center
replacement of failed cloud instances while maintaining the state.



Cloudera is essential to increase or decrease
capacity through the user interface or API.



Cloudera is great at simplifying big data analytics
by providing the technology and tools needed to gain insights from IoT and
connected devices to help monitor and condition our assets.



Cloudera's cybersecurity platform option offers
stronger anomaly detection, visibility, and prevention, as well as faster
behavioral analysis.



Cloudera is beneficial for enabling and utilizing
the platform's machine learning and ad-hoc queries while securely storing,
retrieving, and analyzing any volume of data at scale.

Incentivized

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• Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
• Faster in execution times compare to Hadoop and PIG Latin
• Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
• Interoperability between SQL and Scala / Python style of munging data

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• Excellent management capabilities via Cloudera Manager.
• Open source and does not restrict our data to be bound by a proprietary format.
• Offers excellent support for data governance and auditing.
• Has all the components that would help us build a data hub.
• Excellent platform support offered by Cloudera.

• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

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Independently Invited by TrustRadius

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• Not fully Open Source, couple of components of the distributions are privately owned, meaning with public contributions are not welcome
• Improvements to Cloudera manager can only be recommended. its very hard to get it done once recommended as the full control is with them.
• Should make components more aligned to Open Source rather than making it closed sourced.
• Custom Features of open source software tools supported only by Cloudera are tricky. Cant commit changes to tools like Hue.
• Improvements to Cluster Management tool is required, which are already available to its competitors.

Incentivized

Independently Invited by TrustRadius

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Capacity of computing data in cluster and fast speed.

Likely to renew the use in case the requirements for Cloudera remain valid. The rapid change in customer requirements and solutions that must be validated, integrated or tested changes. As the maturity of the solution increases, the requirements to renew use decrease. From a solution feature perspective by itself would probably grade 10.

If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used

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1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.

Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.

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Independently Invited by TrustRadius

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Cloudera is
compatible with Windows operating systems, and Mac allows cloud-based
deployment, it is also very useful to configure data encryption, guarantee
protocols, and security policies. It also provides integrated auditing and
monitoring capabilities, as well as a control comprehensive data repository for
the enterprise, and ensures vendor compatibility through its open-source
architecture.

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• Business leaders are able to take data driven decisions
• Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
• Business is able come up with new product ideas

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• Cloudera products are the most widely. It is more business friendly as data is more secure. The sensitive data that you operate on is local to you and your project rather than processing this data on Cloud.
• Cloudera is definitely faster as wait time is reduced if on Cloud.
• A lot range of products are covered. So it is definitely good for businesses and had good returns on investments.