Likelihood to Recommend 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.
Read full review 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.
Read full review Pros Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner. Apache Spark does a fairly good job implementing machine learning models for larger data sets. Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use. Read full review 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. Read full review Cons 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 Read full review 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. Read full review Likelihood to Renew Capacity of computing data in cluster and fast speed.
Steven Li Senior Software Developer (Consultant)
Read full review 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.
Read full review Usability The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
Read full review Support Rating 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.
Read full review Alternatives Considered All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like
Presto . Combining it with Jupyter Notebooks (
https://github.com/jupyter-incubator/sparkmagic ), one can develop the Spark code in an interactive manner in Scala or Python
Read full review 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.
Read full review Return on Investment Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark. Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy. Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs. Read full review 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. Read full review ScreenShots