Cloudera Distribution for Hadoop Reviews

I primarily use CDH for data storage and regular dashboard reports.

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.

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.

CDH is stable.

CDH is scalable, but it's expensive to do it.

Cloudera's support is extremely bad and cannot be relied on.

I wouldn't recommend CDH to others because of its high cost.

I would rate CDH as eight out of ten.

We use this solution to process data.

When using an SQL Server you have to build indexes and you need to fine-tune the data.

We import the data that is in the SQL Source.

With a single script, we are able to run the jobs within minutes, which is an advantage.

We are using the Power BI model for the business convention. The performance in Power BI will be reduced if you incorporate more calculations. Those calculations are captured in the Hadoop layer and processed.

It could be faster and more user-friendly.

I have been using this solution for seven months.

It's a stable product. I don't see any performance issues.

This solution is scalable. We have 40 users for different projects in our organization.

We will continue to use this solution.

Technical support is good.

I didn't use any other product.

The installing is straightforward.

Our clients provide us with the access to use it directly.

Once you have been given access to the edge nodes we are able to run the scripts in the Hadoop layer.

We do not pay for licensing because our customers forward it, so there is no need to purchase the license for the project.

I would recommend this solution.

I would rate Cloudera Distribution for Hadoop a nine out of ten.

The Cloudera Manager administrator webpage simplifies the administration tasks and helps to maintain a global overview of the cluster performance.

We are moving from an standard SQL environment (Oracle DataWarehouse) to a Big Data environment, and the Hadoop cluster will be the key of our new organization. It will allow to scale in an easy namer.

We found some difficulties when importing Hive tables from another Cluster.

I want to point the fact that we encounter many problems related to the cloud storage and how resources are managed. Our learning has been that, although it is quite simple to deploy single machines on the cloud, deploying clusters of machines is much more complex as many factors need to be considered: individual machines, connectivity across machines, storage.

I've used it for three months.

We found some issues but were related with the hardware provider. For the moment I have not detected any problem from the Cloudera software point of view.

Technical support is really efficient.

We chose this product as it is considered a market standard and due to its wide documentation on the web. I evaluated other options but the fact that now it is becoming an standard for many companies helped me to choose this option.

In the cloud environment where we deployed (Azure Resource Manager) there was a ready-to-deploy template which simplified a lot the initial set-up.

We implemented with an in-house team. Our initial idea was to stop the cluster during the weekends and when there was no usage. However, we found strong difficulties and we were not able to start programmatically the whole cluster, so finally we left the cluster working all the time.

This issues were mainly related with the cloud provider and how this provider manages the resources for the cluster machines.

From our point of view it is a long-time investment. We hope to get the ROI in the following years.

I am very comfortable with this product. The combination of Cloudera Manager administrator server, which allows the management of the Hadoop Cluster, and the Hue server, which simplifies the use make this product a current standard on the market. Perhaps it lacks a full integration of all its components.

• Cluster rolling restarts 
• Cluster wide configuration management

For the clusters using CM, we are able to more tightly control and manage the configuration of all nodes in the clusters. 

We are currently running six production clusters totaling 900+ nodes, and are building three more clusters. Knowing that if someone has some custom configuration on a node that they haven’t communicated out, and that I can ignore that configuration and bring that node into line with where we’ve decided to run the cluster, is very beneficial.

HBase 1.0 stability issues and processing speed is a major area for improvement. Right now, our Cloudera 5 clusters run four to seven times slower than our Cloudera 4 clusters using our storm and kafka topologies, which causes real-time processing to be a major challenge.

CM’s API is very limited and difficult when used on multiple clusters in the same CM instance

We've used it for approximately two years. We also use Cloudera Manager, which is 6/10.

No issues encountered.

Cloudera 5 is currently very unstable. Between two Cloudera 5 clusters, we have an incident at least twice a week due to what are now outstanding bugs.

It's very easy to deploy and scale as large as you want. Once created on the CM management cluster, is difficult to scale up as needed, as you add more clusters to the same CM instance.

No previous solution was used.

We were already running one production cluster with approximately 75 nodes when I joined, so I’m not familiar with what was needed to get the initial production cluster up. Once I joined, I assisted in standing up the additional nodes and clusters using our chef automation.

In house via chef automation. Chef, or similar systems, makes it much simpler to stand up large scale clusters. That said, I have not used or evaluated vendor team implementation methods.

Faster runtime for batch jobs.

Improved Business Intelligence reporting from daily to every two hours satisfying the business stakeholders who would favour transactional systems to draft reports because it had the latest data. 

The issue that arises using transactional systems with multiple version of truths across the enterprise. With faster turn-around time business stakeholders are now adopting the BI systems designed to give a cohesive view of the performance metrics important to them.

Full Support for all Spark SQL features, support for SparkR, compatibility with Hive for DataFrame saved tables.

Cloudera CDH5.5.x does not support SparkR. SparkR, the integration of R models in API would be a great addition since this will enable fast near real-time analytical integration of R models with data feed.

The functionality in SparkSQL to save a DataFrame as a table in HIVE produces a table not compatible with HIVE. There is a workaround for this in creating the HIVE table first and then doing inserts.

Cloudera CDH5.5.x is a great product, but the adoption of additional features not currently supported will make the product even better but by no means subtract from its desirability.

Do thorough research and ensure your use-cases or scale does not conflict with the system requirements and that those features that would make a difference are supported.

• Cloudera Manager for administering the Hadoop cluster
• Cloudera specific solutions like Impala
• Extensive documentation
• Good user community

Implementing a Hadoop cluster has become relatively straight-forward using CDH. Administering it is also less complex. As a result, efforts spent in these areas are less than anticipated.

• Some of the UI features seem confusing e.g. charts on the CM Services page
• Sometimes it gets confusing to follow a single path for installation due to multiple recommended approaches e.g. parcels vs packages

We have been using it for the last two years.

Following a single path for installation becomes confusing due to multiple recommended approaches e.g. parcels vs packages.

Flume seems unstable and has to be restarted quite often.

None as such

We are mostly using Cloudera Express so we did not use their technical support. However, the Cloudera community is an active place and Cloudera representatives participate actively in understanding and resolving issues.

Cloudera is a prominent player in the Hadoop space and we did not have a need to adopt a different solution. However, we are also looking to work on Hadoop and MapR

Following a single path for installation was initially confusing due to multiple recommended approaches e.g. parcels vs. packages. However, after a while, we managed to master it. However, knoweldge of Cloudera Manager and Hadoop architecture is a must.

We have our own team of consultants who are proficient in implementing it. The high level steps about the implementation remain the same; however, it is the environment specific issues which are challenging.

We haven't really measured ROI.

Licensing price on per node basis for Cloudera seems to be pretty steep (based on the inputs we have received from Cloudera).

It is user friendly and installation is pretty straightforward. Cloudera Manager is a good tool to administer it. However, configuration for specific requirements is sometimes pretty complex.

You should have a team which is knowledgeable in Hadoop. Do keep in mind that the product is still maturing so there are good chances that you will come across unexpected issues now and then.

Our core product is an insurance product and the actuarial module is quite complex. SMEs so far collect data from various sources into Excel sheets and through macros do the analytics which is a very crude form of doing the analysis. So we thought to use big data for such analysis.

That is still in PUC stage, as I have mentioned our analyst used to do the actuarial on a spreadsheet but after Hadoop  implementation they are getting confidence that now analysis is more appropriate and fast. Now exploring cloud implementation as well.

Keeping multi copies of the file and tools of map reduce like PIG, HIVE due to their flexibility it is easy to develop the application with less or almost no knowledge of Java and Sql. And capability to handle huge data size.

As such in the product side, I don't have much to comment. But like other upcoming technologies like RPA, AI, GO etc they have ample training materials with variety of USE Cases, which users can understand and aligned with their current requirements. On same ground I didn't see much training materials from Cloudera.

Seems quite stable, as such didn't face any issue.

It is very stable, didn't face any performance issue.

No when we were heard of Hadoop, we tried on that only. I mean tried to migrate from spreadsheets to Hadoop.

Very straight forward. Typical Windows type installation...Next, next, next clicks.

In-house.

Other department handles all these so I can't comment on that.

Not really.