Cloudera Distribution for Hadoop Reviews

Mostly HUE, Impala, Sqoop, and Hive. The impala-shell command is number one.

We are working on research for genomic data looking for specific genes and variances. Even Hive was not good enough to process it correctly, only with Impala are we getting results quicker.

Sometimes the heavy queries do not finish at all. It would be good to see the progress of heavy script in the impala shell or get some way to access it.

We started to use Cloudera about one-and-a-half years ago.

We are having some issues with stability and are speaking to Cloudera support.

It's acceptable.

It's acceptable.

We were trying AWS Impala as well, but Cloudera won as it had more functionality with HUE, Sqoop, and Solr as built-in functions.

We have struggled a bit in installing and configuring Cloudera Manager on the AWS cluster. For now, it is good.

We did the implementation only using our team and resources. It was a hard start, but an easy landing.

Cloudera is good for mid to big company, but small ones can use AWS Impala/HUE. Go to training, or you are going to spend many hours to find short answers. The Cloudera solution is big with good documentation, but you need to know what and where to read first.

• Cloudera Manager
• Impala
• Sentry

It has enabled us to move BI out of our OLTP database and build a data warehouse.

Some areas are under rapid development, like Spark.

I've used it for three years.

No issues with the current version.

No issues with the current version.

No issues with the current version.

It's excellent.

It's excellent.

We switched because Cloudera just works.

Cloudera Manager greatly simplifies initial setup.

In-house.

Make sure you have clearly articulated, doable use cases before you start.

The features I find most valuable are--

• Enterprise security features (authentication, authorization, data governance, and data protection)
• Proactive support 
• Training

• Providing robust infrastructure
• Fairly matured tools like Cloudera Navigator, Cloudera Manager, etc. 
• Professional support enabled us to provide great customer service
• Our clients are able to perform proactive maintenance in an efficient manner

Spark with R integration is missing. Also, it is lacking Spark SQL support.

I've used it for over eight months.

We faced issues in deploying Azure with Cloudera. Our machine hard disks were getting corrupted whenever we used to get patches on weekends. Now these have been resolved.

They offer excellent support.

It was complex because we were doing first time deployment of Cloudera on Azure. Also complexity was high due to lot of security features.

We are Big Data consultants, so we implement it.

Cloudera is a leader in providing distributions for Hadoop so it was no brainer for us to decide.

There were initial hiccups when deploying Cloudera on Azure but now this combo is working fine in production, so you can go for it.

The features we've found most valuable are--

• Fast processing of data
• Easy to manipulate using HiveQL

We were able to utilize data which was untapped previously. We've got great use cases now to drive business revenue.

It needs more standardized documentation on Hive.

I've used it for two and a half years.

It's great.

The level of technical support is great.

No previous solution was used, and senior management chose to bring it in.

I was not directly involved in deployment.

It was done by the vendor team, who were great.

It's good for Big Data analytics.

It automates the installation and configuration of Hadoop and different Big Data services.

We're currently trying to perform a failed installation and it's little bit difficult. It should restart the installation where it left off.

I've used it for two years.

• In some cases, logs are clear about failed services.
• While deploying in some failed steps it should not provide generic logs.

7/10 - they have forums where they will answer your query within a day.

We previously used Hortonworks and changed because Cloudera is simpler and more interactive.

It was very straightforward.

We did it in-house. They have good technical support to help with implementation.

We use the free version, and they provide everything we need.

Implement the free version as it provides enough services. If you want a backup service, or any extra service, then you can implement the enterprise version.

The most valuable feature for me are--

• Sentry - provides granular-level security
• Impala - open-source, MPP database

We used it to build an enterprise data hub.

Apache Kudu needs improvement. It's a real-time updatable database.

We used a vendor team to implement the solution.

The features most valuable to me are--

• Installation (very easy initial setup)
• Configuration
• Ability to update configuration through UI

It made Hadoop easy to use and made it easy to get started.

The licensing was by node. I think licensing by size of data managed would be a useful improvement.

I used Cloudera Manager to evaluate Hadoop and HBase for one year.

No issues encountered.

No issues encountered.

No issues encountered.

It's excellent.

It's excellent.

It was very easy.

It was implemented in-house.

We compared it to Amazon EMR but found Cloudera Manager to be more functional.

It's a great product and must be evaluated if you are planning to use Hadoop..

• 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.