Cloudera Data Platform Reviews

• It's the one and only complete open source big data platform
• Ambari-managed admin configuration for HDFS, YARN, Hive, HBase, etc.
• Customized dashboards
• Web-based HDFS browser
• SQL editor for Hive
• Apache Phoenix - OLTP and operational analytics on Hadoop
• Apache Zeppelin - A web-based notebook that enables interactive data analytics

• Maintenance of our own data lake in the enterprise-level
• Storage and analysis of server logs
• Applying Operational Intelligence in the enterprise-level based on the analysis of various department units data
• Semantic analysis based on the feeds from social networking data, clickstream data, etc.

• Rolling upgrade
• Disaster recovery features such as mirroring should be supported

We've used it for one year.

No issues encountered.

No issues encountered.

No issues encountered.

3/10

3/10

No previous solution was in place.

It's easy to setup.

We did it in-house.

Completely use the community edition along with other features that can be implemented on top.

No other solutions were looked at.

Study, analyze, and compare with other big data platforms features according to your requirements before choosing the appropriate one.

It has a powerful, user-friendly interface called Ambari which allowed us to administrate our cluster easily.

It allows us to performa data lake implementation to handle/treat huge amounts of data, or what we call the "terrible bytes”.

Integrate a complete hive web client (Ambari views), like Hue Today, in the next release.

I've used it for three years.

The HDP v2.3 is stable release. But we have encountered some issues linked to hbase (fixed by: hbase server and region server should not be installed on the same node).

It has good documentation, but it's not fully complete for complex security needs (knox/ranger with cluster).

We have installed our first cluster using native Apache repositories.

The setup of Hadoop was easy thanks to Ambari, but installing the security components was complex.

Our cluster has been implemented in-house. We have automated the entire installation of Hadoop, set-up and configuration included.

Hadoop/Cloudera is still much cheaper than Oracle's RDBM system, if you want to handle a huge amount of data and make complex analytics. 

It's 40,000€ for 10 Hadoop nodes vs 1.7 million Euros  for an Oracle server with 40 cores.

In short, I recommend this product simply because Hortonworks is the only distribution that runs on Linux and Windows Servers.

Hortonworks is 100% Open Source. Hortonworks does a great job in managing all different components of Hadoop.

We've done multiple implementations of it. It enables customers to perform sentimental analysis from social media data to engineering analytics.

Security- Although they support Knox and Ranger and Kerberos, they are still missing attribute-level encryption features.

Name Node High Availability is still not stable (memory issues).

• Ambari
• Hive
• Sqoop
• Flume
• Spark

The Hadoop value proposition is in expanded functionality, linear scalability, and reduced software and infrastructure costs. Hadoop offers several generic frameworks for batch, real-time, and iterative processing, such as map-reduce, spark, and spark streaming. Additionally, these frameworks provide libraries for predictive analytics and machine learning. This type of expanded functionality is not easily achieved on any other single platform.

File system to provide indexed access to individual records with in-place update/delete. Also, Security integration through a common interface for authentication, authorization, disk encryption, network encryption, data access layer, data masking, etc.

Hadoop, does not provide improved performance, compared to traditional RDBMS, unless processing batches in the TB-PB range, or if the Hadoop platform has significantly more resources available.

I have implemented various flavors of Hadoop over the past five years, including platform configuration and application development.

Deployment is improving rapidly, but like other flavors of Hadoop there are always issues.

Stability is improving rapidly, but like other flavors of Hadoop there are always issues.

5/10 - Responsive, but like all flavors of Hadoop, there are too many tickets to be reasonably triaged and supported.

I have implemented various flavors of Hadoop such as Hortonworks and Cloudera over the past five years, including platform configuration and application development.

Straightforward once you know what you’re doing.

I work for a vendor team.

ROI is one of the main reasons organization pursue Hadoop. Cost per TB is a compelling factor.

Hadoop is complex. It takes a dedicated approach from individuals with a broad range of technology skills and commitment to overcome challenges that do not normally present themselves in well-established technologies.

Hadoop is complex. It takes a dedicated approach from individuals with a broad range of technology skills and commitment to overcome challenges that do not normally present themselves in well-established technologies.

The features I've found most valuable are--

• Ambari UI
• Hive
• Pig
• Hive
• Also integrated Tableau with this distribution

It's easy to deploy and we've used this distribution for some of our recommendation and trend analysis use cases.

I've used it for almost one year.

No issues encountered.

No issues encountered.

We faced some issues while upgrading to newer versions with current distributions, but with their support we solved it.

Customer service is great.

Technical support is great.

No, we did not use a previous solution.

Initial setup was straightforward.

We implemented it with our in-house team.

From a product standpoint, their Ambari UI is incredibly valuable for cluster monitoring. It simplifies the deployment and maintenance of hosts, and we can provision, configure and test Hadoop services.

From an overall perspective, Hortonworks support is crucial to our operations.

As this is open source, there are certain features that need tuning, such as the Hue UI. More stability on this would be helpful.

I've used it for one year.

As this is all new technology, we face issues at every level. However, hardware support and documentation have been instrumental in helping us resolve the majority of those issues.

We've had some issues with stability, but hardware support and documentation have helped us resolve most of those.

No issues with scalability.

They have outstanding customer support. Their responses are prompt, and they resolve issues quickly.

I have not used a solution of this nature before.

The set up is straightforward enough, but at every level there are many parameters to be tuned. Ensuring all these parameters are set is the complex part, as poorly set parameters can cause unwanted issues.

We have an in-house team to do implementations. I would advise that all implementations get seen through all the way to having users smoke test applications to ensure correct functionality.

I would suggest that if you are implementing this at an enterprise level, the support is compulsory. Additionally having a high degree of patience is key, as this is open source and road bumps can be frequent when moving at a fast pace.

There are several features that are most valuable for us--

• Hue
• Hive
• Spark
• S3

With it, we have faster processing times for our apps.

It needs to be quicker and to have the ability to automate deployment on multiple nodes.

I've used it for two years.

Sometimes there were issues.

Sometimes there were issues.

Sometimes there were issues.

I've not had to use it.

No solution had been used previously, but we are using it alongside AWS EMR.

It was complex to configure.

It was done in-house.

We provide services for product implementation, so people looking for such products can contact me.

Ease of deployment and management of the Hadoop cluster are features we've found most valuable.

It allows our organization to collect data from databases that are different, and where the data is similar, it allows for a detailed analysis from a single data store.

The ability to update data is an area where the product could improve.

I've used it for one year.

We had an issue during deployment. You have to be sure that your base image is perfect and that your infrastructure is properly configured or issues will occur.

We don't have their paid support, but I have had discussions with their engineers and they have been extremely helpful. So based on that, I would give them 8/10.

The initial setup is complex. It mainly stems from small issues that typically pop up and also a lack of experience in deploying the product. I highly suggest taking the Hortonworks Training prior to deploying.

We used an in-house team. Take your time and utilize the free resources provided by Hortonworks.

At this point I don't believe I could provide a ROI as we aren't fully utilizing the product.

If possible, I would suggest paying for the professional services which would give you on-site engineers to help deploy the cluster.

We did look at Cloudera, but due to having literally no money to spend for the project, we chose Hortonworks due to its being completely free and open source.

Take your time and script as much as you can so that all base images are the same.