Cloudera Distribution for Hadoop Reviews

• 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.

Cloudera Manager is the most valuable feature for it’s ease of use, features, ease of upgrade and install components. CM can also be use to set up high availability within minutes. Others features like Hive, Pig, Impala, Flume and Spark are also valuable.

It's improved our storage and the availability of analytics tools such as Hive, Pig, Impala, and Spark helps us tremendously.

I'd like to see improvements to Impala. Also, it needs a more integrated environment with Spark, data warehouse, storage systems, cloud. Additionally, I'd want more UIs for components of ecosystem, preferably those UIs are centralized in a gateway.

I've used it for 3.5 years.

For experimental and production clusters alike, use Cloudera Manager right from the beginning. RPM installation is good for learning.

It has compatibility issues if installed in specialized hardware such as EMC Isilon or if node manager and data nodes are not co-located. For production, draw out a detailed plan on how to manage local repo for installation and upgrade. Never install from internet for production clusters.

Most of the clusters are for experimentation that don’t require support. For production clusters, implementations are through major vendors which are handled by them.

It depends on mode of installation. Cloudera Manager is always more straight forward and manageable. Avoid RPM installation as much as possible. Lay out plans with system admin on upgrade plan, commission and decommission nodes. Investigate impact and consequences of having HBase and Hadoop in the same cluster or as separate cluster, what are the impacts on system admin, cost, upgrades, data migrations, resources, etc?

The complexity kicks in when performing parameter configurations. Find out what are the use cases, are there disk IO or compution IO bound, are there lots of structured data or unstructured data for text analytics, etc.

Both vendor team and in-house depending on the cluster size and use cases. Some customers may require certain number of certified personnel, something to think about when choosing a partner.

Be prepared for fast changing landscape in how Hadoop works under the hood and how it is used. Each major release usually involved change of file system and data structure. How would they impact data migration. Ask questions like should they Upgrade or create a new cluster? Plans for training and skill upgrades.

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.

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.

Enterprise resource management, ease of use in terms of integration within the Hadoop ecosystem related products, and security.

Mainly they have to continuously evolve following the technology trends and replace or adapt part of their solutions accordingly.

We've used it since October 2012.

No issues encountered.

No issues encountered.

No issues encountered.

Pretty responsive and reactive compared to their competitors in the field.

It was extremely easy, and allowed less experienced personnel to get into the context pretty fast. Any difficulties/complexities faced were not related to the product itself rather than to the cluster infrastructure used.

In our case it was an in-house team including data scientists and data engineers (management & QA as well). With the appropriate training and the support offered by the vendor, it is not that hard to implement a small to medium scale project solution. However, complexity and size varies significantly between projects; therefore, it really depends.

That is not easy to answer since Huawei has several divisions using the product in different ways. Again regarding pricing/licensing highly depends on the context and the aims of the given organization for instance the level of support they are going to need, the type of services they are going to provide, or even the business domain they are targeting.

There were two provider solutions that have been evaluated. However, the level of customer service and technical support from Cloudera was better than the first one, and the second solution licence pricing was higher compared to Cloudera’s pricing schema.

Cloudera is doing a great job in the field offering an enterprise ready data platform. Based on my experiences I would definitely recommend it.

Very solid. Excellent user experience. good documentation. The Cloudera Manager is definitely a deal breaker. Packaging for Ubuntu is great for all the components.

Before the introduction of Cloudera Manager (that actually works), all the orchestration was done with scripts and Chef, and inexperienced team members had difficulties to participate in maintenance. The Cloudera Hadoop manager eased the work.

More customization, better documentation for the API (basically it's the same for all Cloudera Hadoop components).

I've used it for two years.

No issues encountered.

No issues encountered.

No issues encountered.

Didn't use dedicated service or support. The documentation is a bit of a mess, but it is decent and sufficient.

Straightforward. The CDH VirtualBox with preconfigured environment helps for demonstration purposes

We did it in-house.

We also looked at Hortonworks, but chose Cloudera because of my familiarity with it.

Do a comparisomn with Hortonworks as it's always good to compare to another major vendor

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.