Hortonworks Data Platform Reviews

We evaluated Cloudera and Hortonworks. Based on our evaluation and actual experience in production of 60 nodes and development of 12 nodes, the most valuable features of Hortonworks are:

• 100% open
• No lock-in like Cloudera
• Fast and accurate support instantly
• Largest number of committers to Hadoop by any means
• Hive is better in performance and ease of use compared to Impala

It helps a lot in data in motion (ingestion and manage in real time). We are able to do 3rd-party data monetization of our data within a t+20 minute time frame to our end customers.

• Cost
• Reliability
• Speed
• Ease of use

I have used it for three years.

I initially encountered deployment issues, but they were very good in resolving them.

I have not encountered stability issues.

I have not encountered any scalability issues at all. That's the key reason we picked HDP over Cloudera, as Cloudera have issues & don't support compression of Hive in ORC format. They push only their products (not good).

Customer service has been excellent from the day one until now... and our Admin is comfortable with the SLA and turnaround time.

Technical support is very good and proactive with SmartSense.

We previously used a different solution. We switched from Cloudera. Initially, we went with Cloudera due to it being a popular choice in the market, etc, then realized it was bad choice. Before we scaled from 6 nodes to 12 nodes and before we went livein production, we scrapped it due to Impala's performance and lock-in.

Using Ambari, it was easy to set up and we even tried the AWS for a test cluster.

An in-house team implemented it: two admins, seven developers, one data scientist, one PM and 22 business users at the customer (end-user side).

ROI is 300%.

Hortonworks is the best, comparing all three flavors. If all is well, we might use open source alone in the next three years; others you can't due to lock-in...

Before choosing this product, we also evaluate Cloudera.

It is the best in terms of product vision and actual delivery.

• 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.

• 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 HDFS (Java-based file system) and Hive Utilities are proving to be most useful.

Hortonworks has allowed my organization to increase the amount of data that we regularly store from sensor data and weblogs, which in turn gives us a greater scope of data to analyze.

I would like to see an increase in usability for the Apache Storm engine within the data platform.

I have been using it for less than a year.

When initializing our cluster, we did not allocate enough space to our VAR partition and that ended up causing some issues with the networking to our onsite Tomcat server.

It's fairly low customer service.

It's fairly low technical support.

We started off with this product.

Both straightforward and complex, everything was easy to set up, but a lot of the behind the scenes configuration changes for customization could be rather time consuming.

We used an in-house team. My advice is to study hard and read all the documentation thoroughly before starting any implementation. It is paramount that one understands the system before implementing it.

Current ROI is none as we are still in the POC phase with most of our products.

Be sure that the product is necessary for the situation.

• Open-source
• Big community

It is a different paradigm than standard relational databases. We can also process different tasks then just those related to the standard database world. That said, we are capable of processing various data science tasks, e.g. natural language processing or log processing.

• Stability
• It needs to be more mature
• Security
• User friendliness

I've used it for three years alongside MapR and Cloudera.

Almost every part of the Hadoop ecosystem has its problems and bugs.

Almost every part of the Hadoop ecosystem has its problems and bugs.

Almost every part of the Hadoop ecosystem has its problems and bugs.

The paid service is pretty good, but if you don't pay, there is documentation available in the community which is pretty good.

I slightly experienced Cloudera which is very similar to Hortonworks, but there are parts which are not open source. I'm working more with Hortnoworks because all its parts are open source. and my company has a longer partnership with Hortnoworks

It is easy if you have good administrators. It is also easy if you want to just play with it on your laptop. For real work and stability, I definitely recommend some paid support.

I was involved in multiple projects. Usually, it was done in-house with paid support.

Every project is different. Since Hadoop is an infrastructure for a long period there is no simple ROI. Also, each customer has different expectations.

We use this solution for the hospitality industry. 

It was for end to end data processing and data manipulations.

The data platform is pretty neat. The workflow is also really good. 

The NiFi platform could be enhanced. This refers to the data ingestion in a workflow. 

It would also be nice if there was less coding involved. 

I have been using this solution for six years. 

The technical support is okay, but not excellent. They can take a while to respond. 

If you wish to use this solution, make sure you compare it with some other solutions first to make sure it's right for your needs. 

Overall, on a scale from one to ten, I would give this solution a rating of nine.

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.

Its flexibility is the most valuable feature because you can leverage any Hadoop component and take full advantage of its open source capabilities.

We're able to perform sentiment analysis on Twitter data.

It needs a better UI.

I used it for five months, one year ago.

It requires too much coding work; we're not good Java and Python developers.

No issues.

No issues.

They are detailed and informational.

We got stuck with Java job development and were able to get assistance from tech support.

No previous solution was used.

It was straightforward since we have Linux resources.

We did it in-house.

• Datameer
• Cloudera

You need to be tech savvy.