TrustRadius Insights for Hortonworks Data Platform are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Recommendations
Based on user reviews, users recommend the following for Hortonworks:
It is recommended to learn Big Data concepts before using Hortonworks. Having a solid understanding of Big Data will help users effectively implement their projects using this platform.
Users suggest considering HDP (Hortonworks Data Platform) for long-term use as it is built on Hadoop and is expected to support a wider range of projects in the future. Choosing HDP will provide access to more advanced features and ensure long-term success.
Ambari, the Cluster Management Tool, needs to improve its features to compete with other options. Users suggest that major enhancements should be made to Ambari's functionality to make it more competitive among its counterparts.
In summary, users recommend learning Big Data concepts beforehand, considering HDP for long-term projects, and evaluating Ambari's features when selecting a Hadoop vendor.
We use [Hortonworks Data Platform] in our big data development process data pipeline. In our test environment, we develop some applications. We install a physical cluster and use it for both competence center research and proof of concept design and for development purpose. Some projects are now in production environments for our customer
Pros
Monitoring console
Spark data processing
UI application
Acl with ranger in service and hdfs data
Cons
Upgrade service version require OS competence
Search features like cloudera search
Likelihood to Recommend
<div>I recommend [Hortonworks Data Platform] as Big Data platform in order to start your developments. It's free. It's easy to use. You can install in more server or use a sandbox with you favorite virtualization platform ( vmware or oracle virtualbox). There is also a containerized version.</div><div>Manage our data in hdfs is simple; you can interact with server with REST API.</div>
<div>Hortonworks Data Platform is one of the main solutions we have for Big Data components. We are using a lot of components like HDFS, HBase, Hive, Oozie, Storm, Kafka, Ambari, Zookeeper, Zeppelin, etc that are packaged and provided as part of the Hortonworks Data Platform.</div><div>It provides tools for us to manage big data components, store data raw in no SQL database, query, stream data, have projections on data from HDFS and HBase, etc. It also helps us build pipelines to ingest and perform some analytics on data.
</div>
Pros
It does a good job of packaging a lot of big data components into bundles and lets you use the ones you are interested in or need. It supports an extensive list of components which lets us solve many problems.
It provides the ability to manage installations and maintenance using Apache Ambari. It helps us in using management packs to install/upgrade components easily. It also helps us add, remove components, add, remove hosts, perform upgrades in a convenient manner. It also provides alerts and notifications and monitors the environment.
What they excel in is packaging open source components that are relevant and are useful to solve and complement each other as well as contribute to enhancing those components. They do a great job in the community to keep on top of what would be useful to users, fixing bugs and working with other companies and individuals to make the platform better.
Cons
One thing that could be improved is a browsing, querying, and analytics tool. Currently, Ambari views is the tool they provide to browse for example HDFS, etc. Even though it is improving, it still is not as useful as Hue which provides different kinds of editors, browsers and interfaces a better way like browsing HDFS, browsing HBase tables, accessing Hive and querying, etc.
There are some works in progress or changes in direction with their different lines of products. They have Hortonworks Data Platform and Hortonworks Data Flow, among others, where storm and Kafka are supported. There was an effort to move storm and Kafka to HDF from HDP, which added some confusion as well as supporting on both later. It gets challenging to keep track of what to use where, and to keep up with version changes and compatibilities.
Now with Hortonworks and Cloudera coming together, it gets more confusing which of the components are going to be supported, promoted, merged. Both have competing products and some complimentary products. We're hoping to see it will have a good mix that is going to get the best products from both suites into one, but it will be a challenging year or two until that evolves.
Likelihood to Recommend
<div>All in all, it is a great product and a convenient way of getting a lot of components for big data installed and configured. It provides components for most things you want to perform in ingesting, streaming and setting up for analytics. It also does a great job with the dashboard tool by integrating Grafana and providing dashboards for most components used. It helps users get a great visual on the state of data.</div><div>I can't imagine any cases where you have needs for big data where HDP won't be suited well, as it is as good a platform in that domain as Cloudera or Mapr, and should be able to meet your needs.
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VU
Verified User
Engineer in Information Technology (10,001+ employees)
My company is a non-profit healthcare delivery institute consists of hospitals and clinics. We started a new working group to see possibility of using big data technology with machine learning to improve patient care and healthcare quality improvement. We adopted HortonWorks data platform to store and analyze data from medical devices in our Intensive Care Units. Our ICU has a lot of devices that monitor patients' status in real time and detect any abnormal pattern. Since it produces numerous data, we stored them in a volatile manner, which means they may be deleted after while, to save disk space in our databases. We did pilot study of using Hortonworks Data Platform and Hadoop inside it to replace traditional data storage.
Pros
It is a well suited data platform to support big data storage and analysis, with computational efficiency, good performance, and stability.
It is free to use. Online development community is well supported. Hortonworks engineers seem to have good experience and skill sets.
It is easy and fast to integrate with other tools or components for big data handling and analysis.
Cons
Since it doesn't come with propriety tools for big data management, additional integration is need (for query handling, search, etc).
It was very straightforward to store clinical data without relations, such as data from sensors of a medical device. But it has limitations when needed to combine the data with other clinical data in structured format (e.g. lab results, diagnosis).
Overall look and feel of front-end management tools (e.g. monitoring) are not good. It is not bad but it doesn't look professional.
Likelihood to Recommend
We used Hortonworks within our Medical Informatics department to validate possibility of using big data tools and platforms in the field of patient monitoring and near-real time pattern detection. Although our efforts weren't in production, it was demonstrated that Hortonworks provides good computational efficiency, stability to be used in a mission critical businesses such as healthcare services and patient care.
VU
Verified User
Team Lead in Research & Development (10,001+ employees)
We support organizations and existing customers evolving from traditional business analytics into big data and data science. Hortonworks is big data stack distribution, consisting of 100% open source projects. It mainly stands on Hadoop and Spark components, plus a number of improvements also developed in the open, like Druid and Superset.
Pros
Hortonworks two main pillars are HDP (Hortonworks Data Platform) and HDP (Hortonworks Data Flow). The former applies to the infrastructure required for building and deploying a data lake, and the latter is about ingestion, in batch or realtime.
Both HDP and HDF rely entirely on opensource projects, this is a distinctive point about Hortonworks.
In the last year new improvements like Data Plane and Stream Analytics Manager (SAM) take HDP and HDF several steps further into management and governance.
Cons
As an open source project collection, it relies strongly on community activity. You still have the option to contract premium consulting or training services.
Altough it is quickly evolving into Data Science tools availability (eg. Tensorflow incorporate in HDP 3), it can be cumbersome from a developer transitioning from a traditional IDE, into the notebook vs. datalake metaphore.
As expected for a big data infranstructure, the resource requirements base line is rather high. This means that if used on premise, you need to think of about 10 machines for a minimal reasonable deploy.
Likelihood to Recommend
It is best used where organizations need to build a data lake from scratch, leveraging its capabilities for ingesting huge volumes from a vast number of different sources -including sensors, logs, text, transactional systems and more.
However, if you just want to try a data science use case, think about if your volume demands such deployment.
Typically it is used as an enterprise platform. There are very few companies that use it only departmentally. It solved the business problems of maintaining a pure open source Hadoop environment. It also solves for Disaster Recovery and Security. Hadoop was not designed for Security, but with Hortonworks Ranger and Kerberos, you can implement a world class security framework.
Pros
Security with Ranger. Cell level security. Integration with Kerberos and LDAP.
Ambari management console. UI and API that allows complete management of the platform.
PLUS components have metadata, streaming, monitoring and other tools that flesh out the offering.
Cons
Installation is highly complex and needs to be streamlined.
The platform itself needs to be a little more stable. It might help to actually slow down releases to make sure they are more stable.
Upgrading from lower versions should be easier.
Likelihood to Recommend
Hortonworks Data Platform is well suited:
<ul><li>If you require the newest releases and capabilities and are prepared to upgrade frequently</li><li>If you want full, cell level, ticketing and perimeter security</li><li>If you want metadata, streaming and other PLUS component capabilities</li></ul>If you are not prepared to do upgrades at least on a quarterly basis, DO NOT use Hortonworks. If you have a very plain vanilla implementation, you might want to look at pure open source Hadoop.
We use our hortonworks hadoop cluster to process web & email logs. It has enabled us to process huge volume of data much quicker. It is widely used across the company!
Pros
HDP is the closest to an open source platform you can get in hadoop ecosystem with more choice of tools than everything else. The convenience of Ambari UI and API for building, deploying and managing the cluster makes it relatively easy to get started.
With Yarn and Spark you can mix different nodes for storage and compute and master nodes to manage loads.
The tez engine - hortonworks sandbox which can be installed for learning and development purposes.
Cons
Version upgrades are more challenging than anticipated. Each upgrade has its own quirks and compatibility issues that need to be resolved manually.
Real time analytics like impala is unavailable.
Monitoring isn't that great. Ambari Management interface on HDP is just a basic one and does not have many rich features.
Likelihood to Recommend
<p>Hortonworks provides a framework comprising open source projects which is good for any open source lovers. Easy to start with its great tutorials.</p><div>
<ol><li>Building and deploying large scale Hadoop/Hive/Yarn/Spark clusters with hundreds of nodes in distributed environments on Cloud</li><li>Process Web/Mobile traffic and provide customizable customer experience</li><li>Advanced Analytics picture of customer behaviors and shopping patterns
</li><li>Provide personalized website for each customer based on their needs
</li><li>Use it to handle the entire catalog of major retailers
</li></ol>
Pros
Knowledge base of the original committers
SmartSense of HDP is a great way of understanding the problems before they happen
Integrates very well with Apache Phoenix (SQL on Hadoop)
Very good support team
Provides more features for the money
Cons
Licensing cost is high when compared to other distribution partners
VM setup - It's not as good as what Cloudera provides
Monitoring isn't that great. Ambari Management interface on HDP is just a basic one and does not have many rich features
Version upgrades are more challenging than anticipated. Each upgrade has it's own quirks and compatibility issues that need to be resolved manually
Likelihood to Recommend
It's the right platform for our data science & engineering team who can use it as a ecosystem to solve major retail problems
I have been using Hortonworks Data Platform for personal as well as professional use for some time now. We also consult for clients looking evaluate and use the HDP distribution. HDP makes it intuitive and easy to use for people looking to use Hadoop and relevant Big Data technologies open sourced by Apache. HDP's own products are developed and open sourced which makes the community stronger.
Pros
HDP keeps up-to pace with the Apache Hadoop.
HDP's Ambari is intuitive and easy to use.
HDP remains similar to most open source tools and hence makes the learning curve gentler.
Cons
The HDP community is relatively new and could get better.
Tools like Falcon which are not so much used in general tend to remain dormant in the HDP when it comes to development.
Likelihood to Recommend
I find HDP easy to use and solves most of the problems for people looking to manage their big data. Evaluating the Hortonworks Data Platform is easy as it is free to download and install in your cluster. Single node cluster available as Sandbox is also easy for POCs.
We use Hortonworks Data Platform to analyze time-stamped sensor data with log data.
Pros
First, Hortonworks Data Platform is 100% open source and has no proprietary software. Thus, we don't need to worry about an exit plan.
Next, Hortonworks Data Platform is simple and well documented so that it is easy to implement clusters by following the documents.
Then, Hortonworks Data Platform updates new technologies for everyone as open source frequently.
And finally, it's easy to update new versions of Hortonworks Data Platform.
Cons
Hortonworks Data Platform only provides a Hadoop platform. You must build your application by yourself.
It is difficult to stream process data. Again, you must configure the stream data process by yourself.
Likelihood to Recommend
The key question is does it have an exit plan. Once you use proprietary software, it's very difficult to change platforms and you must pay an annual fee. Hortonworks Data Platform is 100% open source and you can change the platform without worrying about a contract.
