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Hortonworks Data Platform (HDP) is an open source framework for distributed storage and processing of large, multi-source data sets. HDP modernizes IT infrastructure and keeps data secure—in the cloud or on-premises—while helping to drive new revenue streams, improve customer experience, and control costs.
Hortonworks merged with Cloudera in eary 2019.
- Monitoring console
- Spark data processing
- UI application
- Acl with ranger in service and hdfs data
- Upgrade service version require OS competence
- Search features like cloudera search
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
However, if you just want to try a data science use case, think about if your volume demands such deployment.
- 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.
- 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.
- If you require the newest releases and capabilities and are prepared to upgrade frequently
- If you want full, cell level, ticketing and perimeter security
- If you want metadata, streaming and other PLUS component capabilities
- 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.
- 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.
Hortonworks provides a framework comprising open source projects which is good for any open source lovers. Easy to start with its great tutorials.
- Building and deploying large scale Hadoop/Hive/Yarn/Spark clusters with hundreds of nodes in distributed environments on Cloud
- Process Web/Mobile traffic and provide customizable customer experience
- Advanced Analytics picture of customer behaviors and shopping patterns
- Provide personalized website for each customer based on their needs
- Use it to handle the entire catalog of major retailers
- 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
- 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
- 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.
- 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.
- 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.
- 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.