Hadoop is an open source software from Apache, supporting distributed processing and data storage. Hadoop is popular for its scalability, reliability, and functionality available across commoditized hardware.
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Hortonworks Data Platform
Score 5.0 out of 10
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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.
Apache Hadoop (and its subsequent add-ons) are well-suited to larger, unstructured data flows, such as aggregation of web traffic or advertising. Geospatial algorithms and their outputs are well-suited for this kind of aggregation as structuring that data is challenging, but leaving it unstructured and performing queries as-needed is a better fit for most business models. With the advent of data science, I would expect Hadoop fits a LOT of their initial outputs quite well.
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.
Manage our data in hdfs is simple; you can interact with server with REST API.
Hadoop is a batch oriented processing framework, it lacks real time or stream processing.
Hadoop's HDFS file system is not a POSIX compliant file system and does not work well with small files, especially smaller than the default block size.
Hadoop cannot be used for running interactive jobs or analytics.
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.
Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free
Great! Hadoop has an easy to use interface that mimics most other data warehouses. You can access your data via SQL and have it display in a terminal before exporting it to your business intelligence platform of choice. Of course, for smaller data sets, you can also export it to Microsoft Excel.
We went with a third party for support, i.e., consultant. Had we gone with Azure or Cloudera, we would have obtained support directly from the vendor. my rating is more on the third party we selected and doesn't reflect the overall support available for Hadoop. I think we could have done better in our selection process, however, we were trying to use an already approved vendor within our organization. There is plenty of self-help available for Hadoop online.
I feel that this is a highly reliable and scalable solution computing technology that is highly capable of processing large data sets across multiple servers and thousands of machines in a well-defined and distributed manner. Apache Hadoop can automatically scale up the number of servers and machines that are needed to process, store, and analyze data sets. It also handles explosions in data with big data technology. Apache Hadoop is good at handling all node failures as well.
While Apache Hadoop is completely open sourced, Hortonworks Data Platform offers support as well as keeps pace with the open source versions. Also, the HDP open sources its own products, thus giving back to the community. I find using the Hortonworks Data Platform more intuitive than Cloudera or MapR versions.
As it was open source makes it popular choice for handling large chuck of datasets
It was free earlier but now it’s licensed but still enterprise is a fine tuned version which makes it easier for new users and administrators to use it
Our investment is worth every single penny.
Initial cost is more as you might need to hire administrators to setup the cluster and make them in scalable. But once done it’s pretty easy
It provides a convenient way of quickly setting up a big data environment, easily setting up clusters with different configurations. It provides several security architectures that can be used as well. Since it provides a big list of components and packaged together, it is a great tool for companies to get set and utilize it for their use cases.
Since it uses Ambari extensively to install, upgrade and manage software, it is very convenient and easy to support and operationalize the components. Alerting and notifications, ability to create custom alerts give you the capability to add any number of alerts to meet your custom needs. It provides a great way to maintain other software by creating mpacks and the ability to add custom code, and you can add other software to be managed in a centralized tool.
The use and support of popular and useful open source software and the company's contribution to the community makes HDP a very useful tool that enables a quick, secure, easily maintainable suite of components that can help companies meet the needs of the business. What is great is that new components keep getting added based on any new useful tool that comes available, like Druid, and made available as part of the suite of components. That helps businesses keep up with new capabilities as they become available, and use them to solve their problems.
