Amazon EMR (Elastic MapReduce) vs. Hortonworks Data Platform

We are running it to perform preparation which takes a few hours on EC2 to be running on a spark-based EMR cluster to total the preparation inside minutes rather than a few hours. Ease of utilization and capacity to select from either Hadoop or spark. Processing time diminishes from 5-8 hours to 25-30 minutes compared with the Ec2 occurrence and more in a few cases.

Incentivized

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.

Incentivized

• The cluster size of MapReduce is very dynamic and therefore scalability is good for EMR.
• It also works well with other Amazon Web Services like Amazon Simple Storage Service, which means that data can be taken from those services and written back to them.
• I tried using the in-house hosting at the university I work in, but there would be a lot of complications with technical support required. For Amazon, the support and documentation was good to solve these problems faster.

Incentivized

• 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.

Incentivized

• Sometimes bootstrapping certain tools comes with debugging costs. The tools provided by some of the enterprise editions are great compared to EMR.
• Like some of the enterprise editions EMR does not provide on premises options.
• No UI client for saving the workbooks or code snippets. Everything has to go through submitting process. Not really convenient for tracking the job as well.

Incentivized

• 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.

Incentivized

Documentation is quite good and the product is regularly updated, so new features regularly come out. The setup is straightforward enough, especially once you have already established the overall platform infrastructure and the aws-cli APIs are easy enough to use. It would be nice to have some out-of-the-box integrations for checking logs and the Spark UI, rather than relying on know-how and digging through multiple levels to find the informations

Incentivized

I give the overall support for Amazon EMR this rating because while the support technicians are very knowledgeable and always able to help, it sometimes takes a very long time to get in contact with one of the support technicians. So overall the support is pretty good for Amazon EMR.

Incentivized

Try not to change variable names.

Incentivized

Snowflake is a lot easier to get started with than the other options. Snowflake's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.

Incentivized

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.

Incentivized

• It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises.
• It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast.
• It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.

Incentivized

• 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.

Incentivized