Likelihood to Recommend 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.
Read full review Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.
Read full review Pros Amazon Elastic MapReduce works well for managing analyses that use multiple tools, such as Hadoop and Spark. If it were not for the fact that we use multiple tools, there would be less need for MapReduce. MapReduce is always on. I've never had a problem getting data analyses to run on the system. It's simple to set up data mining projects. Amazon Elastic MapReduce has no problems dealing with very large data sets. It processes them just fine. With that said, the outputs don't come instantaneously. It takes time. Read full review Its performance, ease of use, and simplicity in learning and deployment. Using this tool, we can quickly analyze large amounts of data. It's adequate for map-reducing large datasets and fully abstracted MapReduce. Read full review Cons 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. Read full review UDFS Python errors are not interpretable. Developer struggles for a very very long time if he/she gets these errors. Being in early stage, it still has a small community for help in related matters. It needs a lot of improvements yet. Only recently they added datetime module for time series, which is a very basic requirement. Read full review Usability I give Amazon EMR this rating because while it is great at simplifying running big data frameworks, providing the Amazon EMR highlights, product details, and pricing information, and analyzing vast amounts of data, it can be run slow, freeze and glitch sometimes. So overall Amazon EMR is pretty good to use other than some basic issues.
Read full review It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
Read full review Support Rating There's a vast group of trained and certified (by AWS) professionals ready to work for anyone that needs to implement, configure or fix EMR. There's also a great amount of documentation that is accessible to anyone who's trying to learn this. And there's also always the help of AWS itself. They have people ready to help you analyze your needs and then make a recommendation.
Read full review The documentation is adequate. I'm not sure how large of an external community there is for support.
Read full review Alternatives Considered 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.
Read full review Apache Pig might help to start things faster at first and it was one of the best tool years back but it lacks important features that are needed in the data engineering world right now. Pig also has a steeper learning curve since it uses a proprietary language compared to Spark which can be coded with Python, Java.
Read full review Return on Investment Positive: Helped process the jobs amazingly fast. Positive: Did not have to spend much time to learn the system, therefore, saving valuable research time. Negative: Not flexible for some scenarios, like when some plugins are required, or when the project has to be moved in-house. Read full review Higher learning curve than other similar technologies so on-boarding new engineers or change ownership of Apache Pig code tends to be a bit of a headache Once the language is learned and understood it can be relatively straightforward to write simple Pig scripts so development can go relatively quickly with a skilled team As distributed technologies grow and improve, overall Apache Pig feels left in the dust and is more legacy code to support than something to actively develop with. Read full review ScreenShots