Likelihood to Recommend
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.
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Hive is a powerful tool for data analysis and management that is well-suited for a wide range of scenarios. Here are some specific examples of scenarios where Hive might be particularly well-suited: Data warehousing: Hive is often used as a data warehousing platform, allowing users to store and analyze large amounts of structured and semi-structured data. It is especially good at handling data that is too large to be stored and analyzed on a single machine, and supports a wide variety of data formats. Batch processing: Hive is designed for batch processing of large datasets, making it well-suited for tasks such as data ETL (extract, transform, load), data cleansing, and data aggregation.Simple queries on large datasets: Hive is optimized for simple queries on large datasets, making it a good choice for tasks such as data exploration and summary statistics. Data transformation: Hive allows users to perform data transformations and manipulations using custom scripts written in Java, Python, or other programming languages. This can be useful for tasks such as data cleansing, data aggregation, and data transformation. On the other hand, here are some specific examples of scenarios where Hive might be less appropriate: Real-time queries: Hive is a batch-oriented system, which means that it is designed to process large amounts of data in a batch mode rather than in real-time. While it is possible to use Hive for real-time queries, it may not be the most efficient choice for this type of workload. Complex queries: Hive is optimized for simple queries on large datasets, but may struggle with more complex queries or queries that require multiple joins or subqueries.Very large datasets: While Hive is designed to scale horizontally and can handle large amounts of data, it may not scale as well as some other tools for very large datasets or complex workloads.
Read full review Pros 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 Maintaining task status using a card-like structure (similar to Kanban system in manufacturing industries) The messaging platform is very effective and similar to Slack The mobile application makes it easy to manage tasks and give updates while travelling on business assignments Read full review Cons 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 Organizing tasks by assignees could be better. It's a little cumbersome to check off each person you want. Can you group these? I don't really use any view besides task view. Is there something better I could be using? It would be nice if attachments showed up in a nicer format, maybe with a preview? Read full review Usability
It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
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The documentation is adequate. I'm not sure how large of an external community there is for support.
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Our CSR is easily accessible and they have support built into the app itself. They also have a pretty robust support site. We also took advantage of the free trial and learned so much by putting Hive through the paces and figuring out the best way to mold it to our needs.
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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.
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Hive is a bit different than
and Monday, which I used mostly. Overall does a great job managing project and helps with team communication. Removes dependency of asking team members for updates by going to conference rooms. With Hive, the team updates the status, and we can easily track it.
Read full review Return on Investment 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 I've gotten to know my colleagues better, knowing their roles makes it faster to contact them to complete tasks and that speed makes us optimize and earn better results The jobs speed made us focus on optimization and customization for the client, and that in a better treatment by the client and better revenue We can understand which tasks takes more time and to stimate better what we can ask for Read full review ScreenShots