Hive

Hive is great for managing projects with your team. Assigning tasks is simple enough using Hive. It helps manage team goals for the projects. We are able to create reports (via the dashboard) for the progress and updates to provide to the team based on completed stages. Works great for bigger projects.

Incentivized

• 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.
• 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.
• Integration with other tools: Hive integrates with a wide variety of other tools and services in the Hadoop ecosystem, such as Pig, Spark, and HBase, allowing users to perform a wide range of data analysis and management tasks.

Incentivized

• 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?

Incentivized

Its a easy tool, the best way to organize the workflow but has room for more improvements.

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.

Incentivized

One key difference between Hive and Spark is the way they process data. 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. In contrast, Spark is a real-time processing platform that is designed to handle streaming data and support interactive queries. Another difference is the way they execute queries. Hive uses a SQL-like query language called HiveQL, while Spark supports a wide range of languages and APIs, including SQL, Python, Scala, and R. But we chose Hive due to its simple queries on large datasets and for data warehousing tasks.

Incentivized

• 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

Incentivized