Apache Hive

Improved performance compared to a database management system.

HDFS-based distributed processing greatly improves scalability.

Improved query performance compared to oracle databases.

It gave ease of use and performance wise to read/insert large set of data.

It helped to improve site performance to load faster

Reduce time to market

Improve client satisfaction

Saves money :)

Good ROI for being able to access data easily across the network, we have large amounts of data and this is a good system to access it

Good ROI for being easy to learn how to use for new employees, not much time spent which saves costs

Good ROI for being able to integrate with Spark and other applications, hence data can be analyzed through programs

When developing projects you will obtain correct figures and true information about what you need or what you have to develop.

We are currently trying to integrate with other tools so the software will help us.

fast results

reduced time complexity

code debugging is easy

It used to be complicated by the many lines of connectors, but today you just need to understand where to click

The software ensures the quality of the data, prepares and cleans it

Unable to work in OLTP environment is challenge

Real time query is not supported currently

Optimization for query is not proper.

Apache hive is secured and scalable solution that helps in increasing the overall organization productivity.

Apache hive can handle and process large amount of data in a sufficient time manner.

It simplifies writing SQL queries, hence helping the organization as most companies use SQL for all query jobs.

Very easy to write query.

Not suitable OLTP environment but for OLAP.

HiveQL is a declarative language like SQL.

Apache hive helped to manage data on HDFS.

Apache hive helped to do data cleansing and data transformation.

Apache hive queries were slow, so we had to use Impala (MPP) for exposing the data to end-users.

Improved the performance than a traditional DBMS.

Scalability is much better due to support of HDFS distributed processing.

Made queries much more efficient than a traditional database such as oracle.

Have to maintain on premise hardware is one dependency.

It's one of the top data warehouse solution

All the metrics computation and adhoc analysis is done using Apache Hive

We were able to achieve huge speed-up in the area of data insertion.

Data replication for disaster recovery is finally working for our customers.

A good engine for data analysis

Easy syntax lead to fast learning for NLP team.

Shifted to spark later on which supports almost all Hive functions and was faster

Hive is crucial for our BI and reporting.

Hive costs can ramp up quickly and can cause negative ROI when implementing inefficient schemas, queries, etc.

It exposes the distributed calculation world (Hadoop) to the users but doesn't require the user to have the in-depth understanding of boilerplate details, it reduces the time of learning and let the data analyst can focus their efforts on the core business.

We did not face a ROI problem with Apache Hive - as its open source.

Finding information is easier

Fault tolerant

Reliability of data

No licensing costs

Little training needed for most users

Installation and set up of the clusters is easy.

Effective handling of the complex queries and large set of data.

Helps to get good data insights from a vast and complex data stored

It's easy to learn HiveSQL

You don't have to worry about scalability as much with Hive

It saves time on development because of its SQL-like syntax

It makes it easy to access big data

It's easy to connect reporting tools to Hive and build reports on top of it

Allows analysts to use their SQL skills against large datasets.

Slow queries allow for opportunities to discover bottlenecks, parameters to tune, and alternative tools or ways to architect a system.

Positive impact for faster response time compared to other products

Can handle large sets of data and complex queries