Apache Hive vs. IBM Netezza Performance Server

Apache Hive shines for ad-hoc analysis and plugging into BI tools. Its SQL-like syntax allows for ease of use not for only for engineers but also for data analysts. Through our experience, there are probably more desirable tools to use if you are planning on integrating Hive into your processing pipeline.

Incentivized

Netezza Performance Server enhances the data scientist's ability to rapidly design, train, and deploy machine learning models in the database. Netezza Performance Server is essential for implementing cognitive machine learning, providing data scientists with in-database analytics, and enabling them to collaborate on a single platform. Netezza Performance Server supports the use of built-in Spark as well as Python and R for rapid and sophisticated data science and machine learning adoption at scale.

Incentivized

• Hive syntax is almost like SQL, so for someone already familiar with SQL it takes almost no effort to pick up Hive.
• To be able to run map reduce jobs using json parsing and generate dynamic partitions in parquet file format.
• Simplifies your experience with Hadoop especially for non-technical/coding partners.

Incentivized

• The speed of operations is really second to none
• The data control aspect is really effective and very useful
• Stability and no latency issues notices during operations

Incentivized

• Use Hive for analytical work loads. Write once and read many scenarios. Do not prefer updates and deletes.
• Behind scenes Hive creates map reduce jobs. Hive performance is slow compared to Apache Spark.
• Map reduce writes the intermediate outputs to dial whereas Spark operates in in-memory and uses DAG.

Incentivized

• The cost is expensive for medium-sized and smaller companies.
• IBM could bring back the technical account managers.
• The procurement process is a hassle.

Incentivized

Since I do not know the second data warehouse solution that integrate with HDFS as well as Hive.

Hive is a very good big data analysis and ad-hoc query platform, which supports scaling also. The BI processes can be easily integrated with Hadoop via the Hive. It can deal with a much larger data set that traditional RDBMS can not. It is a "must-have" component of the big data domain.

Incentivized

Apache Hive is a FOSS project and its open source. We need not definitely comment on anything about the support of open source and its developer community. But, it has got tremendous developer support, awesome documentation. I would justify the fact that much support can be gathered from the community backup.

Incentivized

We have used a simple but necessary function such as merging certain data tables, which although they may be from different areas, complement each other or are necessary, you can use metadata if what you need is to validate the origin of your information and what impact it has, is also feasible.

Incentivized

With a simple user interface and
access to IBM's online instructions, Netezza Performance Server is easy to
operate. It also has built-in contribution facilities, as well as good customer
support, to make the DBA's job easier.

Incentivized

• 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

Incentivized

• It allows us to work together simultaneously in the database to query our custom results.
• It requires users to know the object name as it does not pop up in their text editor. Thus, users take more time in writing queries.

Incentivized