Apache Hive vs. Azure HDInsight

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

If you want to save costs and just pay for what you use, I highly recommend it. It will help you also to work with data for your reports and analytics. on the other hand I think it could be the subscription you have but high volume of data make it slow but not so much. anyway I think it's really good because it's from Microsoft which always is friendly to use it as all the suit they have.

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

• Shows live changes in analytics. Shows you how social media is working for us. Since we promote weekly events this is something that we really need to pay attention to.
• Azure in itself is very user-friendly, HDInsight is a great addition. For our purposes, we definitely also utilized the power query to translate data to Excel.

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

• Had issues while using Azure ARM Templates for deployments.
• The cost structure suited for high end temporary data analytics, rather than a traditional frequently queried data warehouse.

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

Azure HDInsight is usable on the top of Azure Data Lake and gives us the benefit of analyzing large scale data workload in Hadoop. Usability and support from Microsoft are outstanding.

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

Inexpert, isolated teams... not good for support an excessively complex platform. Lots of weeks or months for a complex problem troubleshoot. Many time lost stuck on MindTree, before the case was finally escalated with Microsoft!

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

Many times you just need spark performing fast and cheap. Azure HDInsight Includes lots of features and not required software. Also its libraries and runtime versions are pritty old. But, what is great Is you don't need to have an expert in your team and things -when work- performs always in the exact same way. Also, as I mentioned, for a starter that's a great ROI.

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

• ROI is of course there, as no legacy software for data presentation.
• No manual intervention for data retrieval.
• Data is available anywhere as requested.

Incentivized