Apache Hive vs. TeamDesk

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

TeamDesk is a phenomenal service for our practice. Each legal practice area has unique characteristics that are important to successful practice. Generalized practice management services do address those unique elements because they are created for a mass audience. What makes TeamDesk so helpful is that it can be customized to account for any unique issue that we confront. We use TeamDesk for matter management. It can handle general issues like billing, matter notes, etc.; but it also allows us to track specific details unique to our practice area across each matter and make comparisons that are useful to our practice. Additionally, we have been able to customize it to facilitate our client development efforts.

• 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 ease of customization has been very helpful. As we use the system longer and decide what works for us and what needs changed, it's a matter of moments to be able to make any desired changes. Much more flexible than anything else we've used in the past.
• The customer service has been excellent. Any issues that have arisen have been quickly and fully addressed.

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 only down side that I see, is that you have to be willing to spend some time creating your database. You start with a blank canvas and all the tools you need to design a custom database. But, you have to take the time to do it. If you take that time, it is well worth it.

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

They are simply amazing. Very fast, like I have a personal support person standing by for me and always solves my problem in one go.

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

Prior to Teamdesk we used Excel, an Access database, an online time and expense tracking system, and Dabble DB. Excel and Access had limitations with simultaneous access on the LAN, the online T&E system did only that and was not flexible, and Dabble was flexible, but how can you rely on a company whose goal is to exit, when you want to run your business on their platform. Teamdesk has everything we need.

• 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

• We are just starting, but I believe it will help to manage our projects more effectively and organize our documents better.