Apache Tajo Reviews

As data analyst processing big data on Hadoop, Apache Tajo becomes useful due to its capabilities. It is easy to use and works with SQL like queries, and I can manipulate large volumes of data that are stored in HDFS.

In cases where I want to examine data as it is flowing steadily or where real-time analytics are necessary, Tajo is not the best fit. Due to the batch processing architecture of Tajo, data needs to be pre-loaded which causes latencies for the real time applications.

Tajo enables me to carry out query operations over huge Hadoop data, and express these operations using conventional SQL. This does away with the need to master several MapReduce programming languages and makes analysis of data much easier.

Recommended Solution for an oragnization or individuals who used SQL in day to day activities. It give faster result compare to hive QL.

Nothing dislikes as such, good to professional, beginers might take time to learn its env and installation process is bit lengthy.

We did some POC to create some metrics of a patients in Tajo. Later, we switched to bigquery.

There is no doubt that the ease of using SQL to query huge data sets was attractive. This could be compared with relief as opposed to having to memorize an entire new language for big data analysis. It also has the benefits of being open-source, which meant that I could easily find a plethora of support from developers online.

It was slow during most of the experiments and particularly for complex queries on large datasets. The jobs used to run for hours on end and therefore, it became very difficult to make quick conclusions from the results that I got. This slowly proved disadvantageous to my unit and turned into a bottlenecks.

Apache Tajo provided an ability to analyze large data at a lower cost and, what is more, use standard SQL for that. This could have been a very useful tool for many data exploration requirements we may come across later in other projects.

Our company needed a solid warehouse solution that could bring us up to speed and provide future enhancements. Apache Tajo nailed it!

The procedures to setup and configure took some learning from our IT team. Education is key!

More training videos please!

warehouse management, inventory control, automation of shipping/deliveries

One of the best feature of Apache Tajo is it provides use for multiple data formats including those of standard and it can be processed with very minimal modifications. It has user defined function, which is intuitive to user and makes sure to understand users input well and with ease.It requires less maintenance and it is as simple as it gets. I have been using it for several for my Big data processing purposes including: Warehousing and Analysis of data, Data storage and Data formatting, Log and Data analysis of Large set of Ecom data.

Given reliability on the SQL set of coding, it requires one to be thorough on SQL before using it so it has a dependency on SQL.

It allow me to have a straightaway direct saying over the flow of data and distributed execution for the various strategy in query evaluation and at the same time provides optimization opportunities.Given it is intuitive and ease of its use with the simple design architecture, tolenance in faults and vectorized query for exe plan along to fatser delivery it helps me have better control over data and it's warehousing of the large set of big data.

I find Apache Tajo is a bit easier to navigate than Apache Hive. I also noticed that the results I received from queries were coming in much faster than our previous software.

The software is a bit complicated and has a tough learning curve for users who aren't previously familiar with SQL coding. We had to really onboard our team in order to effectively utilize the system.

Apache Tajo makes it much easier to organize large sets of data. The software is fast and doesn't frequently crash which is a problem we had experienced in the past. I think one of the biggest benefits was the full SQL support: As long as people who are using Tajo are already comfortable with HQL, there's no pressing need for SQL. Although you can easily understand why it's a benefit to have full-sql support.