3 Reasons Why In-Cluster Analytics is the Next Big Deal

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Recent technological advancement has seen a lot of sectors improve and the analytics sector has not been left behind. The latest advances in the Apache Hadoop system is a major improvement to the Hadoop’s viability as far as data storage is concerned. With these advances, it is clear that SQL software on Hadoop is the best way to access big data which confirms the authenticity of the concept of analytics in Hadoop system. Of course, this is no doubt a big deal, and it has shown how technology has evolved with the intention of fulfilling the potentials of big data analytics.

That said, there are a few reasons why in-cluster analytics is being classified as the big deal today. We highlight some of the top three behind this buzz.

1.      Most companies need it collect, analyze and transform data

If you have huge data to analyze, how do it? First, you can decide to use Hadoop to collect and transform data. Then you can transfer that data into a diagnostic database for further analyses. This guarantees fast and powerful data analysis. The process may be a little bit complicated and pricey, but you get more data as volume grows.

Second, you can opt to analyze the data using the SQL on the Hadoop system. This eliminates the task of moving data from the source to a different database. Regrettably, getting the work done hasn’t been that easy, perhaps there will be more improvement on this.

2.      Most technologies are relying on it

For the past year, there have been major enhancements in the SQL system on Hadoop technologies. We have seen some performance enhancements being added, compatibility with SQL dialects, new functions, aliases for different types of data, and an improved support system for storage setups. These upgrades have enabled for the existence of a comprehensive support system for JOIN clauses as well as EXPLAIN queries in SQL.

Aside from that, the team has also added new features to track the amount of memory usage and manage hard tasks, security features, and enquiry plans. This has attracted a great number of organizations who want to try out the system.

Some of the key upgrades in this space include Apache Hive, Apache Presto, Apache Spark, and Cloudera Impala. They are meant to give the users a whole new experience in the analytics environment.

3.      It accomplishes the potential of big data

The major principle of big data, in this case Hadoop, is the concept of schema-on-read. This means that if you have to put your data in a single system, this system shouldn’t enforce some schema on that data. Rather, the scheme should be applied as you conduct data analysis meaning that you possess the right to define or redefine that plan based on what you are learning and any changes that may occur.

Schema is an important principle of Looker that was created to facilitate fast and flexible in-databases analysis. The latest improvements on the Hadoop’s SQL system has seen all this change. Looker can now enable users to analyze and process data fast and easier.

So, if you have huge amount of data in Hadoop cluster and you want to analyze it as fast as possible, the tasks becomes easier. There is no limit against the amount of data you are moving or the amount you can afford to spend on this process. Looker makes the process easier and it helps you to expand and accelerate what you want to do. It will also save time and thousands of dollars that could have been spent on the process.

These reasons prove that in-cluster analytics is no doubt a big deal. Perhaps it’s time every operating organization decided to embrace the big data analytics and makes data analysis easier.