What Factors to Consider While Choosing Right SQL-on-Hadoop engine

Every organization today is inclining towards Hadoop, because of its ability to retrieve, analyze and visualize the large datasets efficiently.

CIO Review

Every organization today is inclining towards Hadoop, because of its ability to retrieve, analyze and visualize the large datasets efficiently. As many enterprises look to reap better business insights from big data, it would be careless for them to disregard the advancements of Hadoop alongside SQL.

SQL is the largest workload many organizations run on their Hadoop clusters. That is because— SQL-on-Hadoop brings the combination of an SQL along with scalable Hadoop data framework to facilitate users with querying data in powerful ways and to scale up the storage space on commodity servers. With this converging technology, Enterprises are able to overcome the challenges faced via the flexibility of SQL in order to improve the performance and productivity of enterprises. For instance, big data can be now processed and analyzed efficiently without any difficulties and also the analytic application tool is very simple to learn, unlike the traditional SQL.

Many firms run SQL on their Hadoop clusters to process and analyze data, but not all SQL-on-Hadoop tools are the same. Initially, it was necessary to have in depth knowledge of Hadoop architecture to access the data, but now it can be achieved just by plugging in the reporting tool. Credit goes to SQL-on-Hadoop. This evolution has given way to number of SQL-on-Hadoop engines such as Concurrent Lingual, Cloudera Impala, CitusDB, Hadapt available for use with big data. With so many options, choosing the right SQL-on-Hadoop engine is a crucial decision that can have an enduring impact on the organization’s framework.

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Here are few aspects that enterprises should look for in a SQL-on-Hadoop solution to solve organizations big data challenges.

Quick Interactivity

Enterprises should ensure that analytic tools they choose must support applications in real-time with great agility and better performance. It is important that the tools execute the queries quicker. For instance, once the analytic tool is implemented enterprise must expect quick response upon clicking on reports or virtualization. This constitutes traditional operational applications like social media applications, web, mobile and more, and also real-time operational analytics.

Real-time Data Preparation

Firms need to assess real-time queries on real-time data because there are solutions that argue to be real-time since they execute ad-hoc queries, but it is not real-time if the data is pulled from old ETL (Extract, Transform, and Load). Enterprise must invest on appropriate SQL-on-Hadoop that enhances business value. For instance, SQL-on-Hadoop solutions that lack the ability to update data in real-time is not worth to adapt in the organization.

Ability to Optimize Queries

Formerly, SQL was created to process and analyze highly structured data, whereas Hadoop files had nested data, variable data and self-describing data. Based on the advancements, SQL-on-Hadoop engine must be capable of converting these forms of data into flat relational data and should be able to enhance performance of queries.

SQL Coverage

Almost all the firms have relied upon SQL over the years due to its enterprise standard language such as ANSI SQL. This has encouraged other firms to preserve the standard SQL in their databases and relinquish NoSQL solutions. Therefore, organizations must look for SQL-on-Hadoop vendor that offer complete range of SQL support.

RESTful API and UDFs

Enterprises should look for SQL-on-Hadoop solutions that constitute RESTful API and User Defined Functions (UDFs) that will enable developers to quickly create web-based applications and add custom functionality to the database allowing users to write a code using high-level programming language.

Storage Format

For the betterment of the organization, firms must look for SQL-on-Hadoop solutions that supports standard storage format such as CSV files, Avro Files and more, so that the data can be read by all the tools and can be used to eliminate duplicate data.

Inference

While SQL-on-Hadoop application tool fuels the development of big data market, CIOs should ensure that deployment of these technologies improves the productivity, functionality and performance of the organization. SQL-on-Hadoop distributors are used to make big and false claims trying to manipulate users about their tools performance, it’s better to accept those with a pinch of salt. For those enterprises, that considers SQL-on-Hadoop solution is worth implementing, it is crucial for them to consider the above outlined aspects. List of SQL-on-Hadoop solution vendors can be overwhelming if the enterprises are not sure what they are looking for. Hopefully, these aspects will direct the enterprises to buy an appropriate solution and ensure long term success of the SQL-on-Hadoop engine.

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