Some of the Top SQL-on-Hadoop Tools with Pros and Cons

Hadoop ecosystem now serves as a comfortable home to Big Data now, and the Hadoop data stores now have a greater acceptance across the world by programmers, developers, data scientists, and database management experts. These ecosystems are as convenient as the data storages; however, the inherent reporting system of Hadoop poses a few challenges for the users to overcome. The prominent problem among these is to need to learn a new language to query and report on the Hadoop data sets and manage its performance-related matters.

Enormous Data has discovered an agreeable home inside the Hadoop biological system. Hadoop based information stores have increased wide acknowledgment around the globe by designers, software engineers, information researchers, and database specialists. Advantageous as the information stockpiling seems to be, Hadoop’s natural detailing component presents various difficulties to be survived, the conspicuous ones being taking in another dialect without any preparation and execution related issues.

Since the rise of the Hadoop biological system, developers and investigators having long stretches of involvement in SQL – unexpectedly felt injured with the new large information innovation. They were required to get familiar with another questioning language once more to successfully use the advantages gave by Hadoop. SQL being the most mainstream database language, the need of great importance was to consolidate the huge stockpiling limit of Hadoop with SQL, coming about into a SQL–on–Hadoop device which would empower software engineers to uncover applicable information from a regularly developing Hadoop vault.

As the popularity of Hadoop ecosystem is increasing in the vogue of Big Data, the developers and analysts who are used to the traditional SQL databases for decades now feel crippled. The big data technologies instantly pose confusion among them with the need to learn a totally new query language to gain control over this new technology. To handle this situation effectively, the need of the situation is to combine Hadoop’s enormous storage capacity to the comfort of SQL, which paved the way to a tool like SQL–on–Hadoop which enables the developers to get relevant data from the Hadoop data repositories.

SQL-on-Hadoop tools

There are many new tools to handle SQL–on–Hadoop, which let the programmers use their existing SQL knowledge to handle the Hadoop databases. The whole objective is to put a very comfortable and user-friendly front-end query which is SQL based to explore the vast store of data under the Hadoop ecosystem. As the need of the hour is to find out the best tools to be used for this purpose, in this article, we will discuss a few popular tools and the pros and cons of each for the business Big Data users.

1. Apache Hive

The tools were actually made by Facebook, first for their internal database management tasks, but later gained momentum and soon became a favorite choice for the SQL users to run queries on Hadoop databases. Even though the Hive offers an environment which is more like SQL, the tool uses MapReduce methodologies at the backend for querying databases and returns the results accordingly. It can also support the user-defined functions and process compressed data too. If you are not much bothered about the performance algorithms, then it could be one best tool for you to get your tasks done without hassle.

Apache Hive is also the de-facto tool present on various Hadoop installations. Provided it has only a very minimal investment to start with; the Hive becomes a very good choice for the beginners to try. One major issue you may probably face with Hive could be that the queries may sometimes be very slow to execute as MapReduce is usually associated with some overheads.

Along with many benefits it offers, as RemoteDBA points out, the Hive has some limitations too. It could only support limited file formats like text, ORC, SequenceFile, and RCFile, etc. Even though it could cover most of the popular formats of files, this needed to be considered in light of the data loads you have. Even with some limitations now, the future of Apache Hive is certainly very bright as the provider is trying to consistently improve its efficiency with the Tez project, which will be a backend for the Hive to reduce the response time.

2. Cloudera Impala

Impala can be used by developers to run more interactive SQL queries on the HDFS (Hadoop Distributed File System) and HBase applications. Even though Hive offers an interface which is more like SQL, it features a batch processing approach which may further cause some lags, making it troublesome for someone looking for greater performance alternative. This shortcoming could be easily overcome with the help of Impala, which can run queries almost real-time and allow a better of the SQL-based BI tools in Hadoop data systems.

Offered by the provider Cloudera, it is not available as a free, open-source tool, which can support almost all file formats like text, SequenceFile, LZO, RCFile, and Avro, etc. Impala can also support cloud-based architecture using the Elastic MapReduce from Amazon. The ANSI SQL compatibility of Impala will also make sure that there is an only minimum disruption in case of business and the analysts and developers could be optimally productive from day #1 onwards. To handle this, they also need not have to learn any new language. Impala offers easy integration to many other BI tools too to ensure continuity at work.

Starting with Impala also requires some groundwork. To unleash its fullest potential, you should store your data in the Parquet format. This may be a bit troublesome, though. Along with this, the need for installing demons all across clusters and also limited support for YARN maybe some other bottlenecks in Impala’s use.

3. Presto

This is also another tool by Facebook for SQL-on-Hadoop, which comes as an open-source tool. Is written in java language and has many similarities with Impala like:

• Both offer a very interactive user experience.
• Both require a fair amount of groundwork, i.e., installing across many nodes.
• To ensure peak performance, both Impala and Presto require the data to be stored onto some specific file formats.

Along with these, Presto can also provide interoperability with Hive. You can easily combine the data from various sources to Presto, which is found to be a major advantage in terms of enterprise deployments. The biggest difference between Impala and Presto is that any leading vendors do not support the latter. So, if you are planning for enterprise-wide deployment and need consistent, ongoing support, then you may better consider some other options. On the other hand, even the most data-intensive technology giants like Dropbox and Airbnb are using Presto lately.

Organizations now are more data analytics oriented and demand a better performing query language to effectively handle the machine learning and statistical algorithms like factor analysis, regression, testing of hypothesis, etc. With a lot of options in terms of SQL-on-Hadoop tool available in the market, we can always expect that the SQL masters could ideally benefit from the tools mentioned above and hit the ground faster.

About the Author

Karen Anthony is a Business Tech Analyst. She is very responsible towards her job. She loves to share her knowledge and experience with her friends and colleagues.