8 Tools You Can Use to Analyze Big Data

An essential part of modern business, no matter what the industry, is Big Data - sets of copious amounts of data that reveal much in terms of trends and patterns regarding human behavior and interaction. And This Big Data needs to be analyzed, of course, and companies require both the software to do this, and the people who are able to operate this software. That is one of the major reasons that skillsets are changing in terms of employees, and attributes that were valuable in the past are being replaced with those that complement an organization’s niche. Big Data analytics is big news. And so businesses seek individuals who can be termed data scientists. How can you upskill to ensure you possess these integral-yet-supplemental skills that are so in demand. This article will help in terms of the tools - the software - you need to familiarize yourself with if this is a step you are seeking to make 1. Hadoop Haddop is an open-source tool which has become somewhat of a staple for data scientists. Not only does it offer an almost unrivalled amount of Big Data storage, the power of its processing capabilities delivers a vast array of analytical possibilities that businesses can utilize. Java is of course integral to Hadoop, and a decent understanding of this programming language is highly recommended, but the time and effort required to get you there is undoubtedly worth it in the end as knowledge and experience of Hadoop truly puts you at the forefront of the Big Data analytics game. 2. Infatica First up, an explanation. Often the best way of obtaining all the data that you require to be mined is through the process of scraping. Simply, scraping is the activity of bots visiting specific sites - be they private business sites, social media channels or even search engines - to get the information that is required. There are innumerable numbers of these scraping bots in the jungle that is the world wide web, and most website owners will do anything in their power to stop this from happening - after all, their data is valuable them. So what do they do? They implement blocks which prevent data being scraped, or may even seek to mislead you with inaccurate data that has been deliberately planted. The solution here for those who require the information, in most cases, is a residential proxy, which allows you to visit sites under the premise of a residential user, not a bot. In this way, Infatica is an incredibly useful tool to achieve just that. 3. MongoDB The best way to describe MongoDB is as an alternative to the much-loved traditional database. Not all data can be stored in such structured vehicles, however, and some a modern version of the structured database is required for those sets of data that are often anything but structured. This may include, but is not limited to, data that is extracted from mobile applications, and data scraped from content management systems. Then there are product catalogs and other types of semi-structured data sets that traditional databases cannot store. That’s what MongoDB steps in, although to use it requires detailed appreciation of the product from its most fundamental stage, and working on queries can also be tricky. 4. Cassandra Cassandra was actually first developed by Facebook, that behemoth of the social media scene, in order to operate as a NoSQL solution. Since then, other digital giants such as Twitter and Netflix, to name but two, have jumped on board, making Cassandra one of the most widely-used and highly respected tools for the handling of Big Data. Quite simply it is a distributed database which brings the dual benefits of having the capacity to deal with huge data sets on what are known as commodity servers, and it is incredibly high-performing. If it’s good enough for Facebook and Twitter, after all, it must be doing something right. 5. Drill Drill is yet another open-source framework, this time developed by Apache) aimed at huge datasets, this time facilitating the ability of professionals to perform highly interactive analyses. Drill was conceived as a columnar query engine which has the capacity to scale servers and processes to the tune of more than 10,000 - that equates to literally petabytes in seconds. It is highly responsive and compatible with big players such as Google Cloud, MongoDB and Amazon S3, among others. 6. Elasticsearch Elasticsearch is actually an enterprise search engine that is delivered by open-source and programmed in Java, yet licensed by Apache. It’s prime function is to facilitate eye-wateringly quick searches in the support of apps which facilitate the discovery of data, although it has other capabilities too. 7. Oozie Oozie is more than just fun to say, as it is actually among the best-performing workflow processing systems supporting a huge array of languages and tasks. Also developed by Apache and primarily developed to support Hadoop jobs, and has the convenient ability to facilitate concurrent workflow processes across languages and dependencies. 8. Storm Storm is another highly valuable tool in assisting in the support of those ever-more-common unstructured datasets that can be scraped from a variety of places, yet are highly prized in terms of mining. The most amazing feature of Storm is that it is compatible with any programming language, and its reliability is unrivalled. Yet another Apache product, Storm was purchased by Twitter and is now run as an open-source and distributed framework which operates in real time. It is an essential piece of equipment within a data scientist’s toolbox. And there we have it: eight of the most highly sought-after tools within the realms of Big Data storage and analysis. Knowledge of any of these tools will put you on the right path in terms of career opportunities, yet knowledge and capabilities in terms of them all will put you at the forefront of the Big Data revolution. Training is available on all of these tools which will allow you to become a much desired certified expert: it’s a move you will not regret. Image credit: Unsplash