Emerging Big Data Technologies
With the Big Data industry set to become an indispensable of part of the global business world, and it is important to be well versed with its potential high value Big Data technology. Here are a few of the emergent Big Data technologies-
1 – The Hadoop Ecosystem
The Apache Hadoop ecosystem has been growing in presence year after year within the industry, and has made itself indispensable to discussion about the Big Data Industry at large, with its open source infrastructure for data processing. It is almost invariably always included in Big Data enterprises around the globe. Almost all major ecosystems incorporate Hadoop into their commercial applications for Big Data. Moreover, according to Zion Market Research, Hadoop technologies are expected to inflate to $87.14 billion by 2022. Key Hadoop vendors include Cloudera, Hortonworks and MapR.
2 – Spark
Spark is a part of Apache’s Hadoop system, designed to make real time processing of data sets a real possibility and has exploded in popularity across the globe, alongside becoming a whole entire category of its own. It exists as a powerful processing engine within the Hadoop system which has about a hundred times faster than the standard MapReduce. The interest in this technology is growing with a sizable number of adopters for the technology and also having a growing number of vendors among their Hadoop offerings who offer products developed on Spark and its applications.
3 – R
This is a programming language and software infrastructure that allows us to work closely with statistics and analysis. IT is managed by the R foundation, and it is an industry mainstay that’s available under a GPL 2 license. Popular IDEs like Visual Studio and even eclipse support this language on their infrastructure, resulting in a huge surge in its popularity in the data science industry. In fact, outside of the industry, it has also quickly grown into one of the most popular languages in the world. This is significant as the languages that usually find themselves on top are general programming languages that may be used for different types of processes and applications. Rarely has a Big Data language been pushed to the forefront despite having a specialized application. This demonstrates how important it is to the industry and the potential it provides its adopters.
4 – Data Lakes
Global corporations produce massive amounts of data every day, and many of them invest significantly in data lakes, which are huge data storage repositories which contain data from several sources and keep them in their natural form. They differ from data warehouses, which disparagingly stores and additionally processes and structures it for storage. Data lakes, however, consists of untouched data, that can be utilized or accessed whenever. It is particularly useful for companies that produce large amounts of data and the storage of data they aren’t sure what to do with yet.
5 – NoSQL Databases
Most data management systems stores its contents in defined rows and columns to be easier to list, access or interact with. Data science professionals use these systems to access, interpret and manipulate these storages with the use of a special language- SQL. The trend has grown over the last few years and there are several popular NoSQL data bases that have come up like MongoDB Cochbase and Cassandra, etc. and even RDBMS vendors have also developed Oracle to adopt the practice. Big names like IBM have also begun offering NoSQL databaseses. As per Allied Market Research, the NoSQL market looks prepped to be worth $4.2 billion by 2020.