Top 5 Big Data Technologies


The concept of Big Data in the field of data management and analytics has encompassed several new milestones and technology developments over the years. Many Big Data techniques have evolved from the realms of hype to critical differentiators that are being given due attention in this age of the digital world. This is also mainly due to the realization that many Fortune 1000 companies are already incorporating these initiatives in the existing strategies and benefiting from them.

Big Data Trends

Big Data Trends

Let us take a look at the top 5 emerging Big Data technologies that are going to dominate the IT industry for next few years.

  1. Column-oriented Databases: Although the old-school relational databases which store data in rows are excellent for online processing – yet for unstructured data volumes, they are not at par. On the other side, the column-oriented databases have high update speeds that allow huge data compression levels. These databases, which focus on columns, also allow faster query points for unstructured as well as fully structured data.
  2. Hadoop: An implementation of MapReduce, Hadoop stands tall by being the most popular open source platform for Big Data. The reason, it can be used to handle multiple data sources and is flexible enough in order to perform large-scale processing and even reading data from a database. Among its lot of applications, the most important usage is for constantly changing large volumes of data just like: location-based data from weather or traffic sensors.
  3. MapReduce: Being a programming paradigm and an associated implementation, MapReduce is capable of processing large sets of data and putting into practice a distributed algorithm for massive job scalability. Any MapReduce implementation consists of two tasks:
    • The ”Map” task converts an input into tupels.
    • The “Reduce” task combines output of Map task to form a set of tupels. There are several MapReduce variations like Hadoop, PLATFORA and SQL-In-Hadoop.
  1. Better NoSQL: Short for Not Only SQL, NoSQL databases are rapidly gaining popularity over the traditional SQL-based relational ones. Consisting of key-value pairs just like in their counterparts, these NoSQL databases are special-purpose based on performance and light memory storage. There are about 10 to 14 databases currently, each with its own specialty and the number is only growing more.
  2. Hive: Hive is termed as an SQL-like bridge due to its use of in-memory database which speeds up the analytical processing. Originally, Apache Hive was developed by Facebook. But it has been made an open source platform now, owing to its huge success. It conventionally allows business intelligence apps to execute queries using a Hadoop cluster. It also provides a query-based data interface to Apache Hadoop as a high scale data warehouse.

Big Data, in itself, is a quite broad and advanced concept. And due to so many advancements in Big Data techniques and analytics, conditions that allow to integrate these prototypes into successful businesses are the need of the hour. With these technologies and databases, Enterprises can achieve many corporate business heights and realize their value.

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