Big Data has evolved a lot over the years. The definition of what is Big Data or how big is Big Data has seen drastic over time. Forbes in its article ‘A Very Short History Of Big Data’ shows the journey of big data through its major milestones. The ways to store and analyse evolved as the size of big data increased. Today, the term ‘Big Data’ is associated with a number of big data technologies which have developed through the years. However, some Big Data technologies decimated over time. There are a few technologies that have not only remained relevant but have also expanded.
Here is a list of top 5 technologies that are the most relevant today!
1. NoSQL databases
Originally, databases stored structured data in tables containing rows and columns. These are also called Relational Databases or SQL Databases. However, a need to store data in any format emerged. So, NoSQL databases emerged as a new, wide variety of database technologies that stored unstructured data.
- Document: As the name suggests, it stores information in a document. The exact definition of a document differs from database to database.
- Graph: Graph databases store information in nodes, which can then be connected to other nodes. They make highly connected databases and complex queries easier and faster.
- Columnar database: This database is a lot like Relational Databases. But instead of storing data in a row-oriented manner, they store data in a column-oriented manner. It makes certain operations much faster as compared to that of traditional databases.
- Key document Value database: It is the simplified version of the more robustDocument Database. Here, each entry is a simple key-value pair. It is the simplest and is easy to implement.
To know more read our article on ‘NoSQL: A Beginner’s Guide’
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. Moreover, it provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.
However to understand the meaning we need to understand the terms used in the definition.
- Open-source software- In general, open source is any program whose source code is made available for use or modification as users or other developers see fit. Open source software is usually developed as a public collaboration and made freely available. Hadoop being an open-source software allows anyone to access it
- Framework-It means everything needed to develop and run software applications is provided – programs, connections, etc
- Massive storage-The Hadoop framework breaks big data into blocks, which are stored on clusters of commodity hardware.
- Processing power- Hadoop concurrently processes large amounts of data using multiple low-cost computers for fast results
To know more read our article on ‘Apache Hadoop: An Overview’
Hive is an SQL-like interface to query data which is stored in various databases and file systems that integrate with Hadoop. Moreover, it provides an SQL-like (Hive Query Language) interface for users to extract data out of Hadoop system. Anyone having decent working knowledge of SQL will be able to use it quickly. However, it is not an Online Transaction Processing tool. Hive does not provide a row-wise update and insert, which is the biggest disadvantage of using it. It is, however, closer to Online Analytical Processing.
It provides an SQL like interface to data stored in HDP. Hive has three main functions:
- Data Summarization
- Data Analyses
To know more read our article on ‘Apache Hive: A Beginner’s Guide’
Apache Spark is an open source processing engine built around speed, user ease and sophisticated analytics. Apache Spark is mainly a parallel data processing framework that can work with Apache Hadoop, making it develop fast. Leading to an ease of streaming and interactive analysis on all your data.
To know more read our article on ‘The Ultimate Cheat Sheet to Apache Spark!’
It is a fast, scalable, durable and fault-tolerant, publish-subscribe messaging system. Kafka supports a wide range of use cases as a general purpose messaging systems. Some of them include:
- Stream processing
- Website Activity tracking
- Metrics collection and monitoring
- Log Aggregation
These technologies have been a game changer for Big Data Analysis. They have helped its users handle and analyse Big Data in a cost effective manner. They are the technologies that are here to stay and expand.