Big Data : The Essence

Big data databases store petabytes of unstructured, semi-structured and structured data without rigid schemas. They are mostly NoSQL (non-relational) databases built on a horizontal architecture, which enable quick and cost-effective processing of large volumes of big data as well as multiple concurrent queries.

1) What is Big Data technology?Big data is a specific indicator for the vast assembly of data, increasing enormously in size and exponentially with time. Big Data Technologies can be defined as software tools for analyzing, processing, and extracting data from an extremely complex and large data set with which traditional management tools can never deal.

2) What are the types of Big Data technologies?Big Data Technologies are broadly classified into two categories.

Operational Big Data Technologies

Operational Big Data Technologies indicates the volume of data generated every day, such as online transactions, social media or any information from a particular company used for analysis by software based on big data technology. It acts as raw data to supply big data analysis technology. A few Operational Big Data Technologies cases include information on MNC management, Amazon, Flipkart, Walmart, online ticketing for movies, flights, railways, and more.

Bookings for trains, flights, buses, movies, etc. online using an online booking system. Online trading or shopping on e-commerce websites such as Amazon, Ajio, Myntra, etc. Online data from social networking sites such as Instagram, Facebook, Messenger, WhatsApp, etc. Executive details or Employee data of MNCs.Analytical Big Data Technologies

Analytical Big Data Technologies concerns the advanced adjustment of Big Data Technologies, which is rather complicated than Operational Big Data. This category includes the real analysis of Big Data, which is essential to business decisions. Some examples in this area include stock marketing, weather forecasting, time series, and medical records analysis.

Stock marketing data. Weather forecasting data. Maintaining detailed space mission databases where each and every detail about a mission is mentioned. Medical records allow doctors to monitor the health status of a patient. Let’s take a look at the top 5 Big Data technologies being used in IT Industries.

3) Top 7 Big Data technologiesHadoop Framework was developed to store and process data with a simple programming model in a distributed data processing environment. The data present on different high-speed and low-expense machines can be stored and analyzed. Enterprises have widely adopted Hadoop as Big Data Technologies for their data warehouse needs in the past year. The trend seems to continue and grow in the coming year as well. Companies that have not explored Hadoop so far will most likely see its advantages and applications.
Artificial Intelligence is a broad bandwidth of computer technology that deals with the development of intelligent machines capable of carrying out different tasks typically requiring human intelligence. AI is developing fast, from Apple’s Siri to self-driving cars. As an interdisciplinary branch of science, it takes into account a number of approaches, such as increased Machine Learning and Deep Learning to make a remarkable shift in most tech industries. AI is revolutionizing the existing Big Data Technologies.
NoSQL includes various Big Data Technologies in the database, which are developed to design modern applications. It shows a non-SQL or non-relational database providing a method for data acquisition and recovery. They are used in Web and Big Data Analytics in real time. It stores unstructured data and offers faster performance and flexibility while addressing various data types—for example, MongoDB, Redis, and Cassandra. It provides design integrity, easier horizontal scaling, and control over opportunities in a range of devices. It uses data structures that are different from those concerning databases by default, which speeds up NoSQL calculations. Facebook, Google, Twitter, and similar companies store user data on terabytes daily.
R is one of the open-source Big Data Technologies and programming languages. The free software is widely used for statistical computing, visualization, and unified development environments such as Eclipse and Visual Studio assistance communication. According to experts, it has been the world’s leading language. The system is also widely used by data miners and statisticians to develop statistical software and mainly data analysis.
Data Lakes means a consolidated repository for storage of all data formats at all levels in terms of structural and unstructured data. Data can be saved during Data accumulation as is without being transformed into structured data. It enables performing numerous types of Data analysis from dashboards and Data visualization to Big Data transformation in real-time for better business interference.Businesses that use Data Lakes stay ahead of their competitors’ game and carry out new analytics, such as Machine Learning, through new log file sources, data from social media, and click-streaming.This Big Data technology helps enterprises respond to better business growth opportunities by understanding and engaging clients, sustaining productivity, active device maintenance, and familiar decision-making to better business growth opportunities.
Predictive Analytics: This is one of the most widely used and popular big data technologies. Being a sub-part of Big Data analytics, Predictive Analytics uses previous data to predict future events and behavior. It uses the following technologies:Machine learningData modelingStatistical and mathematical modeling
Blockchain is the primary technology behind cryptocurrencies. Its uniqueness lies in its ability to capture structured data in a way that can never be altered or deleted. Because of this feature creates an extremely secure and reliable ecosystem for the Banking, Finance, Securities, and Insurance (BFSI) sector. Along with BFSI blockchains, they also find applications in social welfare sectors like education and healthcare.4) Emerging Big Data TechnologiesTensorFlow has a robust, scalable ecosystem of resources, tools, and libraries for researchers, allowing them to quickly create and deploy powerful Machine Learning applications.
Apache Beam offers a compact API layout to create sophisticated Parallel Data Processing pipelines through various Execution Engines or Runners. Apache Software Foundation developed these tools for Big Data in the year 2016.
Docker is one of the tools for Big Data that makes the development, deployment and running of container applications simpler. Containers help developers stack an application with all the necessary components, such as libraries and other dependencies.
Apache Airflow is a Process Management and Scheduling System for the management of data pipelines. Airflow utilizes job workflows made up of DAGs (Directed Acyclic Graphs) tasks. The code description of workflows makes it easy to manage, validate and version a large amount of Data.
Kubernetes is one of the open-source tools for Big Data developed by Google for vendor-agnostic cluster and container management. It offers a platform for container systems’ automation, deployment, escalation, and execution through host clusters.
Blockchain is the Big Data technology that carries a unique data safe feature in the digital Bitcoin currency so that it is not deleted or modified after the fact is written. It’s a highly secured environment and an outstanding option for numerous Big Data applications in various industries like baking, finance, insurance, medical, and retail, to name a few. 5) What the future holds for Big Data Technologies?The Big Data environment is continually evolving. Very easily, the latest innovations in Big Data Technologies are being launched, many of which will increase based on the demand in the IT industry. These innovations will ensure that there is harmonious functioning for the development of businesses.

Let’s take a look:

Cloud solutions will power Big Data Technologies: With the Internet of Things (IoT) taking the front seat, data generation is on its rise. Applications involving IoT will require a perfect scalable solution for managing huge volumes of Data. What other than cloud services can do this better? Advantages of Hadoop on Cloud have already been realized by many organizations and technologies pertaining to the coupling of Big Data technologies like Hadoop, Spark, IoT and cloud. These are expected to be well on rising in the coming years.Traditional Database world will revolutionize: RDBMS systems have dominated the database world for decades when structured data formed the major proportion of data in any organization. Looking at the data sources today – Social media data, IoT, sensors, etc. – where each one of us is generating volumes of data on a daily basis, it’s clear that the amount of unstructured Data is steadily increasing, and companies have started realizing the potential insights one can gain from such data. Now, to manage and process such data, NO-SQL databases have been the best option in the last few years. Well, this trend will continue to grow. Applications on NO-SQL databases, mostly POCs, are expected to move into the deployment phase. The most popular No-SQL databases, like MongoDB and Cassandra, will continue to be implemented by more vendors. Also, graph databases like Neo4j will gain more market.Hadoop will continue to rock: In terms of technological developments, Hadoop will come up with features that would make it more enterprise-ready. Once Hadoop security projects like Sentry, Rhino, etc., gain stability, Hadoop’s implementation will expand across many more sectors, and companies can use the solutions without many security concerns.Real-Time Solutions will expand: All companies now have the data and know-how to store and process Big Data. The real difference will be how fast they can deliver analytics solutions for better business decisions. The processing capabilities of Big Data technologies will certainly increase. Projects like Spark, Storm, Kafka, etc., were developed with this aspect in mind. These Big Data technologies will see companies advancing from POCs to real-world applications.Self-Service Big Data applications will continue to evolve: Big Data technologies that simplify data cleaning, data preparation, and data exploration tasks are expected to increase. Tools for Big Data like Tableau and Hadoop have recently been popular. These tools for Big Data will greatly minimize the effort of the end-users. Companies like Informatica have already shown innovations in this frontier. We can see more such Big Data technologies and more companies working towards such self-service solutions.