Emerging Big Data Technologies in 2022

In the present era, businesses generate and locate extensive amounts of data on a daily basis. The recent spike in digital transformation has made the role of data management and analytics all the more prominent. According to reports, the data departments have encountered exponential data growth in the past few years, and the growth is said to continue in 2022. If the companies want to extract appropriate information from this vast and miscellaneous stockpile, they must adopt a smart data management system. Big data plays a crucial role in enhancing the B2B marketing scene. Research says that there were 1.7 megabytes of data getting produced every second in 2020. How do you control that? The answer is “Big data technologies!”

Big Data Technologies are software solutions that include data mining, analytics, sharing, visualization, etc. They adopt specific data frameworks, tools, and techniques that can be used for sorting, examining, remodeling, analyzing, etc. In the digital age, having technologies with such capabilities can greatly enhance business performance. Our advanced world is in alarming need of big data technologies as they make it more effortless for people to tackle big problems related to the grid. Companies are creating new technologies that can help them with data accuracy. Forward-thinking businesses make use of the latest Big Data technologies to stimulate growth and business values. There are numerous big data technologies with fascinating platforms being harnessed by industries. Industry professionals love to use big data technologies to help them with complex data for analytics, visualization, mining, and storage of data.

The emerging big data technologies in 2022 that can help in your business are as follows-

1. Hadoop technologies –

Hadoop is an open-source framework used to store and processes big data. It is a distributed file system that permits simultaneous processing and fault tolerance by storing data on inexpensive commodity servers that run as clusters. With Hadoop, organizations can store any structure of data. Hadoop is recreating a significant role in today’s life. It can be adopted by any sector as per their business requirements. The plus points of Hadoop are- it is scalable, cost-effective, resilient to failure, and very flexible.

• YARN: Perfoms resource management tasks such as allocating to applications and scheduling jobs.
• MapReduce: Permits processing of data on top of the distributed storage system.
• HIVE: Allows SQL-proficient professionals to conduct data analytics.
• PIG: Being a high-level scripting language, it streamlines data transformation on top of Hadoop.

2. NoSQL technologies –

NoSQL proves to be a beneficial option for organizations whose data workloads are more towards processing and analyzing large amounts of varied data. As organizations develop beyond querying structured data, NoSQL databases arise to facilitate faster performance. The most popular NoSQL databases in the market are :

• Mongo DB – MongoDB is a NoSQL program that utilizes documents equivalent to JSON. With MongoDB, organizations can handle several data types that come in extensive amounts across distributed architectures. The key advantages of MongoDB are flexibility and scalability.
• Cassandra – Apache Cassandra is a reliable and open-source distributed database management system. Cassandra handles a massive amount of data across several commodity servers. Therefore, providing high availability and scalability by eliminating a single chance of risk or failure.

3. Data Mining technologies –

It is the process of extracting valuable information from raw data. In the case of voluminous data, extraction without a unique technology is impossible. Few of the big data technologies used for data mining are.

• Elasticsearch – Known for its scalability, speed, and REST APIs. Elasticsearch is an analytics engine that works on an open search distributed interface. It enables users to store, search & analyze massive data volumes in near-real-time. Elasticsearch also allows for customizable analytics and reports through its dynamic aggregation engine.
• RapidMiner –It is a software package used for data mining and running predictive analysis. RapidMiner is one such platform that upskills organizations’ extensibility and portability by providing impactful business insights to several industries. It permits sophisticated workflows with support, scripted in many languages.

4. Data Visualization technology –

In data visualization, the big data is processed to form graphic illustrations that allow humans to understand large amounts of information in seconds.

• Tableau –A robust big data technology that provides options to create a proper visualization. Often preferred for its high speed for data analytics, Tableau offers several features like integration with over 250 applications, assistance to solve real-time big data analytics issues, medium speed to enhance extensive operation, etc.

5. Data Analytics –

It is the process of cleaning, transforming, and modeling data for discovering relevant insights, hidden patterns, and market trends for decision-making. The data analysis technologies for big data include.

• Splunk – It is the software that transforms machine-generated data to times series events. Splunk allows you to discover the hidden value of the data, making it easier for business leaders to answer business and operational problems in real-time. It can be utilized for app management, improving security, and for business and web analytics.
• KNIME – It is an open-source platform with features like integration, free reporting, and data analytics. The pros of using KNIME include its capability to connect to various data sources, provision of control over what happens with data at every stage, etc.

6. R Programming –

R programming is indeed one of the most powerful big data software in existence as it offers techniques for statistical and graphical problems. It is mainly implemented by data miners for conducting data analytics.

7. Artificial Intelligence (AI) –

AI, along with technologies like ML and Deep Learning, are propelling business analytics in a way that the organization can innovate to the next level. The most significant advantage is staying ahead of the competition by recognizing possible problems and errors that may get skipped by humans.

Conclusion

The continuous evolution of big data is causing organizations to store, manage, and analyze data in a better way for competitive advantages. Therefore, implementing Big Data Technology in business will deliver a super abundance of benefits. To leverage the benefits of most of the new Big Data Technologies, you are ought to first identify the problem your organization has been facing. This will allow in determining the best-fit solution.

We at MSRcosmos, help you to choose and develop suitable solutions for managing and analyzing big data. We assist you in developing the right solution for your big data needs by addressing the scale and agility needs of your modern applications. With our extensive expertise in the field, we promise to deliver an effective solution customized specially for your needs.