5 Big Data Technologies In 2021
In this article, we’ll dive into the world of Big Data and explore the top 5 Big Data technologies to emerge in 2021.
The Big Data bubble has undoubtedly begun to burst, and realistic developments in Big Data applications are expected in the following year. Most of us are now well-versed in words like Hadoop, Spark, NO-SQL, Hive, Cloud, and so on. Every month, we know of at least 20 new NoSQL databases and a slew of additional Big Data technologies. But which of these Big Data technologies has a bright future? Which Big Data tools will provide you with the most value?
What is Big Data Technology?
Big data is a term that refers to a large collection of data that is rapidly growing in size and volume. Big Data Technologies are software tools that analyze, analyze, and extract data from exceedingly complex and huge data sets that typical management systems cannot handle.
What are the types of Big Data Technologies?
Big Data Technologies are broadly classified into two categories.
1. Operational Big Data Technologies
Operational Big Data Technologies refers to the amount of data generated every day, such as online transactions, social media posts, or any other information from a specific firm that is analyzed using big data technologies. It serves as raw data for big data analysis software. Information on MNC management, Amazon, Flipkart, Walmart, online ticketing for movies, airplanes, and railroads are just a few examples of Operational Big Data Technologies.
2. Analytical Big Data Technologies
Analytical Big Data Technologies is more sophisticated than Operational Big Data Technologies since it involves advanced adjustments to Big Data Technologies. This category comprises real-world Big Data analysis, which is critical for business choices. Stock marketing, weather forecasting, time series analysis, and medical records analysis are some examples of this type of analysis.
Let’s look at the top five Big Data technologies employed in the IT industry.
Top 5 Big Data Technologies
1. Hadoop Ecosystem
Hadoop Framework was created to store and process data in a distributed data processing environment using a simple programming model. Data from a variety of high-speed and low-cost equipment can be saved and examined. In the last year, businesses have embraced Hadoop as a Big Data Technology for their data warehouse needs. The trend appears to be continuing and accelerating in the following year. Companies that haven’t looked into Hadoop yet are likely to recognize its benefits and applications.
2. Artificial Intelligence
Artificial intelligence (AI) is a vast field of computer science concerned with the creation of intelligent machines capable of doing activities that would normally need human intelligence. From Apple’s Siri to self-driving automobiles, AI is rapidly evolving. As an interdisciplinary discipline of research, it considers a variety of methodologies, such as expanded Machine Learning and Deep Learning, to create a significant change in the majority of tech businesses. Existing Big Data Technologies are being revolutionized by AI.
3. NoSQL Database
In the database, NoSQL encompasses a variety of various Big Data Technologies that were created to construct current applications. It depicts a non-SQL or non-relational database with a data gathering and recovery strategy. They are utilized in real-time Web and Big Data Analytics. It saves unstructured data and provides faster performance and flexibility for a variety of data formats, including MongoDB, Redis, and Cassandra. In a variety of devices, it gives design integrity, better horizontal scaling, and control over opportunities. By default, it employs data structures that aren’t related to databases, which speeds up NoSQL calculations. Every day, Facebook, Google, Twitter, and other comparable corporations keep gigabytes of consumer data.
4. R Programming
R is an open-source Big Data programming language and technology. The free program is frequently used for statistical computing, visualization, and support communication in unified development environments like Eclipse and Visual Studio. According to experts, it was the most widely spoken language on the planet. Data miners and statisticians use the system to develop statistical software and, in particular, data analysis.
5. Data Lakes
In terms of structural and unstructured data, Data Lakes refers to a centralized repository for storing all data forms at all levels.
Data can be saved in its raw form without being processed into structured data during data accumulation. It allows for real-time data analysis ranging from dashboards and data visualization to Big Data transformation for greater business intelligence.
Businesses that use Data Lakes keep ahead of the competition by doing new analytics, such as Machine Learning, using new log file sources, social media data, and click-streaming.
This Big Data technology aids businesses in responding to greater company growth prospects by better understanding and engaging customers, maintaining productivity, active device maintenance, and familiar decision-making.
What The Future Holds For Big Data Technologies?
The Big Data landscape is always changing. The latest breakthroughs in Big Data Technologies are simply released, and many of them will grow in response to the IT industry’s demand. These advances will ensure that firms are able to grow in a balanced manner.
Let’s take a look:
Cloud solutions will power Big Data Technologies: Data generation is on the rise as the Internet of Things (IoT) takes center stage. IoT applications will necessitate a perfect scalable solution for managing massive amounts of data. What else can accomplish this better than cloud services? Many enterprises and technologies relating to the coupling of Big Data technologies like Hadoop, Spark, IoT, and cloud have already discovered the benefits of Hadoop on Cloud. In the next years, these are projected to skyrocket.
Traditional Database world will revolutionize: For decades, RDBMS systems ruled the database world when structured data made up the majority of data in any firm. Looking at today’s data sources – social media data, IoT, sensors, and so on – where each of us generates large amounts of data on a daily basis, it’s clear that the amount of unstructured data is steadily increasing, and companies have begun to recognize the potential insights that such data can provide. In recent years, NO-SQL databases have shown to be the greatest solution for managing and processing such data. This is a tendency that will continue to expand. Applications that were largely proof-of-concepts on NOSQL databases are projected to enter the deployment phase. More vendors will adopt the most popular No-SQL databases, such as MongoDB and Cassandra. Graph databases, such as Neo4j, will also grow in popularity.
Hadoop will continue to rock: Hadoop will develop capabilities that will make it more enterprise-ready in terms of technological advancements. Hadoop’s deployment will extend across many more sectors if Hadoop security projects like Sentry, Rhino, and others gain stability, and enterprises will be able to use the solutions without having to worry about security.
Real-Time Solutions will expand: By now, every company has the data and know-how needed to store and process Big Data. The key difference will be how quickly companies can deploy analytics solutions to help businesses make better decisions. In 2021, the emphasis will be on speed. Big Data technology’ processing capacities will undoubtedly improve. Spark, Storm, Kafka, and other projects were created with this in mind. With these Big Data technologies, we will see firms progress from proof-of-concept to real-world applications.
Self-Service Big Data applications will continue to evolve: Big Data solutions that make data cleaning, preparation, and investigation easier are predicted to grow in popularity. In recent years, Big Data tools such as Tableau with Hadoop have grown in popularity. End-user effort will be considerably reduced with these Big Data technologies. Informatica, for example, has previously demonstrated innovation in this area. We’re seeing more Big Data technologies emerge, as well as more enterprises working on self-service solutions.
To summarize, Big Data is still on the rise, with increasing adoptions and uses of existing Big Data technologies, as well as the introduction of fresh solutions in areas such as Big Data security, Cloud integrations, and data mining.