Latest Big Data Technologies That are Ruling the Roost in 2023
In this tech-driven era, efficient data management is becoming increasingly important for businesses. One of the key elements of this digital age is the emergence of big data and its consequent importance for business success. And big data is only going to get bigger. Globally, the amount of data generated, copied, consumed, and captured is expected to exceed 149 zettabytes by 2024. Understanding the value of big data is already a challenging task. Then, there is the issue of funding and obtaining a return on investment, which remains at the forefront of several companies that are adopting big data. This is where knowledge of technologies for big data is imperative. That, as a result, means businesses need skilled data analysts on their roster.
This blog delves into technologies for big data that analysts, new and experienced, must know about, as well as the importance and components of big data.
In this blog, we will analyze:
- What are Big Data Technologies?
- Components of Big Data Technology
- The Importance of Big Data
- Top Technologies for Big Data in 2023
- Upskill in Big Data with Emeritus
What are Big Data Technologies?
Big data refers to the massive amounts of data that organizations generate every day. This data is typically very large and complex and, therefore, difficult to handle with traditional data processing tools and technologies. However, technological advancements have now made it possible to quickly and effectively store, process, and analyze such large amounts of data.
Some examples of technologies for big data processing include Apache Hadoop, Apache Spark, and MongoDB, all of which are available to use in organizational systems. Each of these technologies has its own strong and weak points, but all of them can be used to gain insights from large data sets. Big data storage technologies are a compute-and-storage architecture that collects and manages large amounts of data while also allowing for real-time data analytics.
Components of Big Data Technology
The four basic components of big data technology are:
1. Data Collection
The process of collecting data from various sources is referred to as data capture. This can range from social media posts to sensor readings.
2. Data Storage
This is the process of storing data in a way that allows it to be accessed for subsequent analysis.
3. Data Processing
This component includes various algorithms to evaluate data and derive insights.
4. Data Presentation
The process of representing this data in an understandable manner for humans.
The Importance of Big Data
Needless to say, big data is key for business success and growth. Organizations highly rely on it to improve customer service, marketing, sales, team management, and a variety of other routine activities. The data is essential for making informed, data-driven decisions that produce measurable benefits. Brands can enhance profitability and their return on investment with information and insights gathered from such big data and position themselves as market leaders.
The role of big data in increasing a company’s revenue lies in the following elements:
- Helping businesses improve their advertising and marketing strategies or campaigns
- Assisting in the analysis of changing corporate purchasing, customer, and market behavior
- Increasing customer interaction and leading conversion rates
- Improving market and client responsiveness
ALSO READ: How to Become a Data Scientist: The Ultimate Guide
Top Technologies for Big Data in 2023
Big data technology is continually growing to meet the upcoming challenges, and the landscape will become more complex in the next few years. As we close in on the end of the year, we look at the best technologies for big data that 2023 has on offer:
1. R Programming Language
R is a significant data technology used in programming languages for statistical computation and graphics. Additionally, this programming software offers a wide range of features, including linear modeling, nonlinear modeling, traditional statistical tests, time-series analysis, clustering, and graphical approaches.
It is a well-designed platform that includes a variety of mathematical symbols and formulae. It also enables successful data management by providing a huge, coherent and integrated set of powerful real-time data analytics capabilities.
2. NoSQL Databases
Among the significant technologies for big data are the more traditional Relational Database Management Systems (RDBMSes). They store data in structured and well-defined columns and rows. SQL, for example, is a particular language used by developers and database managers to query, alter, and manage data in RDBMSes. On the other hand, NoSQL databases specialize in storing unstructured data and giving quick speed. Some popular NoSQL databases are MongoDB, Redis, Cassandra, Couchbase, etc. The leading RDBMS providers, such as Oracle and IBM, now offer NoSQL databases.
3. Hadoop
Every data scientist is familiar with Hadoop. It is a free and open-source software platform for creating data-processing applications that run in a distributed computing environment. Furthermore, Hadoop provides huge storage for every sort of input. It supports an unequaled processing capacity and the ability to manage multiple jobs at the same time.
In recent years, the Hadoop framework has been widely employed by the world’s leading technological companies, including Facebook and Google. It has also made its way into the computer stacks of major banking and insurance firms, research organizations, and other businesses.
4. RapidMiner
RapidMiner is a world-class big data technology that is known for providing breakthrough business insights to a wide range of organizations. It acts as a data preparation, deep learning, text mining, and predictive analytics environment all in one. It is more popular among nonprogrammers and researchers due to its compatibility with Apple, Android, NodeJS, flask, and a variety of other frameworks.
RapidMiner also offers a data set collection and allows users to load real-time data from Cloud, RDBMS, NoSQL, and other sources.
5. Qlik
By combining embedded and predictive analysis, Qlik assists big data analysts in detecting prospective market trends. Moreover, with the associative engine (responsible for bringing all data together) and a regulated multi-cloud architecture, it provides a full range of real-time data analytics.
By indexing every relationship inside the data, the associative engine ensures that limitless combinations of big data are delivered. It aids in detecting in-depth insights for improved productivity.
6. Cassandra
Cassandra is a data-storing tool that comes under the NoSQL database and can handle data from several clusters. It is frequently the top choice in NoSQL databases due to its scalability, query-based language property, and distributed method.
7. ElasticSearch
The biggest advantage of this open-source search engine is that it allows analysts to parse extremely large volumes of data speedily, almost in real time. This full-text search tech is typically used for applications that have highly complicated requirements.
Upskill in Big Data With Emeritus
Things are constantly evolving in the realm of technology. What was once in high demand might swiftly become obsolete. This goes well in the case of big data as well. You must, therefore, be aware of the top technologies for big data that will be prevalent in 2023 if you want to stay ahead of the curve. Additionally, according to Research and Markets, the global big data industry will be worth $268.4 billion by 2026. This means the upcoming years are going to be beneficial for aspirants looking forward to choosing data science as a career. Emeritus’ data science courses will ensure you stay ahead of the curve. Aspiring candidates can learn about a wide range of data science topics, including business analytics, data visualization, gamification, and artificial intelligence. So, dive deep into big data analytics and gain a head start in your career.
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