What is Big Data Technology?

What is Big Data Technology?: Types and Examples

Before the development of big data technologies, conventional programming languages and simple structured query languages were used to manage the data. Due to the constant expansion of each organization’s information and data as well as the domain, these languages were not effective enough to handle the huge database. Because of this, it has become crucial to manage such massive amounts of data and implement a reliable system that meets all the requirements and needs of clients and large businesses. Big data technologies are the current trending tool for all these requirements.

In this write-up, we’ll know about the top technologies that have developed new applications to support Big Data. What is big data technology? This technology’s main purpose is to evaluate, process, and extract data from a sizable collection of exceedingly complicated structures. For conventional data processing tools, these particular operations are quite challenging to handle.

Big data technologies are frequently linked to a variety of different technologies, including deep learning, machine learning, artificial intelligence (AI), and the Internet of Things (IoT). Which are collectively considered the main drivers of IT operations. In addition to these technologies, big data technologies concentrate on the analysis and management of substantial amounts of both batch-related and real-time data.

Big Data Technologies: Four Types

The four basic categories of big data technologies are data storage, data mining, data analytics, and data visualization. Each of them has a specific tool linked with it, thus based on the big data technologies needed for your organization, you should pick the appropriate tool.

Data Storage

The ability to retrieve, store, and manage enormous data is a feature of big data technology that deals with data storage. It consists of the infrastructure needed for users to store data in a way that makes it easy to access. The majority of data storage solutions work with other software. Apache Hadoop and MongoDB are two frequently used programs.

Apache Hadoop: The most extensively used big data technology is Apache Hadoop. It is an open-source software platform that manages large data processing and storage across hardware clusters in a distributed computing environment. The distribution makes it possible to handle data more quickly and effectively. The framework is made to be scalable, handle all data formats, and have fewer problems or faults.

MongoDB: Large amounts of data can be stored with the NoSQL database MongoDB. MongoDB organizes documents into collections using key-value pairs (a fundamental data unit). It is one of the most well-liked large data databases because it can easily manage and store unstructured data. It is developed using C, C++, and JavaScript.

Data Analytics

From the raw data, data mining extracts the relevant patterns and trends. Big data tools like Rapidminer and Presto can turn both organized and unstructured data into useful data.

Rapidminer: Using the data mining program Rapidminer, one can create prediction models. It uses the processing and preparation of data and the development of machine and deep learning models which are its two main strengths. Both functions can have an impact across the business because of the end-to-end paradigm.

Presto: Facebook created the open-source query engine Presto to do analytical queries on their big datasets. At present, it is widely accessed. In a matter of minutes, Presto can aggregate data from many sources within an organization and run analytics tools over it.

Data Mining

Technologies are employed in big data analytics to clean up data and turn it into information that can be utilized to inform business choices. Following data mining, users execute algorithms, models, and more using software like Apache Spark and Splunk.

Apache Spark: Because it runs programs quickly and effectively, Spark is a well-liked big data tool for data analysis. Because it employs random access memory (RAM) rather than data being stored and processed in batches using MapReduce, it is faster than Hadoop [4]. Many different data analytics jobs and queries are supported by Spark.

Splunk: Another well-liked big data analytics tool for concluding enormous datasets is Splunk. In addition to reports and dashboards, it can also produce graphs and charts. Users of Splunk can additionally use artificial intelligence (AI) to data results.

Data Visualization

Finally, amazing data visualizations may be produced using big data tools. The ability to tell a powerful story using a straightforward graph is useful in data-oriented professions for giving recommendations to stakeholders for business profitability and operations.

Tableau: Tableau is a particularly well-liked application for data visualization because of how simple it is to construct pie charts, bar charts, box plots, Gantt charts, and other types of charts using its drag-and-drop interface. Users can securely share dashboards and visualizations on this platform in real-time.

Looker: A business intelligence (BI) tool called Looker is used to interpret large data analytics and then communicate those findings to other teams. A query can be used to build charts, graphs, and dashboards, for example, to track weekly brand interaction using social media analytics.

Why Choose Big Data Technology?

The employee’s duty of maintaining and interpreting the data will ultimately take time and energy which determine the commercial value of the company. Coupling these efforts with big data technology with definitely help in making better decision-making. Big data technologies reduce the need for hardware and distributed software expertise by allowing fewer technical people to run predictive analytics applications and assist enterprises in deploying a suitable infrastructure.

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