4 Types of Big Data Technologies (+ Management Tools)

Written by Coursera Staff • Updated on

Organizations can harness big data through four types of technology. Learn more about them and what tools can be used to manage all that big data.

[Featured Image]:  A male wearing a blue shirt is sitting in front of his desktop, performing his duties as a data analyst.

As technology companies like Amazon, Meta, and Google continue to grow and integrate with our lives, they leverage big data technologies to monitor sales, improve supply chain efficiency and customer satisfaction, and predict future business outcomes. Currently, there is so much big data that International Data Corporation (IDC) predicts the “Global Datasphere” will grow from 33 Zettabytes (ZB) in 2018 to 175 ZB in 2025 [1]. That’s equal to a trillion gigabytes.

Big data technologies are the software tools used to manage all datasets and transform them into business insights. In data science careers, such as big data engineers, sophisticated analytics evaluate and process vast volumes of data. 

Here are the four types of big data technologies and the tools organizations can use to harness them.

4 types of big data technologies

Big data technologies fall into four main categories: data storage, data mining, data analytics, and data visualization. Each is associated with certain tools, and you’ll want to choose the right tool for your business needs and the type of big data technology required.

1. Data storage

Big data technology that deals with data storage can fetch, store, and manage big data. It comprises infrastructure that allows users to store data, so it’s convenient to access. Most data storage platforms are compatible with other programs. Two commonly used tools are Apache Hadoop and MongoDB. 

  • Apache Hadoop: Apache is the most widely used big data tool. It is an open-source software platform that stores and processes big data in a distributed computing environment across hardware clusters. This distribution allows for faster data processing. The framework reduces bugs or faults, is scalable, and processes all data formats.

  • MongoDB: MongoDB is a NoSQL database that stores large volumes of data. Using key-value pairs (a basic unit of data), MongoDB categorizes documents into collections. It is written in C, C++, and JavaScript and is one of the most popular big data databases because it can easily manage and store unstructured data.

2. Data mining

Data mining extracts useful patterns and trends from the raw data. Big data technologies like Rapidminer and Presto can turn unstructured and structured data into usable information.

  • Rapidminer: Rapidminer is a data mining tool that can build predictive models. It draws on these two roles as strengths: processing and preparing data and building machine and deep learning models. The end-to-end model allows both functions to drive impact across the organization.

  • Presto: Presto is an open-source query engine that Facebook originally developed to run analytic queries against their large datasets. Now, it is available widely. One query on Presto can combine data from multiple sources within an organization and perform analytics on them in a matter of minutes.

3. Data analytics

In big data analytics, technology cleans and transforms data into information businesses can use to drive decisions. This next step (after data mining) is where users perform algorithms and models, using tools such as Apache Spark and Splunk.

  • Apache Spark: Spark is a popular big data tool for data analysis because it is fast and efficient at running applications. It is faster than Hadoop because it uses random access memory (RAM) instead of being stored and processed in batches via MapReduce [4]. Spark supports a wide variety of data analytics tasks and queries.

  • Splunk: Splunk is another popular big data analytics tool for deriving insights from large datasets. It can generate graphs, charts, reports, and dashboards. Splunk also enables users to incorporate artificial intelligence (AI) into data outcomes.

4. Data visualization

Finally, big data technologies can create stunning visualizations from the data. In data-oriented roles, data visualization is beneficial for presenting recommendations to stakeholders for business profitability and operations—to tell an impactful story with a simple graph.

  • Tableau: Tableau is a prevalent tool in data visualization because its drag-and-drop interface makes it easy to create pie charts, bar charts, box plots, Gantt charts, and more. It is also a secure platform that allows users to share real-time visualizations and dashboards.

  • Looker: Looker is a business intelligence (BI) tool that makes sense of big data analytics and then shares those insights with other teams. A query, such as monitoring weekly brand engagement through social media analytics, can configure charts, graphs, and dashboards. 

Learn big data with Coursera.

Immerse yourself in the world of big data technologies. Learn all you need to know about big data analysis based on the world’s most popular big data technologies Hadoop, Spark, and Storm, from Yonsei University’s course Big Data Emerging Technologies, part of the specialization Emerging Technologies: From Smartphones to IoT to Big Data

Article sources

  1. Seagate: “The Digitization of the World From Edge to Core, https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf.“ Accessed May 1, 2024.

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