4 Types of Big Data Technologies (+ Management Tools)

As technology companies like Amazon, Meta, and Google continue to grow and integrate into our lives, they leverage big data technologies to monitor sales, improve supply chain efficiency and customer satisfaction, and predict future business outcomes. Data generation is growing exponentially, with Statista estimating global data creation will grow to 394 Zettabytes (ZB) by 2028 [1]. Each zettabyte is equal to a trillion gigabytes.

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

Learn more about the four types of big data technologies and the tools you can use to harness them.

If you’re ready to start building expertise in big data technologies, enroll in the IBM Data Warehouse Engineer Professional Certificate. You’ll have the opportunity to learn how to manipulate and analyze large volumes of data in databases in as little as four months. Upon completion, you’ll have earned a career certificate to add to your resume.

What are the big data technologies? 4 types of big data technologies and tools you’ll need to know

Big data technologies typically fall into four main types: data storage, data mining, data analytics, and data visualization [2]. Each uses certain tools, and depending on the type of big data technology required, you’ll want to choose the right tool for your business needs.

1. Data storage

Big data technology deals with data storage and can fetch, store, and manage big data. It incorporates infrastructure that allows users to store the data conveniently. Most data storage platforms are compatible with other programs. Two commonly used tools are Apache Hadoop and MongoDB. 

• Apache Hadoop: Apache Hadoop is one of the most widely used big data tools. 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 helps reduce bugs or faults, is scalable, and can process all data formats.

• MongoDB: MongoDB is a NoSQL database that you can use to store 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 manage and store unstructured data with ease.

How is big data collected?

Big data comes from a variety of structured and unstructured sources, making it a complex and ever-growing source of information. Common sources for big data collection include wearable devices, Internet of Things (IoT) devices, sensors, and social media activity, to name a few. Depending on your field, sources of big data will differ, but the speed of information generation, the complexity of the information, and the types of analysis it requires often define them.

Read more: Understanding Different Types of 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 you can use to build predictive models. It draws on these two roles as strengths of processing and preparing data, and building machine learning and deep learning models. The end-to-end model allows you to use advanced analytics and AI automation to drive impact across the organization.

• Presto: Presto is an open-source query engine that was originally developed by Facebook to run analytic queries against its large data sets. 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. Big data analytics

You might use big data analytics tools to clean and transform data into information that drives business decisions. This next step (after data mining) is where users create algorithms and models and perform predictive analytics 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 [3]. 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 data sets. It has the ability to generate graphs, charts, reports, and dashboards. Splunk also enables users to incorporate artificial intelligence (AI) into data outcomes.

4. Data visualization

Finally, you can use big data technologies to create stunning visualizations from the data. In data-oriented roles, data visualization is a skill that 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 very popular 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 a secure platform that allows users to share visualizations and dashboards in real time.

• Looker: Looker is a business intelligence (BI) tool used to understand big data analytics and share insights with other teams. With a query, you can configure charts, graphs, dashboards, and other data visualizations, such as monitoring weekly brand engagement through social media analytics. 

Stay current with big data technology trends

Charting your next career move? Keep up with the latest insights by subscribing to our LinkedIn newsletter, Career Chat. Also, continue exploring big data and data analytics with these free resources:

• Take the Quiz: Which Data Analysis Course Should You Take?

• Watch on YouTube: 7 Essential Data Analytics Skills for Beginners or Is Data Analytics Really That Hard? Tips for Rising to the Challenge

• Hear from an industry professional: 7 Questions with a Data Analytics Professor

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