All Things Data

Data can be harnessed with the use of technologies, which can be categorized into four types.

As technology companies like Amazon, Meta, and Google continue to grow and integrate with our lives, they are leveraging 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 types of datasets and transform them into business insights. In data science careers, such as big data engineers, sophisticated analytics evaluate and process huge volumes of data. 

Here are the four types of big data technologies and the tools that can be used to harness them.

4 types of big data technologies

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

  1. Data storage

Big data technology that deals with data storage has the capability to fetch, store, and manage big data. It is made up of infrastructure that allows users to store the data so that it is 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 is designed to reduce bugs or faults, be scalable, and process all data formats.
  • MongoDB: MongoDB is a NoSQL database that can be used 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.
  1. Data mining

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

  • Rapidminer: Rapidminer is a data mining tool that can be used to build predictive models. It draws on these two roles as strengths, of processing and preparing data, and building machine and deep learning models. The end-to-end model allows for both functions to drive impact across the organization [3].
  • Presto: Presto is an open-source query engine that was originally developed by Facebook 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.
  1. Data analytics

In big data analytics, technologies are used to clean and transform data into information that can be used to drive business decisions. This next step (after data mining) is where users perform algorithms, models, and 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 [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 has the ability to generate graphs, charts, reports, and dashboards. Splunk also enables users to incorporate artificial intelligence (AI) into data outcomes.
  1. Data visualization

Finally, big data technologies can be used 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 make sense of big data analytics and then share those insights with other teams. Charts, graphs, and dashboards can be configured with a query, such as monitoring weekly brand engagement through social media analytics. 

Applications of Big Data Technologies

Big data technology has numerous applications in different fields. Some recognized areas of applications include: 

  • Healthcare: Big Data Technology is used to analyze data of patients to personalize medicine plans. It also offers predictive analysis for disease outbreaks and is efficient in devising treatment plans to optimize healthcare operations efficiently. 
  • Finance: This technology offers valuable insights into the field of finance for the detection of fraud. It also provides customer segmentation for the target market. 
  • E-Commerce: Big Data Technology gives valuable recommendation engines for personalized shopping experiences. 
  • Education: This technology helps in creating adaptive learning platforms for personalized education and offers insights into students’ performance analytics. 
  • Retail: Big Data Technology helps retailers perform customer behaviour analysis for personalized marketing. It also focuses on inventory management and price optimization techniques based on market trends.

Types of Big Data Technology

Big Data Technology is primarily divided into two types: Operational Big Data Technologies and Analytical Big Data Technologies. 

Operational Big Data Technologies

This type of big data technology focuses on the data that people use to process. Typically, the operational-big data includes data such as online transactions, social media platforms, and data from any particular organization. The operation analytics benefit is the analysis using software based on big data technologies. The data can also be called raw data used as the input for several Analytical Big Data Technologies.

Some examples of Operational Big Data Technologies include: 

  • Data on social media platforms like Facebook and Instagram
  • Online ticket booking systems

Analytical Big Data Technologies

Analytical Big Data is an enhanced version of Big Data Technologies. This type of big data technology is complex when compared to operational big data. Analytical big data is mainly used when performance metric is used and important business decisions are to be made based on reports created by analyzing operational analytics. This means that the investigation of big data is important for business decisions. 

Some examples of Analytical Big Data Technologies include: 

  • Stock Marketing Data
  • Medical health records 

Top Big Data Technologies 

  1. Apache Hadoop

Apache Hadoop is an open-source framework for distributed storage and extensive data set processing through simple programming models. It includes the HDFS for data storage across multiple machines and the MapReduce programming model for data processing. Hadoop’s architecture allows it to scale from single servers to thousands of machines, each capable of local computation and storage. As a cornerstone technology in the big data landscape, Hadoop efficiently manages vast amounts of both structured and unstructured data, making it an essential tool for handling large-scale data processing tasks.

  1. Apache Spark

Apache Spark is an open-source unified analytics engine known for its speed and ease of use in big data processing. It provides in-memory computation capabilities, significantly boosting the performance of big data processing tasks compared to disk-based Hadoop MapReduce. Spark supports Scala, Java, Python, R, etc, and offers high-level APIs for operations such as SQL queries, streaming data, ML, and graph processing. Its batch and real-time processing ability makes it a versatile tool in the big data ecosystem.

  1. Apache Kafka

Apache Kafka is a distributed event streaming platform that handles real-time data feeds. Developed initially by LinkedIn, Kafka is designed to provide high-throughput, low-latency data processing. It is used for building real-time data pipelines and streaming applications, allowing for the publish-subscribe model where data producers send records to Kafka topics and consumers read from them. Kafka’s robust infrastructure can handle millions of messages per second, making it ideal for applications that require real-time data processing, such as log aggregation, stream processing, and real-time analytics.

  1. Apache Flink

Apache Flink is an open-source stream-processing framework known for its ability to handle real-time data streams and batch data processing. It provides accurate, stateful computations over unbounded and bounded data streams with low latency and high throughput. Flink’s sophisticated features include complex event processing, machine learning, and graph processing capabilities. Its fault-tolerant and scalable architecture makes it suitable for large-scale data processing applications. Flink’s advanced windowing and state management capabilities are particularly useful for applications that need to analyze continuous data flows.

  1. Google BigQuery

A fully managed, serverless data warehouse that leverages Google’s infrastructure to facilitate rapid SQL queries. It enables quick and efficient querying of large datasets without infrastructure management. BigQuery employs a columnar storage format and a distributed architecture to deliver high performance and scalability. It integrates with other Google Cloud services and supports real-time data analysis, making it an essential tool for business intelligence, data analytics, and machine learning applications.

  1. Amazon Redshift

A fully managed cloud data warehouse service that makes it easy to analyze large datasets using SQL and business intelligence tools. Redshift’s architecture is designed for high-performance queries, providing the ability to run complex analytical queries against petabytes of structured and semi-structured data. It offers features like columnar storage, data compression, and parallel query execution to enhance performance. Redshift integrates with various data sources and analytics tools, making it a versatile solution for big data analytics and business intelligence.

  1. Snowflake

Snowflake is a cloud-based data warehousing platform known for its scalability, performance, and ease of use. Unlike traditional data warehouses, Snowflake’s architecture separates storage and compute resources, allowing for independent scaling and optimized performance. It supports structured and semi-structured data, providing robust SQL capabilities for data querying and analysis. Snowflake’s multi-cluster architecture ensures high concurrency and workload management, making it suitable for organizations of all sizes. Its seamless integration with various cloud services and data integration tools enhances its versatility in the big data ecosystem.

  1. Databricks

Databricks is a unified data analytics platform powered by Apache Spark, designed to accelerate innovation by unifying data science, engineering, and business. It provides a collaborative environment for data teams to work together on large-scale data processing and machine learning projects. Databricks offers an optimized runtime for Apache Spark, interactive notebooks, and integrated data workflows, simplifying the process of building and deploying data pipelines. Its ability to handle batch and real-time data makes it a powerful tool for big data analytics and AI-driven applications.

  1. MongoDB

MongoDB is a NoSQL database known for its flexibility, scalability, and ease of use. It stores data in JSON-like documents, allowing for a more natural and flexible data model than traditional relational databases. MongoDB is designed to handle large volumes of unstructured and semi-structured data, making it suitable for content management, IoT, and real-time analytics applications. Its horizontal scaling capability and rich query language support complex data interactions and high performance.

  1. Cassandra

Apache Cassandra is a highly scalable and distributed NoSQL database engineered to manage vast quantities of data across numerous commodity servers without a single point of failure. Its decentralized architecture provides high availability and fault tolerance, making it ideal for mission-critical applications. Cassandra’s support for flexible schemas and its ability to manage structured and semi-structured data allows for efficiently handling diverse data types. Its linear scalability ensures consistent performance, making it suitable for use cases such as real-time analytics, IoT, and online transaction processing.

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  1. Elasticsearch

Elasticsearch is a distributed, open-source search and analytics engine built on Apache Lucene. It is designed for horizontal scalability, reliability, and real-time search capabilities. Elasticsearch is commonly used for log and event data analysis, full-text search, and operational analytics. Its powerful querying capabilities and RESTful API make integrating various data sources and applications easy. Elasticsearch is often used with other tools in the Elastic Stack (Elasticsearch, Logstash, Kibana) to build comprehensive data analysis and visualization solutions.

  1. Tableau

Tableau is a robust data visualization tool that empowers users to comprehend and interpret their data effectively. It offers an intuitive interface for crafting interactive, shareable dashboards, enabling the analysis and presentation of data from multiple sources. Tableau supports a broad array of data connections and facilitates real-time data analysis. Its drag-and-drop functionality ensures accessibility for users of all technical skill levels. Tableau’s capacity to convert complex data into actionable insights makes it an indispensable asset for business intelligence and data-driven decision-making.

  1. TensorFlow

Developed by Google, it is an open-source machine learning framework offering a comprehensive ecosystem for creating and deploying machine learning models. It includes a wide array of libraries, tools, and community resources. TensorFlow supports various machine learning tasks, such as deep learning, reinforcement learning, and neural network training. Its flexible architecture allows deployment on various platforms, from cloud servers to edge devices. TensorFlow’s extensive support for research and production applications makes it a leading choice for organizations leveraging machine learning and AI technologies.

  1. Power BI

business analytics tool allowing users to visualize and share insights derived from their data. It provides diverse data visualization options and interactive reports and dashboards accessible across multiple devices. Power BI integrates with numerous data sources, allowing real-time data analysis and collaboration. Its user-friendly interface and robust analytical capabilities suit both technical and non-technical users. Power BI’s integration with other Microsoft services, such as Azure and Office 365, enhances its functionality and ease of use.

  1. Looker

Looker is a contemporary business intelligence and data analytics platform that enables organizations to explore, analyze, and share real-time business insights. It uses a unique modeling language, LookML, which allows users to define and reuse business logic across different data sources. Looker provides a web-based interface for creating interactive dashboards and reports, facilitating collaboration and data-driven decision-making. Its powerful data exploration capabilities and seamless integration with various data warehouses make it a versatile tool for modern data analytics.

  1. Presto

Presto is an open-source distributed SQL query engine crafted for executing fast, interactive queries on data sources of any scale. Initially developed by Facebook, Presto supports querying data in various formats, including Hadoop, relational databases, and NoSQL systems. Its architecture allows for parallel query execution, resulting in high performance and low latency. Presto’s ability to handle complex queries across disparate data sources makes it an excellent tool for big data analytics, enabling organizations to gain insights from their data quickly and efficiently.

  1. Apache NiFi

An open-source data integration tool designed to automate data flow between systems. It features a web-based user interface for creating and managing data flows, allowing users to visually control data routing, transformation, and system mediation logic. NiFi’s robust framework supports real-time data ingestion, streaming, and batch processing. Its fine-grained data provenance capabilities ensure end-to-end data tracking and monitoring. NiFi’s flexibility and ease of use suit a wide range of data integration and processing scenarios, from simple ETL tasks to complex data pipelines.

  1. DataRobot

An enterprise AI platform that automates the building and deploying machine learning models. It provides tools for data preparation, model training, evaluation, deployment, making it accessible to users with varying levels of expertise. DataRobot’s automated machine learning capabilities allow organizations to quickly develop accurate predictive models and integrate them into their business processes. Its scalability and support for various algorithms and data sources make it a powerful tool for driving AI-driven insights and innovation.

  1. Hadoop HDFS (Hadoop Distributed File System)

Hadoop HDFS is the core storage system utilized by Hadoop applications, designed to store large datasets reliably and stream them at high bandwidth to user applications. It divides files into large blocks and distributes them across multiple cluster nodes. Each block is replicated across multiple nodes to ensure fault tolerance. HDFS’s architecture allows it to scale to thousands of nodes, providing high availability and reliability. It is a foundational component of the Hadoop ecosystem, enabling efficient storage and access to big data.

  1. Kubernetes

Kubernetes is an open-source container-orchestration system for automating containerized applications’ deployment, scaling, and management. It provides a robust platform for running distributed systems resiliently, with features such as automated rollouts, rollbacks, scaling, and monitoring. Kubernetes abstracts the underlying infrastructure, allowing developers to focus on building applications rather than managing servers. Its support for various container runtimes and cloud providers makes it a versatile tool for deploying and managing big data applications in diverse environments.


The landscape of big data technologies in 2024 is dynamic and rapidly evolving. Businesses leverage these technologies to gain a competitive edge, from the widespread adoption of cloud-based solutions to advancements in machine learning and artificial intelligence. Staying ahead of these trends is crucial for data professionals aiming to drive innovation and efficiency within their organizations. Consider enrolling in the Post Graduate Program in Data Engineering course to equip yourself with the skills necessary to excel in this ever-changing field.

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