Must-Know Big Data Technologies for 2023: A Comprehensive Guide by IQCode

Top Big Data Technologies: A Guide to Data Storage, Mining, Analytics, and Visualization

In today’s digital era, companies deal with vast amounts of data every day, leading to the term “Big Data” for this massive collection of structured and unstructured data. As traditional software cannot handle this scale and complexity, enterprises are turning to Big Data technologies to store, process, and analyze vast amounts of data. This guide explores the top Big Data technologies ready to transform the technical field and bring substantial benefits in 2023.

Types of Big Data Technologies:
– Data Storage (Apache Hadoop, MongoDB, RainStor, Cassandra)
– Data Mining (Presto, RapidMiner, ElasticSearch)
– Data Analytics (Kafka, Splunk, KNIME, Apache Spark)
– Data Visualization (Tableau, Plotly)

To choose the most suitable Big Data technology, it’s important to review and compare its features. This guide highlights the features of these technologies and companies that utilize them. Let’s dive into the world of Big Data and explore the top Big Data technologies to look out for in 2023.

Understanding Big Data Technology

Big Data refers to the vast amount and types of data that require innovative methods to process and analyze. Companies use Big Data technologies to gain insights for making profitable decisions. These technologies are software utilities designed to extract information from extremely complex datasets.

Big Data technologies bridge the gap between traditional data technologies and expanding data and business needs. They include frameworks, methods, tools, and techniques for storing, examining, analyzing, and evaluating data. Real-time data analysis with Big Data Processing Technologies reduces future risks and is essential in the internet era.


Big Data Technology can be classified in two categories:

1. OPERATIONAL BIG DATA TECHNOLOGIES: This type of Big data corresponds to the data generated from day-to-day operations like social media platforms, internet transactions, or any information from a particular company. It is used as raw data to be analyzed by Operational big data technology. For instance:

* Online ticket booking systems.

* E-commerce websites.

* Social networking sites.

* Employee data or executive details in multinational companies.

2. ANALYTICAL BIG DATA TECHNOLOGIES: This type of big data pertains to analyzing data that are relevant to business decisions. It is used when performance metrics are involved and when critical business decisions need to be taken on the basis of operational data analysis. Some examples of analytical big data technologies include:

* Stock marketing data.

* Weather forecasting data.

* Medical records for patients monitoring.

* Maintaining space mission databases.

Note: We can use Python, R, or any other programming language to analyze Big Data.

Big Data Technologies Overview

Big data technologies have revolutionized the market and IT industries by providing efficient data management and analysis solutions. These technologies can be categorized as Data Storage, Data Mining, Data Analytics, and Data Visualization.


//category 1: Data Storage
– Hadoop Distributed File System (HDFS)
– Amazon Simple Storage Service (S3)
– Cassandra Database

//category 2: Data Mining
– Apache Mahout
– Knime
– IBM SPSS Modeler

//category 3: Data Analytics
– Apache Spark
– Apache Flink
– RapidMiner
– Splunk

//category 4: Data Visualization
– Tableau
– D3.js
– QlikView

These are some of the popular big data technologies used by various companies for effective management and analysis of their data. By incorporating these technologies, companies can draw insights, make informed decisions, and gain a competitive advantage in the market.

Big Data Technologies for Data Storage

Big Data Technology for Data Storage includes infrastructure to fetch, manage, and store massive amounts of data with software that processes data effortlessly. The following are some widely used Big Data Technologies for Data Storage:

1. Apache Hadoop

Apache Hadoop is a distributed storage platform that processes Big Data utilizing the MapReduce programming model. The Hadoop framework includes five modules, Hadoop Distributed File System (HDFS), Hadoop YARN, Hadoop MapReduce, Hadoop Common, and Hadoop Ozone. Hadoop automatically handles hardware failures. Apache Hadoop is used by companies like LinkedIn, Intel, IBM, Facebook, and more.

– A distributed file system called HDFS enables fast data transfer between nodes
– HDFS is fundamentally a resilient file system
– Inexpensive, fault-tolerant, and extremely flexible
– MapReduce is a built-in batch processing engine in Hadoop

2. MongoDB

MongoDB is a document-oriented database designed to handle large amounts of data with document-oriented NoSQL features and MapReduce calculation capabilities. Some features of the document-based database system makeup include easy aggregation, dynamic queries, secondary indexes, and rich updates. Companies like Facebook, eBay, Google, etc. use MongoDB.

– Seamlessly integrates with languages like Ruby, Python, and JavaScript
– MongoDB stores data in JSON documents
– Native programming languages map to JSON documents effortlessly
– Able to handle dynamic queries, secondary indexes, rich updates, and easy aggregation

3. RainStor

RainStor is a database management system developed by RainStor Company that manages and analyzes Big Data and eliminates duplicate files. The RainStor product is available in two editions, Big Data Retention and Big Data Analytics on Hadoop. Barclays, Credit Suisse, and Reimagine Strategy are companies that use RainStor.

– Able to manage and analyze Big Data at the lowest total cost
– Runs faster queries and analyses using both SQL queries and MapReduce
– Provides the highest compression level, data compressed up to 40x
– Eliminates the need for re-inflation when accessed

4. Cassandra

Cassandra is a distributed NoSQL database that enables in-depth analysis of numerous sets of real-time data. Cassandra utilizes CQL to interact with the database. With scalability and fault tolerance for cloud infrastructure or commodity hardware, Cassandra is the ideal platform for mission-critical data processing. Facebook, Netflix, and GitHub are some companies that use Cassandra.

– Cassandra’s decentralized architecture prevents single points of failure within a cluster
– Supports ACID (Atomicity, Consistency, Isolation, and Durability)
– Ideal for enterprise applications due to data sensitivity
– Supports Hadoop integration with MapReduce, Apache Hive & Apache Pig
– Cassandra is infinitely scalable to accommodate more customers and data as required.


Data mining extracts valuable information from large, constantly streaming and highly variable raw data. It requires special techniques such as big data technologies. Some widely used technologies are:

Presto is an open-source SQL query engine that supports fast analytic queries on data sources of various sizes, both relational and non-relational. Companies like Repro, Airbnb and Netflix use it. Its key features include the ability to query data where it lives, retrieve data quickly and support multiple data sources in one query.

RapidMiner is an advanced open-source data mining tool primarily used for predictive analysis. Companies like Windstream Communications and McKinley Marketing Partners use it. Its key features include an integrated platform for processing data, machine learning model building, deployment, and various other capabilities.

Elasticsearch is an open-source search and analytics engine used for searching, indexing, and analyzing data of all types. Companies like Facebook, Shopify, and Uber use it. Its key features include its ability to store and analyze structured and unstructured data up to petabytes, providing simple RESTful APIs and schema-free JSON documents, and scalability.

Overall, these data mining technologies are powerful tools used by companies to turn raw data into useful insights.

Data Analytics

Big data analytics involves extracting essential information from raw data by cleaning, transforming and modeling it through data analytic techniques. Valuable insights can be gained from big data analytics tools, including hidden patterns, correlations, customer preferences, and statistical information about the market. Here are a few types of data analysis technologies:

8. Kafka

Kafka is an open-source, fault-tolerant messaging system based on publish-subscribe model that can handle massive data volume. It can analyze streaming data at scale and is used by thousands of organizations for streaming analytics, data integration, and high-performance data pipelines. Companies such as Netflix, Goldman Sachs, and Uber use it.

Key features:
– Kafka scales effortlessly without any downtime in four dimensions
– Kafka is very reliable due to its distributed architecture, partitioning, replicating, and fault-tolerance.
– Higher throughput for message publishing and subscription
– Zero downtime and no data loss

9. Splunk

Splunk is a scalable and advanced software platform that searches, analyzes and visualizes machine-generated data from websites, sensors, devices, and applications. It provides metrics, diagnosis, and insight into business operations, web analytics, business intelligence, security, and compliance. Big data can be ingested from diverse sources, including machine data, and can be analyzed. Companies such as JP Morgan Chase and Wells Fargo use Splunk.

Key features:
– Splunk improves business performance with automated operations, advanced analytics, and end-to-end integrations.
– Splunk indexes the ingested data to enable faster search and querying
– It provides analytical reports including interactive graphs, charts, and tables
– Easy sharing of the reports with colleagues


KNIME is a free platform for analytics, reporting, and integration of large sets of data. It is a user-friendly and open-source platform that provides hundreds of modules for integration, data transformation and analytics, mining and text analytics. Companies such as Fiserv and Procter & Gamble use KNIME.

Key features:
– Additional plugins are added via the extension mechanism to extend functionality
– Integrates many open-source projects such as Spark Library for machine learning and development interfaces for Java, Scala, Python, and R
– KNIME workflows can serve as data sets for creating report templates that can be exported to various file formats
– You can perform simple ETL operations

11. Apache Spark

Apache Spark is an open-source analytics engine supporting big data processing. It is designed with In-Memory Computing (IMC), enabling fast queries against data of any size and a generalized Execution Model (GEM) for supporting a wide range of applications that run 100 times faster than Hadoop MapReduce. Java, Python, and Scala APIs are available for ease of development with Spark support for SQL queries, machine learning, streaming data, and graph processing. Companies such as Amazon and eBay use it.

Data Visualization: Top Tools


Data visualization presents data through graphics to easily interpret patterns and anomalies. The use of charts, maps, and graphs simplifies the representation of large amounts of data. Here are two top data visualization tools:

12. Tableau:
Tableau is the fastest-growing tool for data visualization, commonly used for creating graphs, charts, and dashboards. It is user-friendly and requires no programming experience. Data is analyzed rapidly, resulting in interactive worksheets and dashboards displaying valuable insights for decision-making.
Notable features include data blending, real-time analytics, and live datasets. Tableau supports a variety of data sources such as on-premise files, relational and non-relational databases, and cloud data. Some companies that use Tableau are Coca-Cola, Wells Fargo, and Nike.

13. Plotly:
Plotly is a Python toolkit for interactive data visualization and can be used interactively within Jupyter notebooks and Pycharm. It is easy to use and more cost-effective than some other tools. It offers a richer set of libraries and APIs, including Python, R, MATLAB, Arduino, Julia, etc. Plotly supports unique features such as interactive charts, with the ability to include buttons, drop-downs, and sliders. The tool provides full control over the plotted data set, with a lot of chart types to choose from. It also has a vast range of AI and machine learning charts. Companies that use Plotly are Bitbank and Paladins.

Big Data Technologies for the Future

The future of Big Data technology is promising and new innovations are likely to gain popularity as demands increase. Tools such as Apache Hadoop, Apache Spark, MongoDB, Cassandra, and Plotly are among those available for storing, mining, analyzing, and visualizing data. Thorough research is important before selecting a tool to ensure its unique features align with your organization’s needs. Identify the type of problems your organization faces and select the tool that best fits your requirements. We hope this article helps readers navigate the world of Big Data technologies.

Big Data Resources

Explore these resources to learn more about Big Data:

- Big Data Tools
- Big Data Projects
- Big Data Engineer
- Applications of Big Data
- Big Data Interview Questions
- Characteristics of Big Data

Search for these online or use the links provided to learn about this exciting field.

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