Big Data Technologies to watch in 2022

Today’s article will cover the best big data technologies for dealing with growing large data.

Then we’ll look at big data technologies like Apache Hadoop, Apache Spark, Apache Flume, Kafka, NoSQL, MongoDB, Tableau, and more.

Data abounds. By 2025, 463 exabytes of data will be generated daily, equal to 212,765,957 DVDs!

Every day, billions of emails and tweets are sent. Thus, each person generates an exponential amount of data.

Every day, 2.5 quintillion bytes of data are created. Big data is useless without information. Companies use big data to obtain insights and make profitable decisions.

Big Data market will hit $103B by 2023.

We are all fascinated about big data technologies. So let us explore how big data technologies are used by big organizations and startups to increase corporate revenues.

Big Data Technologies and Tools

This software is designed to analyze, process, and extract information from enormous unstructured data sets that can’t be processed by typical data processing software.

Massive amounts of real-time data required big data processing solutions. They leverage Big Data technology to lessen the chance of failure.

Top big data technologies:

Apache Hadoop

The best large data tool. Apache Hadoop is an open-source software platform for storing and analyzing Big Data. Hadoop stores and processes data on a cluster of commodity hardware.

Hadoop is a low-cost, fault-tolerant, and highly available data processing framework. Hadoop 3.1.3 is the current stable version built in Java. Hadoop HDFS is the world’s most reliable storage.

Apache Hadoop is scalable and resilient.
The architecture is designed to work even in adverse scenarios such as a machine crash.
Hadoop stores data on commodity technology, making it cheap.
Hadoop distributes data storage and processing. This data is processed in parallel, resulting in speed.
Facebook, LinkedIn, IBM, MapR, Intel, Microsoft, and others use Hadoop.

Apache Spark

Apache Spark is an open-source big data solution meant to accelerate Hadoop processing. The Apache Spark project aimed to improve the efficiency and usability of MapReduce’s distributed, scalable, fault-tolerant processing architecture.

It uses in-memory computation to deliver speed. Spark offers high-level APIs in Java, Scala, Python, and R.

Spark can run applications on Hadoop clusters 100 times quicker in memory and ten times faster on disk.
Spark is more flexible than Hadoop since it can operate with different data storage (OpenStack, HDFS, Cassandra).
Clustering, Collaborative, Filtering, Regression, Classification, and other machine algorithms are included in Spark’s MLib package.
On Hadoop, Kubernetes, Apache Mesos, or on the cloud.


MongoDB is a 2009 open-source data analysis tool. It is a NoSQL document-oriented database written in C, C++, and JavaScript.

MongoDB is a popular Big Data database. It helps manage unstructured or semi-structured data that changes regularly.

MongoDB runs on MEAN, NET, and Java. It is also cloud-based.

MongoDB is highly reliable and cost-effective.
It supports aggregation, geo-based search, text search, and graph search.
Includes indexing, sharding and replication.
It is a relational database.
MongoDB is used by Facebook, eBay, MetLife, and Google.

Apache Cassandra

A highly available and scalable NoSQL (Not Only SQL) database, Apache Cassandra is an open-source project.

It is a large data tool that can handle both structured and unstructured data. It uses CQL to interact with the database.

Cassandra’s linear-scalability and fault-tolerance on low-cost hardware or cloud infrastructure make it ideal for mission-critical data.

Cassandra’s decentralized architecture means there is no single point of failure in a cluster.
It is robust and long-lasting.
Cassandra’s performance scales linearly with node count.
It outperforms popular NoSQL alternatives.
Cassandra is used by Instagram, Netflix, GitHub, GoDaddy, eBay, and Hulu.

Apache Kafka

The Apache Software Foundation created Apache Kafka, a distributed streaming platform. It is a fault-tolerant publish-subscriber messaging system with a big data queue.

It allows us to send messages between points. Kafka is used to create real-time streaming data pipelines and applications. Kafka uses Java and Scala.

For real-time streaming data processing, Kafka works well with Spark and Storm.

Apache Kafka can handle large data volumes effortlessly.
Kafka is scalable, distributed, and resilient.
It features high message publishing and subscription throughput.
Kafka is used by LinkedIn, Twitter, Yahoo, and Netflix.

Data from the searchable repository is captured and indexed by Splunk, resulting in meaningful graphs, reports and alerts.

Real-time data processing support
Formats accepted include JSON, CSV, and log files.
Business metrics can be monitored and choices made with Splunk.
We can use Splunk to monitor any IT system.
Splunk allows us to include AI in our data approach.
Some of the biggest names in IT use Splunk.


Instant BI and data visualization using QuickView. For turning raw data into knowledge, it is unsurpassed. QuickView allows customers to gain business insights by investigating how data is connected and unrelated.

Quietly and clearly, QuickView adds new levels of analysis, value, and insight to current data warehouses. It allows users to search directly or indirectly through all data in the program.

Nothing happens when the user clicks a data point. The other fields are user-selectable. It encourages free data analysis, allowing consumers to make better judgments.

QuickView has an in-memory storage function that speeds up data collecting, integration, and analysis.
For Associative Data Modeling
QuickView software automatically determines data relationships.
It enables global data discovery.
Support for mobile and social data discovery.


It analyzes and visualizes data. Users can dynamically search and pick data from multiple sources using Qlik Sense’s associative QIX engine.

It is used by both technical and non-technical individuals to analyze data. Qlik Sense is the greatest tool for displaying and analyzing data.

A user can simply generate an analytical report in the form of a tale using a drag and drop interface. On a central hub, clients can exchange apps and reports, export data stories to improve business, and safeguard data models.

Qlik Sense employs the associative model.
It has a central hub or dashboard where all Qlik files and reports can be shared.
Qlik sense can be implemented in an app to collect data.
It compares data in memory.
Data analysis is aided by interacting with charts and visualizations via ‘smart search’.


Tableau is a strong data visualization and analytics software solution.

It is the ideal tool for converting raw data into a format that is easily understood without coding experience.

Tableau helps users to work on real data, turning it into meaningful insights and improving decision-making.

It provides quick data analysis and visualizations in the form of interactive dashboards and workbooks. It syncs with other Big Data tools.

You can create visuals in Tableau using basic drag and drop techniques.
Databases, spreadsheets, non-relational databases, big data and data warehouses are just a few of Tableau’s data sources.
It’s tough and safe.
It provides real-time sharing of data visualizations, dashboards, sheets, etc.

Apache Storm

It’s a distributed real-time computing system. Storm uses Clojure and Java. Apache Storm reliably processes unbounded data streams. It’s a simple tool that works with any language.

Apache Storm can be used for real-time analytics, online machine learning, ETL, and more.

Storm is free and open-source.
It’s scalable.
Storm is robust and easy to set up.
Storm ensures data processing.
It can handle millions of tuples per second per node.
Many companies, including Yahoo and Alibaba, use Apache Storm.

Apache Hive

Hive is an open-source Big Data analytics tool. For querying unstructured data, Hive employs Hive Query Language (HQL).

This framework allows developers to process data stored in Hadoop HDFS without writing complex MapReduce jobs. Hive has a CLI interface (Beeline Shell).

Apache Hive supports all client applications written in different languages.
It minimizes the complexity of MapReduce jobs.
HQL syntax is like SQL. So SQL experts can easily write Hive queries.

Apache Pig

It’s a way to make MapReduce jobs easier. Yahoo created a pig to make developing Hadoop MapReduce applications easier. Pig Latin is a scripting language intended for the pig framework that runs on Pig runtime.

The compiler converts Pig Latin commands to MapReduce programs in the background. It converts Pig Latin to MapReduce programs for YARN data processing.

Pig allows users to define custom functions for certain tasks.
It is suitable for complex use cases.
It can handle any data.


Facebook created Presto, an open-source query engine (SQL-on-Hadoop) for interactive analytics on petabytes of data. It can query data in Cassandra, Hive, proprietary data storage, and relational databases.

Unified analytics across the organization using a single Presto query. It does not use Hadoop MapReduce and may fetch data in seconds to minutes.

Presto is simple to install and debug.
Presto has a modular architecture.
Allow user-defined functions.

Apache Flink is a stateful distributed processing engine for finite and unbounded data streams.

It’s in Java and Scala. Apache Flink runs in memory in all popular cluster setups.

It lacks a single point of failure.

Flink has high throughput and minimal latency.
It can handle thousands of cores and gigabytes of data.
Flink is used in event-driven, data analytics, and data pipeline applications.
Alibaba, Bouygues Telecom, BetterCloud, and others use Apache Flink.

Apache Sqoop

Apache Sqoop is a top-level open-source project. It’s a tool for moving large data sets between Apache Hadoop and structured datastores. Relational databases like MySQL, Oracle, etc.

Sqoop can import data from a relational database into HDFS or export data from HDFS to a relational database.

Sqoop has connections for all major relational databases.
With Sqoop, we can load all database tables with one command.
Apache Sqoop allows us to incrementally load the table as it is updated.-


RapidMiner is a popular Data Science tool. For Data Science Platform, it was ranked #1 by Gartner in 2017. It is a sophisticated data mining tool.

Rapidminer is a data preparation, machine learning, and deep learning tool.

RapidMiner features:
It provides a single platform for data processing, modeling, and deployment.
RapidMiner Radoop integrates with the Hadoop framework.
RapidMiner can automate predictive modeling.


KNIME (Konstanz Information Miner) is an open-source data analytics tool. It’s in Java.

It lets users design Data Flows, run analytic processes, and view results, interactive views, and models. KNIME is a good SAS substitute.

KNIME supports straightforward ETL procedures.
And we can do it in different languages and technologies.
It has several integrated tools and powerful algorithms.
Intuitive KNIME setup
It is not unstable.


Elasticsearch is Lucene-based. It’s a Java-based open-source database server. It does full-text searches and analyses using an HTTP Web Interface and JSON documents.

A complex framework for language-based searches is used to store unstructured data from many sources.

Elasticsearch is stable and scalable.
It is fast even when searching enormous data sets.
Elasticsearch leverages schema-free JSON documents and simple RESTful APIs to search, index, and query data.
It takes JSON documents and has no schema.


So we’ve seen Apache Hadoop, Apache Spark, MongoDB, Cassandra, and more in this article.

The post also mentioned QlikView, Qlik Sense, and Tableau. Other large data technologies include Apache Hive, Pig, Storm, Flink, and others.

After reading this essay, I hope you understand the top big data technologies and why we use them. Now it’s up to you to make your move.

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