Top Big Data Technologies that you need to know in 2023

Table of Contents

Do you post Instagram reels? Like your friend’s Facebook post? If so, you just created data on the internet. Now and then, an infinite quantity of data is generated worldwide. We know tape drives to cloud storage numerous methods are used for handling the data. But what is Big data? In recent years many of us may hear the word big data and Top big data technologies are depicted as a demanding future. The data which cannot be stored, processed, and analysed by the traditional system in a given time is called big data. Dealing with complex and massive amounts of data requires the use of big data technologies and tools.

The scale of big data differs from organisation to organisation, time to time, and system to system. This means a particular figure cannot be fixed to call it big data. For instance, if a pen drive only has 1 GB of storage space and 1.5 GB of data must be kept there, the remaining 0.5 GB is considered big data for that specific device. Here, Big data technologies enter as a saviour and manage an infinite number of databases. From maintaining an organization’s database to disaster management, the role of big data technologies is inevitable.

Big data technologies are designed to handle all types of data. The types of data include

Structured data
Semi-structured data
Unstructured data

Structured data

The data in which information is predefined, organized, and easy to access and the process is Structured data. A few examples are

Phone numbers (10 digits)
Name of an individual (First name, middle name, last name)
Banking information, etc.,

Semi-structured data

The data which has the properties of both structured data and unstructured data is semi-structured. This does not require a predefined, fixed schema but it is more flexible. It is often described as a self-describing structure that evolves when new attributes are added and have nested information.

HTML code, graphs and tables, e-mails, Zipped files
JSON, CSV, XML
Electronic data interchange (EDI), etc.,

Unstructured data

The textual or un-textual data created by either humans or machines contributes to unstructured data. The majority of big data is unstructured data that does not have a predefined data model or a particular format. For example,

Web pages
Text files
Audio files
Videos
PowerPoint presentations
Images (GIF, JPEG, etc.,)
Online data created by customers and many more

Top Big Data technologies

We can classify top big data technologies into 4 groups:

Data Storage
Data Mining
Data Analytics
Data Visualization

Top big data technologies used in data storage

Hadoop

Hadoop is an open-source software platform used to store and process massive amounts of data sets across computer clusters using simple programming models. The processing power of Hadoop is extensive which allows it to handle boundless tasks simultaneously. The framework is known for its reliability, scalability, and faster data processing.

One of the main reasons why Hadoop is an emerging big data technology is its high availability. This feature enables us to access data in any undesirable conditions such as Name Node failure, machine crash, Data Node failure, etc without any downtime. Hence, there is no need to depend on hardware for high availability, as Hadoop has the feature of extra NameNode (Passive Standby NameNode), which withstands more than one failure simultaneously.

Modules of Hadoop:

HDFS: Based on Google’s GFS paper Hadoop Distributed File System (HDFS)is developed. It provides high-throughput access to data by breaking the files into blocks and Storing them in nodes in a distributed architecture.

Hadoop Common is a collection of common utilities supporting the other Hadoop modules.

Hadoop YARN: A framework for managing the cluster resource and job scheduling.

Hadoop MapReduce: It is a YARN-based programming framework that parallelly processes large data sets. Regarding processing, we can say MapReduce is the heart of Apache Hadoop.

Written in: JAVA, Coding can also be done in Python and C++ language.

Organisations using Apache HADOOP

Many organisations and institutions are using Apache Hadoop either for educational or productional purposes. Here are some examples.

Adobe -Hadoop is used for data storage and processing.

Crowdmedia-To do a statistical analysis on trends in Facebook and other social media networks.

LinkedIn-To create web analytic reports, storage, log analysis, pattern analysis, and much more

Zillow, Redfin, and Trulia -Uses Hadoop to democratise data for real estate consumers employing customer analytics.

MongoDB:

In terms of data storage in big data technology, MongoDB NoSQL is considered to be an essential core component. Since it uses a NoSQL database, it differs from the traditional RDBMS database. With the help of schema documents and various data storage structures, it can accommodate a large quantity of data.

As an emerging big data technology, MongoDB provides flexibility in handling a wide variety of data types among distributed architectures. A default storage engine, WiredTiger is used in MongoDB to write all the data consistently in a snapshot to disk, across all data files. A document-level concurrency model, checkpointing, and compression are provided by WiredTiger.

Three components of MongoDB:

Atlas- Developer data platform for deploying, running, and scaling MongoDB in the cloud.
Enterprise Advanced- Commercial edition for large companies which supports mission-critical, advanced security features and automated administration.
Community Edition- It is free software used by millions of people to access and analyse data.

Written in: C, C++, C#, Go, Java, Python, Ruby and Swift, PHP, Rust.

Organisations using MongoDB

Bosch

Bosch uses MongoDB Atlas to address the potential of big data in the electrical and automotive industries. Future-oriented solution creation also makes use of it.

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Sanoma

Do you know in Sanoma’s Bingle app, learning exercises scaling went from 1.5 to 12 million per day during the pandemic outbreak? It became possible with MongoDB’s open-source database.

L&T-SuFin

With the help of MongoDB Atlas L&T-SuFin cost-effectively manages the data and focuses more on developing new features by freeing the developer time. It continues to transform the B2B marketplace for industrial products in India.

Other organisations using MongoDB

Nextar, Noodoe, Toyota, Forbes, etc.,

Rainstor

A database management system developed to manage and process an organisation’s big data requirements. In 2004, RainStor Software Company developed this database.

In a world of big data where we speak about petabytes and exabytes, the pivotal requirement is security. Rainstor database provides salient features to encrypt, authenticate, and audit ‘high-value’ data in all manners. Once data is saved, it cannot be changed. New data or changed one can be added but it is impossible to replace thus providing an immutable data storage model. Hence, highly regulated firms such as banks, government organisations, and finance companies opt for Rainstor.

The deduplication Techniques of Rainstor discard the duplicates where no data sets are lost and maintain integrity.

Extreme compression focuses on storing voluminous data with a compression ratio of 40:1 to 100:1. So, we can refer to the vast amount of data just by unlocking the power of data you already have. Another important feature of Rainstor is it is cost-effective.

Operates like: SQL.

Latest stable version: RainStor 5.5

Organisations using Rainstor:

Barclays, Credit Suisse, etc, are the finance industries using Rainstor for their big data needs.

Impetus -A technical company using the Rainstor database.

Data Mining

Now, let us see the Top big technologies which are used in Data mining

Presto

Presto is an open-source Distributed SQL-based Query Engine. It is used to run interactive Analytic Queries against the data sources of every scale and the size ranges from Gigabytes to Petabytes.

Presto allows data querying in Hive, Cassandra, Relational Databases, and Proprietary Data Stores.

Difference between SQL server and Presto

	SQL	PRESTO
Category	Database Tool	Big Data Tool
Key factor	Reliable and easy to use	Works directly on files in s3(no ETL)

Written in: JAVA

Latest stable version: Presto 0.280

Organisations using Presto

UBER

Uber employs Presto for its SQL Data Lakehouse, where more than 7,000 users log in every week and execute 500K queries per day.

Alibaba

Data Lake Analytics in Alibaba is utilizing the federated query engine features of Presto. It accumulated several successful business use cases that highlight the power of Presto’s analytics potential.

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Blinkit

Presto on AWS serves Blinkit, the top quick delivery service in India, enabling them to stand by their motto “everything delivered in 10 minutes.” To have adaptability and market valuation, Blinkit switched from their cloud data warehouse to Presto on S3.

Other organisations using Presto

Facebook, Netflix, Checkr, Airbnb, Checkr, Repro, and Twitter

ELASTIC SEARCH

Elasticsearch, a distributed search and analytics engine built on the Lucene Library, can handle a growing number of use cases. The Elastic Stack’s brain saves your data centrally for precise and blazing-fast search. It was developed by Elastic NV in 2012.

For the following three reasons, Elastic’s ELK analytics stack is becoming more popular in online analytics use cases.

With a tiny test dataset, starting an Elasticsearch toy instance is very simple.

Compared to more complicated systems like Hadoop’s MapReduce, Elasticsearch’s JSON-based query language is much simpler to learn.

Application developers can effortlessly handle a brand-new technology stack like Hadoop with a second Elasticsearch instance.

Written in: JAVA

Latest stable version: Elasticsearch 7.1

Organisations using Elasticsearch

Walgreens:

By providing a superior product catalogue search experience, one of the

biggest retail companies, Walgreens streamline their online grocery using

Elasticsearch.

Vimeo:

Elasticsearch is used by one of the largest video hosting businesses, Vimeo, to search through millions of videos daily.

eBay:

eBay is one of the biggest businesses using Elasticsearch for application search, searching through 800 million listings in milliseconds and providing millions of users with a top-notch end-user experience every day.

Data Analytics

Following are the top big data technologies used in data analytics

SPLUNK

Splunk correlates and indexes data into a searchable container, enabling the creation of notifications, reports, and visualisations. It provides tools and plug-ins that make developing and accessing Splunk apps simpler. It was developed in 2014 by Splunk INC.

Languages used: AJAX, C++, Python, XML

Latest stable version: Splunk 7.3

Organisations using SPLUNK

The number of businesses utilising Splunk is enormous.

Accenture:

Accenture and Splunk have partnered to provide customers with data-driven solutions. It is achieved by combining Splunk products with Accenture’s high-performing IT, Security, Business Analytics, and IoT.

Splunk in the public sector:

Splunk’s security, IT, and observability solutions are used by thousands of public sector entities in the United States, including all 3 branches of government, and more than twelve cabinet-level departments.

Other companies use Splunk

Dominos, Lenovo, Porsche, BookMyShow, John Lewis, Kurt Geiger, and Telenor.

R-LANGUAGE

R is a free software framework and programming language for statistical computing as well as graphics. When creating statistical software, statisticians and data miners frequently use the R program, especially in data analysis.

For importing and cleaning data, many quantitative researchers use the R programming language as a tool. In the year 2000, it was developed by R-Foundation.

Written in: Fortran

Latest stable version: R-3.6.0

Organisations using R-LANGUAGE

Facebook

Data science managers, data analysts, and data scientists use R programming to analyse user behaviour with status posts and profile pictures on meta platforms like Facebook.

Google

For advertising efficacy and economic forecasting, Google’s data scientists, cloud AI architects, and analysts use the R programming language.

Other organisations using R-LANGUAGE

American Express, Blackrock, Bank of America, Citibank, Barclays Bank, and ANZBharti Axa Insurance are some of the organisations using R-programming for descriptive statistics.

Data Visualization

Some of the top big data technologies used in data visualisation are discussed below

Tableau

The visual analytical platform Tableau is one of the top big data technologies that empower the business industry. Not only business analysis but also preparing, analysing, and sharing insights of complex data becomes faster with Tableau. It was developed by Tableau on May 17.

Written in: JAVA, C++, Python, C

Current stable version: TableAU 8.2

Organisations using Tableau

Bentley motors

For a sustainable future, Bentley motors use Tableau as it provides

Strong data culture at all stages of business,
Customer satisfaction with unified data
employees utilising self-serving analytic data

UN World Food Programme

Utilizing the data visualisation tool Tableau, WFP focuses on its workforce better and uses its data effectively.

Plotly

JavaScript is not required while using Plotly to complete automated tasks. It provides an extensive range of charts and graphs. Hence geographical, statistical, and scientific data charts can be created and hosted online. Even beginners can read effortlessly because of its interactive features. It was developed in the year 2012.

Written in: JavaScript

Current stable version: Plotly 1.47.4

Organisations using Plotly

Bitbank, Paladins

Emerging Big Data Technologies

In addition to the top big data technologies above, let us discuss some emerging big data technologies.

Kubernetes

Also called K8S was open-sourced by google in 2008. It is used to deploy, scale, manage and automate containerized applications.

Developed by: Cloud Native Computing Foundation in 2015.

Written in: Go

Current stable version: Kubernetes 1.14

Companies using Kubernetes: Google, Shopify, Udemy, The New York Times,Delivery Hero, etc.,

Airflow

As an emerging big data technology, Air flow helps you to move data from a source to a destination. The workflows which are made of Directed Acyclic Graphs (DAGs) can author, schedule and monitor data pipelines.

Developed by: Apache Software Foundation in 2019.

Written in: Python

Current stable version: Apache AirFlow 1.10.3

Companies using Airflow: Walmart, Robinhood, Slack, Airbnb.

Beam

Beam is a portable, extensible, unified, open-source big data technology. It reads your data from multiple supported sources, processes it, and writes the results in the most popular destination of data flow.

Developed by: Apache Software Foundation, 2016

Written in: JAVA, Python

Current stable version: Apache Beam 0.1.0 incubating

Companies using Beam: Handshake, Dreamdata, Yintrust, Thumbtack, etc.,

Docker

One of the emerging big data technologies Docker is recognized as the most loved tool in stack overflow’s developer survey. This open-source platform includes UIs, CLIs, APIs, and security to develop a fast and portable application. The creation, deployment, and running of applications can be done using software containers.

Developed by: Docker INC,2003

Written in: Go

Current stable version: Docker 18.0

Companies using Docker: Pinterest, Spotify, Twitter, CRED, etc.,

Conclusion

Hope I have sowed some seeds of ideas about top big data technologies. From decision-making to customer service, organizations utilize big data in every aspect. Hence, there will be a promising scope in the future if we opt to enhance our knowledge of big data technologies.

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