Big Data Tools, Characteristics and Technology

Writer : Mis. Rossela Ilkan

Big Data has become a critical component of any company's decision-making process and a means of gaining an advantage over the competition. Therefore, Apache Spark and Cassandra are in high demand as Big Data technologies. In order to get the most out of the data generated by the company, companies are looking for professionals who are skilled in using them.

These data tools are useful for analyzing large amounts of data and discovering patterns and trends. Consequently, if you want to work in the Big Data industry, you need these tools.

In this article, we'll take a look at some of the most popular Big Data technologies.

It's all about Big Data Tools and Technology

1. Apache Storm

Real-time data streams can be processed with Apache Storm, a distributed computing system. In Java and Clojure, it can be used with any programming language. Nathan Marz created the software, which was later purchased by Twitter in 2011. The following are some of Storm's most important features:

  • Has a tremendous amount of flexibility.
  • The node can process more than a million tasks in a fraction of a second.
  • Processing of data in real time
  • Until the user shuts it down or an unexpected technical failure occurs, the Storm topology is running.
  • It guarantees that every tuple will be processed.
  • It can be run on a Java Virtual Machine (Java Virtual Machine)
  • Apache Storm has Storm's back (DAG) Graph topology in direct acrylic
  • A medium or large organization may use it because of its open-source nature and because it is adaptable and durable.
  • It's quick and responsive. End-to-end delivery response and data refresh can be performed in seconds, depending on the nature of the data problem.
  • Regardless of message loss or cluster node failure, Storm will continue to process data.


This is how a MapReduce job works in Apache Storm. However, unlike in Apache Spark, where data is processed in batches, here it is processed in real-time.

A REST API is provided by the Storm UI daemon that allows you to perform the following:


  • Get metrics data from the Storm cluster by interacting with it.
  • Activate and deactivate topologies, as well as configure data.
  • Nodes are processed at least once even if a failure occurs.


All of this makes Storm one of the most popular Big Data technologies in use today.

2. MongoDB

As an advanced alternative to current databases, this is an open-source NoSQL database. Large amounts of data can be stored in a document-oriented database such as this one. The traditional database will be replaced by a collection of documents rather than rows and columns.

Key-value pairs are the building blocks of documents, which are organized into collections. Companies that need to make quick decisions and work with real-time data should use MongoDB. Mobile applications, product catalogs, and content management systems are all common sources of data that can be stored using the Big Data technology.

The following are some of the most common reasons for implementing MongoDB:

  • Organizations benefit from its adaptability because of the way data is stored in documents.
  • Regular expressions, range queries, and searching by field name are all supported. You can query a document to return a list of fields.
  • It is possible to index every field in a MongoDB document in order to improve search quality.
  • Because it distributes data across multiple MongoDB instances, it is excellent at load balancing Data is duplicated for load balancing in the event of a technical failure, and the system can run on multiple servers at once.
  • Integers, strings, Booleans, arrays, and objects can all be stored.
  • Using dynamic schemas, this technology allows you to store and prepare data more quickly, resulting in lower costs. Learn how MongoDB can be used in real-time applications.

3. Cassandra

Database management system Cassandra is designed to handle large amounts of data distributed across multiple servers. This is one of the most widely used Big Data technologies for dealing with structured data sets. NoSQL was the original goal when Facebook created it. Incorporated giants like Netflix, Twitter, and Cisco are now using it.

Cassandra's most exciting features include the following:

  • Because it has an intuitive query language, migrating from a relational database to Cassandra will be a breeze.
  • All data can be accessed and written on any node thanks to the Masterclass architecture
  • There is no single point of failure because the data is replicated across multiple nodes. Even if one of the nodes stops working, the data on the other nodes will still be accessible.
  • Multi-datacenter replication is another option. Data can be retrieved from other data centers if it is lost or damaged in one data center.
  • Built-in safeguards, such as restore points and data backups, are included.
  • This tool can be used to identify and recover nodes that have gone down.

Streaming data from devices and sensors has made Cassandra a popular choice for IoT real-world applications. Social media analytics and customer data management are two of the most common uses for it.

4. Cloudera

Big Data technology Cloudera is among the fastest and most secure on the market today. As an open-source Apache Hadoop distribution, it was designed from the start with large-scale implementation in mind. You can access data from any environment with this scalable platform.

Cloudera has a number of advantages that will benefit your project, such as:


  • Analyzes real-time data to provide actionable insights.
  • In addition to AWS, Google Cloud, and Microsoft Azure, Cloudera Enterprise can be deployed on a variety of cloud platforms.
  • Data models can be developed and trained by Cloudera.
  • Data clusters can be spun up or down at any time. When you only need it, you can pay only for it when you need it.
  • Incorporates an enterprise-level hybrid cloud platform

There are five bundles of Cloudera software, support, and service that can be used on-premise or in the cloud:


  • Cloudera Enterprise Data Hub
  • Cloudera Analytic DB
  • Cloudera Operational DB
  • Science and technology in the field of Cloudera Data Science
  • Cloudera Essentials

5. OpenRefine

Cleaning and converting data is easy with OpenRefine, a powerful Big Data tool. With this tool, you'll be able to work with large datasets with ease. The following are some of this tool's most notable attributes:

  • Several web services can be used to expand your data set.
  • Add data from a variety of sources.
  • Perform cell transformations on cells with multiple data values.
  • Advanced data operations can be performed with Refine Expression Language.
  • The tool allows you to quickly and easily explore large data sets.


The Big Data technologies we've discussed here will benefit any company looking to boost profits, gain a deeper understanding of their customers, and create better products.. Even better, you can get a head start on mastering these skills by using free online tutorials and resources.

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