Top 9 Big Data Technologies To Look Out For!

Prior to the introduction of big data technology, data was managed using common programming languages and rudimentary structured query languages. However, these languages were not efficient enough to handle the data because each organization's information, data, and domain have grown continuously. 

 

As a result, it became critical to handle such massive data and implement an efficient and stable technology that addresses all of the client and large organization's objectives and demands, as well as those responsible for data production and control. Big data technologies are the buzzword of the moment for all such requirements.

 

The world of big data is only expanding: every year, organizations of all sizes generate more data in numerous ways. Companies are investing more in big data tools and technology as they seek to leverage all that data to improve operations, better understand customers, provide products faster, and get other business benefits through analytics applications.

 

What are Big Data Technologies?

 

Big data technology is classified as a software utility. This technique is primarily intended to analyze, process, and extract information from a large data set and a massive set of exceedingly complex structures. This is extremely tough for standard data processing software to handle.

 

Big data technologies are frequently related with many other massively augmented technologies such as deep learning, machine learning, artificial intelligence (AI), and the Internet of Things (IoT). Big data technologies, in conjunction with these technologies, are focused on analyzing and handling massive amounts of real-time and batch-related data.

 

Big data technology is a collection of data collection and analysis methodologies, tools, and expertise. The goal is to get insights that will help you make better decisions and achieve better business results.

 

How do big data technologies function? Using specialized data analytics software. First, data is gathered from a variety of sources, including written documents, emails, photos, video, mobile text messaging, social media comments, and so on.

 

Then, using special software tools, you can store, analyze, and visualize data in an easy-to-understand format, allowing you to discover patterns and trends in data and draw valuable conclusions and judgements. The big data ecosystem (or landscape) includes all of these (technologies and data sources).

 

Types of Big Data Technologies

 

As previously said, Big Data technologies attempt to assist users in storing, managing, integrating, processing, and finding answers. The many big data technologies and methodologies can be broadly classified:

 

1. Data storage:

 

Big data technology that deals with data storage has the ability to retrieve, store, and manage large amounts of data. It is made up of infrastructure that lets consumers store data in a convenient location. The majority of data storage platforms are interoperable with other programmes. Apache Hadoop and MongoDB are two popular tools.

 

• Apache Hadoop: Apache Hadoop is the most popular big data technology. It is an open-source software platform for storing and processing large amounts of data in a distributed computing environment using hardware clusters. This distribution speeds up data processing. The framework is intended to be bug-free, scalable, and capable of processing any data formats.

 

• MongoDB: MongoDB is a NoSQL database that can hold massive amounts of data. MongoDB organizes documents into collections by using key-value pairs (a basic unit of data). It is developed in C, C++, and JavaScript and is one of the most popular big data databases due to its simplicity of managing and storing unstructured data.

 

2. Operational:

 

This is the side of Big Data Technologies that brings together a huge number of Big Data Engineers, IT specialists, and Networking experts who work on big data processing and storage.

 

These are the technologies that are in charge of generating big data and storing it in an effective manner but are not in charge of offering answers. This data can be generated by social media platforms, ecommerce websites, hospitality-based enterprises such as ticketing data, or any other data-generating internal organization

 

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3. Exploration of data:

 

Data mining is the process of extracting meaningful patterns and trends from raw data. Unstructured and structured data can be converted into meaningful information using big data tools such as Rapidminer and Presto.

 

• Rapidminer: It is a data mining programme that may be used to create prediction models. It capitalizes on the strengths of these two roles, processing and preparing data and constructing machine and deep learning models. The end-to-end paradigm enables both functions to have an impact throughout the organization. 

 

• Presto: Presto is an open-source query engine created by Facebook to conduct analytic queries over huge datasets. It is now readily available. In a couple of minutes, a Presto query can aggregate data from different sources within an organization and conduct analytics on it.

 

4. Visualization of data:

 

Finally, big data technology can be utilized to generate visually beautiful data visualizations. Data visualization is a skill that is useful in data-oriented professions for delivering recommendations to stakeholders for business profitability and operations—to tell a compelling story with a simple graph.

 

The graphical depiction of information and data is known as data visualization. Data visualization tools, which include visual components like as charts, graphs, and maps, make it easy to view and comprehend trends, outliers, and patterns in data. Furthermore, it allows employees or business owners to deliver facts to non-technical audiences without causing confusion.

 

5. Data Analytics: 

 

Data analytics is the study of analyzing raw data in order to draw conclusions about it. Many data analytics approaches and processes have been turned into mechanical processes and algorithms that operate on raw data for human consumption.

 

Data analytics is a broad phrase that incorporates a wide range of data analysis techniques. Data analytics techniques can be applied to any type of information to gain insight that can be utilized to improve things. Data analytics approaches can uncover trends and indicators that would otherwise be lost in a sea of data. This data can then be utilized to optimize procedures and boost a company's or system's overall efficiency.

 

Also Read | Top 10 Big Data Technologies

 

Top Big Data Technologies:

 

Many open source big data tools are available, some of which are also available in commercial versions or as part of big data platforms and managed services. Here are some popular open source tools and technologies for big data management and analysis, listed in alphabetical order and with a synopsis of their essential features and capabilities.

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Top Big Data Technologies

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1. Airflow:

 

Airflow is a workflow management application for scheduling and running large data pipelines in big data systems. It enables data engineers and other users to guarantee that each activity in a workflow is completed in the correct order and has access to the necessary system resources. Airflow is also marketed as being simple to use: 

 

Workflows are written in Python and can be used to build machine learning models, move data, and perform other tasks.

 

The platform was developed at Airbnb in late 2014 and was officially launched as an open source technology in mid-2015; the following year, it entered the Apache Software Foundation's incubation programme and became an Apache top-level project in 2019.

 

2. Hadoop:

 

Hadoop, as one of the most popular big data solutions, is used to deal with data in clump mode, which means it breaks down the data into smaller parts that the user can subsequently consume. Hadoop is essentially a system that uses an unusual distribution strategy to deal with Big Data. HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), MapReduce, and HadoopCommon are all you need. 

 

3. Drill:

 

According to the Apache Drill website, it is "a low-latency distributed query engine for large-scale datasets, including structured and semi-structured/nested data." Drill can query petabytes of data using SQL and common connection APIs and can scale across thousands of cluster nodes.

 

Drill is a big data exploration tool that layers on top of several data sources, allowing users to query a wide range of data in various forms, from Hadoop sequence files and server logs to NoSQL databases and cloud object storage.

 

4. Druid:

 

Druid is a real-time analytics database with low query latency, high concurrency, multi-tenancy, and rapid visibility into streaming data. According to its supporters, multiple end users can query the data stored in Druid at the same time with no influence on performance.

 

Druid, which was established in Java in 2011, became an Apache technology in 2018. It's often regarded as a high-performance alternative to standard data warehouses that works well with event-driven data. It uses column-oriented storage and can load files in batch mode, just like a data warehouse.

 

5. Data Lakes:

 

Data Lakes are utilized as a centralized repository for holding various types of data, including untransformed data. Because there is no requirement to convert data into a structured format before storing it, the process of data accumulation is simplified, and many big data-based technologies can be used to do analytics.

 

6. Plunk: 

 

This big data tool is intended to deliver quick data insights. The user can access Hadoop clusters via virtual indexes and link to tools such as Tableau and various databases like Oracle, MYSQL, and others. It assists the user in dealing with real-time streaming data by indexing, collecting, and correlating such data. Furthermore, users may simply acquire insights by generating reports, graphs, dashboards, and so on.

 

7. Docker:

 

Docker enables users to create, test, deploy, and operate programmes. It makes use of the container concept, which is a technique for splitting software into standardized components. It enables the user to package their application such that all of its components, such as dependent libraries, system utilities, and so on, are all in one place. Docker helps to standardize application processes, save expenses, and improve the overall stability of the deployment process.

 

8. Kafka:

 

According to Apache, more than 80% of Fortune 100 firms and thousands of other organizations utilize Kafka for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Kafka is a framework for storing, reading, and analyzing streaming data.

 

The technique separates data streams from systems, allowing the data streams to be utilized elsewhere. It operates in a distributed environment and communicates with systems and applications via a high-performance TCP network protocol. 

 

9. Kubernetes:

 

This big data solution assists users in automating, deploying, and scaling containerized applications across host clusters. Kubernetes can simply track container applications deployed in the cloud.

 

Also Read | Data Lakes: Definition, Benefits, and Challenges

 

Conclusion

Businesses are increasingly relying on big data technologies to solve everyday problems and make critical business choices. To grasp big data technologies, you must first understand the fundamentals. Understanding how these technologies function and where they fit into the big data ecosystem is part of this.