During the last couple of decades, big data has become a rather popular concept among the majority of technical terms. Besides, evolving technological advances have allowed companies to analyze a huge amount of information to get valuable insights and enhance their data platforms. Companies are now able to monitor, analyze, and utilize all the received metrics that have been exerted from the processing of big data.
As the industry of data processing keeps covering most of the domains around the world, there are more and more big data tools entering the market to rule in the upcoming years.
What Stands Beyond Big Data?
While data includes numerous well-arranged and unstructured information, big data comprises an abundance of it. Enterprises use it to conduct better work decisions, more precise analytics, and effective strategies. They leverage big data to make accurate decisions when it comes to increased productivity and client satisfaction.
Big data comprises 4 V’s: volume, variety, velocity, and veracity where each component refers to different things:
- Volume: the data scale
- Velocity: streaming data analysis
- Variety: different data forms
- Veracity: data reliability
As the data grows, applications produce even more information that needs to be processed, companies will utilize cloud systems to store data and manage the vast amount of datasets.
How Big Data Works?
The main thought behind the big data phenomenon is that the more people are interested in something, the more they can discover and find an answer to their queries. However, the growing need to process large quantities of data calls for a well-organized and steady foundation.
To process different kinds of datasets, the company needs a solid technology framework to start with.
As big data is constantly collected from various sources, there is a need for new advances to process it. The framework should be capable of receiving such volumes of data and integrate it into the company’s infrastructure.
When the company receives huge amounts of information, they need a space to store it that can be done on-premises, in the cloud, or both of them. Besides, they can choose the structure for putting the data away in order to access it whenever it is needed.
Once the data is received and stored, it should be broken down to give away all the required metrics. Сompanies can investigate data and settle on their preferred choices to comprehend the highlights that clients look into the most.
Many organizations are closely observing new technological innovations to be aware of what is already on the market and what’s coming next. Hence, the below list of big data tools is something to look for in 2021 and beyond.
Best Big Data Tools in 2020
Although there are dozens of big data tools available on the market, they all offer different functions and open up various opportunities.
This is by far the most well-known tool when it comes to big data and dealing with it. The tool is open-source and allows companies to process vast loads of data, and is built on commodity hardware. The framework operates under Apache License and is free to use. The main properties include:
- Mapreduce model of data processing
- Efficiency and flexibility
- HDFS system to hold data
- Support of other system models
- Cloud infrastructure
This is a distributed software that is free, real-time, and supportive of various programming languages. The framework is able to receive and deal with data streams coming from a large number of sources. The most significant features include:
- Speed and scalability
- Integration with various programming languages
- Fault-tolerance control
- Processing of every data unit
It is a cross-platform software that is open-source and capable of integrating data analytics, and science, along with ML technology. The offered product variety enables companies to build comprehensive processes for data mining. RapidMiner offers different licenses to choose from. The key characteristics feature:
- Customer-server layout support
- Cloud-integration services
- Predictive analytics built on big data
- Databases integration
- Tools for managing data
- Predictive model validation
It is an autonomous framework that monitors, optimizes, and processes information based on the company’s activity. The platform allows users to choose among several subscription plans and is designed for big organizations. The key features include:
- Procedures that eliminate manual actions recurrence
- Actionable recommendations and insights
- Cloud optimization
- Easy usage
- Availability of engines that are open-source
It is software for visualizing and analyzing data that comprises three models. Tableau Desktop is designed for those working with analytics, Tableau Server — for people working in enterprises, while Tableau Online — for a cloud.
Tableau is capable of processing different sizes of data and allows users to visualize it with the help of a web connector. There is a free testing mode and various subscription options. Main features include:
- Quick and simple setup
- Blending different datasets
- The ability to create different visualization types
- Collaboration in real-time
- Mobile-friendly dashboards
It is a distributed-type framework that is open-source and designed to process a vast quantity of data with no collapse point. Its primary focus is on structured sets of information. The framework is free to use and possesses the below features:
- Speed and scalability
- Processing huge volumes of big data
- No point of collapse
- Automated replication
- Cloud applicability
- Information distribution in existing data centers
The software is open-source and is capable of handling batch and real-time information. It processes in-memory records that allow users to speed up the outcomes. The tool is able to run on one system, hence can be developed and tested easier. It operates under Apache License and has a free trial. The main characteristics include:
- Dataframe API
- Fast graph processing
- An ability to high-stream operations
- Joint libraries
- Cloud environment deployment
- Autonomous cluster mode
An open-source tool is utilized to stream process information and can be limited and unlimited. The framework can run in different cluster environments and is able to perform different scale tasks. The features include:
- Accuracy in terms of results
- Fault-tolerance control
- Failure recovery
- Versatile windowing
- An ability to run on dozens of nodes
- Support of different third-party connectors
To Sum Up
As there appears more and more information that requires to be processed, technological tools are of great help when it comes to developing an information flow that integrates data from a number of sources into warehouses for further analysis. The tools are capable of covering data management and its storage, data mining, and cleaning, its analysis and visualization, and overlap with existing software.
As the market keeps expanding, offering more big data frameworks, it is important to select the one that defines business needs and offers relevant solutions.