13 Big Data Tools You Can Use for Effective Data Analysis
With the growing popularity of digital media, companies have turned to new ways of measuring analytics, particularly with the use of big data. Big data is an advanced form of data that provides users with massive amounts of information. If you're interested in learning how to process different data, it's helpful to familiarize yourself with the concept of big data and the tools that make its processing possible so you can understand how data processing works.
In this article, we discuss what big data tools are, describe the benefits of using them and list 13 big data tools available for your use.
What is big data?
Big data refers to data that contains complex, extensive data sets within it, typically from newer data sources on the market. It encompasses a variety of data from various sources, including artificial intelligence, social media, the internet, smartphones and apps. The term big data is a reference to the massive amounts of data these streams process, so much so that most processing software programs can't manage all of this information. These are the elements that comprise the foundation of big data:
Velocity: When discussing big data, velocity typically refers to the speed at which programs can process data, with some high-velocity data having the ability to stream into memory as opposed to copying them onto a disk. The rise of smart devices has changed the way professionals consider velocity, as most of these products can process data in real time, requiring immediate attention.
Volume: Big data contains a large volume of data, all of which tools process to provide users with informative analytics regarding that data. When processing big data, you can likely process plenty of unstructured data, often of little to no value.
Variety: There are various types of data from different sources and for different purposes, which is why variety is an important component of big data. The widespread use of big data and its tools has resulted in an increase in unstructured data types that have a lengthier processing speed, including audio, video and text-style data.
13 big data tools
Here are 13 of the most common big data tools and their primary features:
Cassandra is a big data tool developed by Apache, a major software platform that oversees data storage for big data programs. It's an analytic tool that can manage large amounts of data sourced from multiple different servers. Cassandra has the distinction of being able to process data quickly, making it a convenient tool for users interested in running multiple sets of data at a time.
Integrate.io, formerly Xplenty, is a big data tool that integrates and processes data before transferring it to the cloud. The program can combine data from multiple different sources together into a single platform. A benefit of this tool is its user-friendly interface that minimally uses code, making it accessible even for technology-averse individuals. Users have access to support through various forms of communication, including email, phone, video meeting and real-time chat services.
Related: 11 Types of Jobs in Big Data
The High-Performance Computing Cluster, or HPCC, is an open-source big data tool. This system has high scaling abilities, making it adaptable to data from various sources and of different volumes. HPCC can enable parallel data processing, giving users the ability to process information from various sources at once. Another benefit of the HPCC tool is its affordability, as the program is free.
Hadoop is an open-source big data tool that focuses on providing customers with massive storage for their data. In the world of data software, the product has a reputation for its intense processing power, which allows users to analyze large amounts of data at a time within a relatively short timeframe. Hadoop can store all of your data, images, video and text media using its Hadoop Distributed File System (HDFS), which streamlines the data processing and storage processes.
MongoDB is a big data tool that's most compatible for users who work with data that changes often, such as sales or engagement information, and unstructured data. MongoDB can store large amounts of data from various apps, product catalogs and various other data platforms. This tool requires the use of queries, which can have a learning curve, but the program offers customer support. It also follows extensive security standards to help protect your data from unauthorized parties.
R is a big data analytic tool that is most common in the fields of statistics and data mining but can be helpful for those running large amounts of data from different sources. The R software is an open-source program that's free for public use, making it popular for those searching for affordable big data tools. A key distinction of the R system is its advanced interface and graphics and its charting features that allow users to visualize their data.
The Tableau program is a big data analytics tool that offers customers widespread integration, making it desirable for users interested in running multiple data tools at once. This tool offers users the ability to manage data regardless of its size or source and has an industry reputation for being user-friendly. Tableau also offers real-time processing and organizes data in a custom dashboard. You can use a free trial to determine if the program is right for you before committing to a price.
KNIME, or Konstanz Information Miner, is a type of big data tool that gives users the ability to report and integrate data across different sources. Customers can also use KNIME for other activities, such as data mining and running analytics. It's known for its extensive algorithms and easy-to-use interface. A major benefit of KNIME for individuals searching for big data tools is that the program is available for free.
Related: 9 Ways Industries Use Big Data
Adverity is a program that allows users to track and manage end-to-end marketing data. This is helpful for generating reports based on market trends and customer behaviors. Some of the principal features of Adverity include the ability to automate data integration by using artificial intelligence (AI) and having a high-security threshold, which helps users keep data safe. Finally, the tool includes powerful predictive analytics to help users prepare for the future.
Dataddo is a tool that makes collecting data more effective. With this cloud-based program, you can find the best sources for the data you want to collect and allow the program to perform its collection processes automatically. Beyond its ability to find and collect data, the program also features adaptable support for other programs, allowing you to integrate Dataddo more effectively into your existing data collection and analysis structures. The program also is a "no-code" program so it's easy for beginners to use.
Datawrapper is a tool you can use to create charts and graphs quickly and effectively. One of the most notable features of the tool is the ability to make graphs easy to understand, which can help data analysts and stakeholders understand the information the data conveys. This program is an open-source tool, so there are many additions you can find to help you customize the tool for your specific needs.
12. Apache Storm
Apache Storm is an open-source tool you can use to find the reasons behind the behavior of data. One feature of the program that may be useful to companies is the fact that it analyzes data in real-time, meaning that organizations can understand the data they use quickly, possibly giving them an advantage over other organizations that don't use Storm. The program also supports multiple types of data processing, making it a flexible program that can adapt to the changing needs of organizations in many industries.
Talend is a tool that minimizes the number of tasks between collecting big data and using it. This is useful for organizations that want to process data quickly and don't need highly accurate data for analysis. This tool is flexible, allowing you to use it for a variety of data collection, storage and analysis tasks.
What are the benefits of big data tools?
There are several benefits to using big data tools when processing large amounts of data, including:
Faster processing speed: Using big data tools helps increase the speed at which users can process data, as these systems often can review massive amounts of information in little to no time. The increase in speed can directly correlate with an increase in productivity for companies using big data technology, as fast processing allows them to focus their time and efforts on other tasks and ventures.
Forecasting abilities: Many businesses use big data tools to help identify future trends in their markets, products, consumer behaviors and sales. They can use these tools to gather large amounts of analytics that they can evaluate and use to make conclusions regarding the future of their business and theorize ways to handle potential setbacks.
Lower costs: Another advantage of using big data tools is that it allows companies to save money because of its ability to process and store large amounts of data in bundles, either using data storage software or cloud services. Rather than having to pay for different storage methods, companies can use big data tools to keep all of their information in a single, affordable place.
Product innovation: Big data tools are also beneficial for their ability to encourage product innovation, as companies can analyze their data more efficiently to make conclusions about consumer and market trends in relation to their products. For example, a business may run large amounts of analytics to organize and evaluate how customers felt about their previous product launch and make future decisions based on those findings.
Please note that none of the companies mentioned in this article are affiliated with Indeed.
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