Top 11 Best Big Data Tools And Software That You Can Use In 2022
Today, data has become one of the most vital things in the world of information technology. Currently, technology is developing rapidly; affecting the growth of websites, social media, online portals, blogs, and so on. There were times when people used to talk about data in terms of kilobytes and megabytes. But, it keeps multiplying every day by manifolds and as a result, now it is talked about in terabytes. Also, its exponential growth through the years generated several types of data including structured, semi-structured, and unstructured.
Another crucial aspect of data worth remembering is its importance. The data becomes useless unless it is turned into useful and knowledgeable information to help management in decision making. As a result of this unprecedented growth, numerous Big Data tools and software has gradually proliferated. While these tools can perform several data analysis tasks they also provide efficiency in time and cost. Additionally, these tools can also help enhance the effectiveness of business by exploring business insights.
Hence, we have listed out the top big data tools and software available in the market. Businesses can use these tools and software to help store, analyze, report, and do much more with this data. Here is the best Big Data software which can help you boost interest in big data and develop Big Data project effortlessly.
Apache Hive is a java based open source ETL (extraction, transformation, and load) and data warehouse tool. It is developed over the HDFS and can perform several operations such as ad-hoc queries, data encapsulation, and analysis of massive datasets effortlessly. As a data warehouse, it can only handle and query structured data. It has a directory structure that is used to partition data to enhance the performance of specific queries.
The tool supports four different types of file formats including sequence file, textfile, ORC, and Record Columnar File (RCFILE). It also supports SQL for data interaction and modeling to allows custom User Defined Functions(UDF) for data filtering, data cleansing, and others.
Quoble is a cloud-based data platform that develops a machine learning model. The vision of this tool is to focus on data activation by allowing easy to use end-user tools including SQL query tools, notebooks, and dashboards. To provide a single shared platform too processing different types of datasets it enables users to drive ETL, AI, and ML-based applications, and analytics more efficiently.
It is an open-source framework which allows reliable distributed processing of large volume of data across computers. It consists of several modules namely Hadoop Common, Hadoop Distributed File System, Hadoop YARN, Hadoop MapReduce. This tool not just makes data processing flexible but also provides a framework for efficient data processing. The motivation behind its design is to scale up single servers to multiple servers to identify and handle the failures at the application layer.
It is an end-to-end, open-source, and fully transparent platform that is used for machine learning and model development. It can store streaming data to various databases and supports machine learning steps such as data visualization, data preparation, predictive analysis, deployment, and others. It permits many products to develop new data mining processes and build predictive analysis through multiple data management techniques.
It is an open-source tool that provides a single architecture and single platform for data processing. It enhances scalability and performance and is very simple to learn and program. Moreover, the ETL engine can be used to extract, transform, and load data by scripting language named ECL. Other features include in data management tools are data cleansing, job scheduling, data profiling, and others.
Apache Cassandra is a free, open-source tool that provides users with scalability, high availability, and excellent performance. Cassandra can handle a high volume of unstructured data. It has no single point of failure (SPOF) meaning if the system fails then the entire system will stop. It replicates data automatically and applies to such applications that are not able to lose data.
It is one of the best tools for developing large scale data as it offers a database management system for warehousing applications. It also offers end-to-end solutions as it developed based on the MPP (Massively Parallel Processing) Architecture. Additionally, it is highly scalable and can connect network-attached systems or mainframe.
It is an efficient data visualization tool as its primary objective is to focus on business intelligence. This tool does not require complicated software setup and can be used to collaborate in real-time. Hence, it also provides several central locations to manage schedules, tags, and delete and change permissions. It offers all this without any integration cost.
It is a database management tool with a cross-platform document database. This tool provides high performance, high availability, and scalability for indexing and querying. The tool works on the idea of documentation and collection of data. It stores data using JSON- like documents while its distributed database provides availability, distribute geographically, and horizontally scaling. The tools are free to use and offer features such as indexing, ad hoc querying, and aggregation in real-time.
The tool has an open-source framework and distributed engine of the stream for processing. It can run in all known cluster environments such as Apache Mesos, Hadoop YARN, and Kubernetes. It supports a variety of third-party systems connectors and can recover its failure. Lastly, It provides several APIs at different levels of abstraction, and also it has libraries for common use cases.
It is an amazing software that can access, analyze, and prepare any data from any source. The motto of this tool is to turn big data into big insights. It is one of the most useful orchestrations, data integration, and business analytics platform. It permits users to check data with analytics and simultaneously supports a wide range of big data sources. It doesn’t require coding and can deliver the data effortlessly.
Today, Big Data has become a competitive edge for businesses. The field of Big data is booming with opportunities as organizations depend on the information extracted for their further decision making. It has become a very robust and cost-effective process to manage data. Most of the Big Data tools provide a particular purpose. In this blog, we have provided you with the 11 best tools in our opinion that can help you achieve your goal.