Data science has become one of the most important technical concepts to be implemented in the real world. Compelling data from various sources and later processing them to generate meaningful outputs helps understand a particular entity better. With the help of big data analytics and machine learning, the implementation of artificial intelligence has picked up the pace and now there are smart solutions launched in the app market every day.
Therefore, as big data plays a vital role in leading AI web and mobile app development solutions to the right path, the tools to be used for analysis and processing must be efficient enough to deliver desired results and ensure only the best is provided.
5 Best Big Data Tools to Use for Efficient Outputs
There are many tools available for processing big data due to changing technology needs and updating technology. However, choosing the one that fits your criteria and is trustworthy is required to eliminate the chances of fraud or failure.
Hadoop is by far the most popular framework used for managing big data. Developed by Doug Cutting, a software designer, the primary functions of Apache Hadoop are, Hadoop distributed file system (HDFS), task scheduler for better control (YARN), data searching, analysis, reporting, indexing files on a large-scale and more. Hadoop provides a parallel data processing facility that helps process huge amounts of data easily by dividing it into small chunks and distributing for better performance.
Including the facility of integration with IoT high points and metadata injection, it speeds up the data collection process. As connected with IoT (Internet of Things) directly, it can directly store the sensor data even from mobile devices which may have been collected using an app like youtube or any other app solutions. Pentaho not only includes analyzing ability but it also includes data integration which poses as one of the top advantages it has over other tools available, especially Hadoop.
Elasticsearch is a search engine developed using JSON rest API using Lucene. This is especially used for searching for documents from complex databases. It uses a key-value store for objects and can run queries than other databases easily that too at petabytes scale. Elasticsearch is a No-SQL solution that accommodates excellent facilities even for a small volume of data. It efficiently integrates with Spark Cluster but loses out the fight with Hadoop because of streaming data loss during ingestion that affects the overall performance.
Lumify offers a platform for big data fusion, analysis, and visualization as it provides various analytics tools to establish a relationship among data and objects by processing them. The features offered by Lumify are impressive as it offers 2D and 3D graph plotting of final outcome, specific ingest processing by default and also allows to divide the work up in projects or workspaces to increase the efficiency.
Talend is an open-source platform that helps data management and integration facilities. Talend also checks for data quality and is the next generation tool for big data analytics for sure. The features offered by this excellent tool are simplifying MapReduce and Spark by native code generation, Agile DevOps support, and allows natural language processing and machine learning concepts for higher data quality and more.
Conclusion
These tools are currently being used for simplifying the data operations and making big data analytics an easier task to perform. Moreover, these tools offer excellent support for big data analytics and provide efficient information on the tap of a button. Hence, using these tools, one can easily perform the analytics and gather more information from the collected data.
Guest article written by: Gaurav Kanabar is the Founder and CEO of Alphanso Tech, an India based IT Consulting company that provides youtube clone development service and other app development services to individuals as per their specified demand. Besides this, the founder also loves to deliver excellent niche helping readers to have deep insight into the topic. Twitter || Linkedin