The tools of the trade always keep changing as the game too changes with every passing moment. The big tools of big data are some of the finest cutting-edge technologies created with the purpose of delivering quick and effective solutions in preparing, managing, storing and processing data without accounting much for computing speeds or software storage. Big data tools could include paid software’s with user friendly UIs or free software’s with steep learning curves. Nevertheless, the big data tools in this list must be definitely considered by novices and experts alike: –
Known amongst users by its iconic yellow elephant logo, Hadoop, true to its animal motif, can process large sets of databases. What’s even better is its 100% open source framework which requires a commodity hardware in an existing data consortium. Apache Hadoop can run on a cloud infrastructure as well as is split into four major components. The first is the Hadoop Distributed File System which has an extremely high scale bandwidth. Then comes MapReduce which has programming models for processing big data. Next is YARN which acts as a platform for running and scheduling Hadoop’s processes in its infrastructure and finally the vast libraries to support the execution and supplementation of Hadoop modules.
MongoDB is an open source big data software that runs on a NoSQL framework which is cross-platform compatible along with tons of add-ons and features. MongoDB is compatible for fast edge businesses that run on quick and real-time data to allow for fastidious and continuous decision making. Built and run on MEAN software stack it also has NET applications and can run on Java platforms as well.
It can store all sorts of data types and has flexible in cloud-based platform support allowing for seamless transitions for data between servers in a cloud framework. MongoDB utilizes dynamic schemas allowing users to create data on the go and with ease and no large sunk costs.
Named after a bird in New Zealand, this open source may lack in an attractive user interface but packs a powerful punch in terms of tools and methods all stacked under one platform. Developed by the University of Waikato, WEKA hosts tool that can be used for data analytics, machine learning algorithms, data mining, classification, regression and so on. Algorithms can be applied directly to data sets or can be called through JAVA code making it user friendly for those interested in simpler drag and drop models of processing.
WEKA is also popular for its light weight and multipurpose functions which should make it a sure addition to the big data tools list for any data engineer.
An excellent data visualization tool available both in open source(public) and paid versions. Tableau cuts down on the time taken to interpret tabular data and presents values in a visually appealing format using colors, texts, graph sizes and complex geometries.
Commonly used by businesses, it allows users to create dashboards for various kinds of data frames and can work on a multitude of data types hosting an innumerable number of charts, graphs, trend lines, geographic plots and other types of mensuration tools. Perhaps its most striking feature is to condense mammoth datasets into simplistic interactive images that add a touch of color and style rarely offered by most data visualization tools with additional processing filtering and processing options as well.
Users would have to brush up their knowledge of the R language to use the software but will be mesmerized at its ease of function and infinite packages. R is compatible for various platforms such as MacOS, Windows and UNIX, comprised of a whopping 11556 packages all suitable for a number of data sectors. The software also allows you to create and implement packages automatically based on the user’s requirements. It’s most amazing feature is the knitr package where chunks of code can be written separately and executed as per the user’s needs. It is written in three different programming languages including C, Fortran and R. RStudio also provides extensive graphical and statistical packages packed with useful models for regression, clustering, statistical tests, time-series assessments, ANOVA and much more.