One of the obvious things about big data and its future is that the amount of data produced every day will only remain to grow. So far, we are generating around 2.3 trillion gigabytes of data every day, and this will only rise in the future. Big data is everywhere. Other than phones and computers there are smartwatches, smart televisions, smart wearable techs, and many more in the market that further gather data from consumers, giving the scope for the huge production of data.
A Big data analytics tool grants insights into data collections. The data is collected from various big data clusters. The tool help business to know data trends, build patterns and its complexities, and convert data into understandable data visualization.
Because of the cluttered nature of big data, analytical tools are very relevant when it comes to understanding the achievement of your business and to gain customer insights. As there are a lot of data analytics tools that are ready online, this blog will help you get some knowledge and pick the best big data analytics tool. So, let’s see the 10 best and powerful big data analytics tools for any size of business.
15 Most Important Big Data Analytics Tools in 2020
1) Apache Hadoop
Apache Hadoop is a software framework used for clustered file systems and the handling of big data. It prepares datasets of big data using the MapReduce programming model. Hadoop is an open-source framework that is written in Java and it gives cross-platform support.
Without any scope for doubt, this is the topmost big data tool. More than half of the big companies around the world use Hadoop. For instance, Amazon Web services, Hortonworks, IBM, Intel, Microsoft, Facebook, etc.
The backbone of Hadoop is its Hadoop Distributed File System (HDFS) which can hold all types of data like video, images, JSON, XML, and plain text over the same file system. It is highly useful for R&D purposes. Also, it provides quick access to data and is highly scalable. The problem is sometimes disk space issues can be faced due to its 3x data redundancy. I/O processes could have been optimized for better appearance.
2) Cloudera Distribution for Hadoop (CDH)
Cloudera Distribution for Hadoop intends for enterprise-class deployments of that technology. It uses open source technology and has a free platform distribution that includes Apache Hadoop, Apache Spark, Apache Impala, and many more. It enables you to gather, process, administer, manage, discover, model, and assign unlimited data.
Some advantages of CDH include comprehensive distribution, easy implementation, less complex administration, high security, and governance, and it administers the Hadoop cluster very well. Just like anything it has some disadvantages also.
Cons of CDH its User Interface features like charts on the CM service, which is really complicated. Multiple advised approaches for installation seem confusing. And the Licensing price on a per-node basis is pretty pricey.
Apache Cassandra is free of cost and open-source distributed NoSQL DBMS built to handle huge volumes of data scattered across many commodity servers, delivering high availability. It engages CQL (Cassandra Structure Language) to interact with the database.
Advantages of Cassandra includes, it has No single point of failure that is one advantage of Cassandra and more are there like, it’s handling of massive data as fast as possible, log-structured storage, automated replication, linear scalability, and the Simple Ring architecture
The problem of Cassandra is it requires some extra efforts in troubleshooting and maintenance. Its Clustering could have been improved a little more and the row-level locking feature is not available.
Datawrapper is an open-source platform for data visualization that supports its users to create easy, accurate and embeddable charts very fast. Its major customers are newsrooms that are spread all over the world.
The main advantages of Datawrapper are that it is device friendly. Behaves responsive on all types of devices like mobile, tablet or desktop. It can Bring all the charts in one place. And also has large customization and export options. Ultimately, it requires no coding. The disadvantage of Datawrapper is that it has only limited color palettes
MongoDB is a cross-platform document-oriented database program. Classified as a NoSQL database program. It is free and is an open-source tool that supports multiple operating systems including Windows Vista and other new versions, Linux, Solaris, FreeBSD, etc.
Konstanz Information Miner (KNIME) was created in January 2004. The tool was made by a few software engineers at the University of Konstanz. It is an open-source Big data analytics tool that allows you to investigate and create data via visual programming. With the aid of the modular data-pipelining concept, KNIME can integrate several components for machine learning and data mining.
One of the important reasons why KNIME entered in the list is due to its drag and drop option. With KNIME, you do not require to write blocks of codes. You can easily drag and drop combined points between actions. This big data analytics tool encourages different programming languages. You can also stretch the functionality of the tool to analyze chemistry data, Python, R, and text mining. But, while visualizing the data, the tool has its restrictions.
KNIME Analytics is one of the best solutions that can assist you to improve the maximum out for data. You can find over 1000 modules and ready-to-execute models in KNIME. Again, it carries a stockpile of integrated tools and advanced algorithms that can be beneficial for a data scientist.
7) Tableau Public
Tableau Public software is an open-source big data analytics tool that allows users to connect any data source, for instance, web-based, Microsoft Excel or corporate warehouse data. The tool creates data visualizations, dashboards, maps, etc. and supports them with real-time updates via the Web. Users can share analysis reports on social media or directly with the client via various means. It is possible to download the final result in different formats. In order to take advantage out of Tableau Public, the users are advised to have an organized data source.
Tableau Public is very effective with Big data, which makes it a favorite for many users. Furthermore, one can examine and visualize data in a better way with Tableau Public.
Tableau regulates visualization in an uncomplicated tool. The software is particularly effective in business as it can interact insights via data visualization. The visuals in Tableau support you to test a hypothesis, shortly check your intuition and browse data before accessing into a risky statistical journey.
R-programming is one of the best big data analytics tools that is broadly adopted for data modeling and statistics. R can easily manage your data and present it in different forms. It has grown better to SAS in several ways such as results, performance, and capacity of data. R collects and helps various platforms such as macOS, Windows, and UNIX. It carries 11,556 packages that are classified suitably. R also extends the software to automatically set up packages according to user demand. And, it can be arranged with Big data.
R is written in three distinct programming languages – C, Fortran and R. As R, the programming language backs open-source software environment, it is favored by many data miners who develop statistical software for data analysis. Extensibility and efficiency of use have improved R’s reputation more rapidly in recent times.
R-programming also provides graphical and statistical techniques that incorporate non-linear and linear modeling, clustering, classification, time-series analysis, and conventional statistical tests.
Talend is one of the most advanced open source big data analytics tools that is created for data-driven activities. The users of Talend can connect at any place at any presented speed. One of the greatest advantages of Talend is that it has the ability to connect at a large data scale. It is 5 times faster and works the task at 1/5th the price. It simplifies ELT & ETL for Big data and also supports Agile DevOps to accelerate big data projects.
The purpose of the tool is to interpret and automate big data integration. Talend’s graphical wizard produces native code. The software also supports master data management, big data integration and supports data quality.
Apache Spark is the next big data analysis tool in the list that contributes more than 80 high-end operators to support in order to create parallel apps. Spark is utilized at companies to examine large datasets.
The robust processing engine provides Spark to instantly process data in a huge amount. It has the capability to run apps in Hadoop clusters 100x quicker in memory and 10x faster on disk. This tool is completely based on data science, which gives it the strength to support data science easily. Like KNIME, Spark is also helpful for machine learning and data pipeline model development.
Spark holds a library named MLib that extends a dynamic group of machine algorithms. These algorithms can be applied for data science, for instance, Clustering, Filtering, Collaborative, Regression, Classification, etc. Apache Spark also Provides inbuilt APIs in Python, Scala or Java.
NodeXL is an exceptional analysis software of networks and relationships. It recognized for its accurate calculations. It is a free open source analysis and visualization tool that is regarded as one of the most efficient tools to interpret data. It covers advanced network metrics and automation. You can also run social media network data importers via NodeXL.
The Uses of NodeXL are many. For instance, this tool that is in Excel helps you in various areas like Data Representation, Data Import, Graph Analysis, and Graph Visualization. The tool blends well with Microsoft of versions 2016, 2013, 2010, and 2007. It grants itself as a workbook that involves different worksheets. The worksheets comprise various elements that can be seen in a graph structure like edges and nodes. You can import several graph formats such as edge lists, GraphML, UCINet.dl, Pajek .net and adjacency matrices.
Weka is a marvelous open-source tool that can be applied for big data analytics in your organization. The tool comprises several machine learning algorithms assigned for data mining processes. You can instantly apply algorithms to data sets or command them via your Java code. The tool is ideal for building new machine learning models as it is completely developed in Java. Besides, the tool helps various data mining tasks.
Even if you haven’t done any programming recently, Weka backs you to learn the theories of data science. It actually makes the process easier for users who have limited knowledge of programming.
OpenRefine once called Google Refine and Freebase Gridworks before is a stand-alone open-source desktop application for data clean-up and conversion to other formats. OpenRefine works upon a set of data that has cells under columns like that of database tables.
It is useful to clean up cluttered data. You can retrieve data from a web service and join it into the dataset. Still, it is not recommended to use for larger datasets.
Pentaho is a business intelligence software. It helps you to extract value from your organizational data. This big data analytics tool just prepares and compounds any data. It contains a broad range of tools that can effortlessly determine, visualize, investigate, report and foretell. Pentaho software is open, embeddable and expandable. The tool is meant to make sure that each user can convert data to value.
The open-source data analysis and visualization specialist tool Orange is amazing for both experts and beginners. It is an all-in-one analytics tool that allows an interactive workflow to view and interpret data. The tool incorporates characteristics like a great toolbox that provides a wide range of tools to design an interactive workflow. Furthermore, the package consists of many visualizations, dendrograms, heat maps, networks, trees scatter plots, and bar charts. These are the best big data analytics tools that can be of great use to your organization. Utilizing these tools will make it simpler when translating data into value.
NdimensionZ Solutions assists to query, analyze, and visualize the Big Data at an affordable expense in order to present client the complete satisfaction. Get the benefits of our valuable services from the Big Data team at NDZ.
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