10 Best Big Data Analytics Tools for 2019 – With Uses & Limitations
1. Best Big Data Analytics Tools
In this blog on Best Big Data Analytics tools, we will learn about Best Data Analytic Tools. Also, will study these Data Analysis Tools: Tableau Public, OpenRefine, KNIME, RapidMiner, Google Fusion Tables, NodeXL, Wolfram Alpha, Google Search Operators, Solver, Dataiku DSS with their uses, limitations, and description.
Best Big Data Analytics Tools
2. List of Big Data Analytics Tools
Data Analytics is the process of analysing datasets to draw results, on the basis of information they get. It is popular in commercial industries, scientists and researchers to make a more informed business decision and to verify theories, models and hypothesis.
These are the 10 Best Big Data Analytic Tools with their uses and limitations, which can help you to analyse the data. Let’s discuss them one by one:
- Tableau Public
- OpenRefine
- KNIME
- RapidMiner
- Google Fusion Tables
- NodeXL
- Wolfram Alpha
- Google Search Operators
- Solver
- Dataiku DSS
a. Tableau Public
i. What is Tableau Public – Big Data Analytics Tools
It is a simple and intuitive tool. As it offers intriguing insights through data visualization. Tableau Public’s million-row limit. As it’s easy to use fares better than most of the other players in the data analytics market.
With Tableau’s visuals, you can investigate a hypothesis. Also, explore the data, and cross-check your insights.
ii. Uses of Tableau Public
- You can publish interactive data visualizations to the web for free.
- No programming skills required.
Visualizations published to Tableau Public can be embedded into blogs. Also, web pages and be shared through email or social media. The shared content can be made available s for downloads. This makes it the best Big Data Analytics tools.
iii. Limitations of Tableau Public
- All data is public and offers very little scope for restricted access
- Data size limitation
- Cannot be connected to R.
- The only way to read is via OData sources, is Excel or txt.
b. OpenRefine
i. What is OpenRefine – Data Analytic Tools
Formerly known as GoogleRefine, the data cleaning software. As it helps you clean up data for analysis. It operates on a row of data. Also, have cells under columns, quite similar to relational database tables.
ii. Uses of OpenRefine
- Cleaning messy data
- Transformation of data
- Parsing data from websites
Adding data to the dataset by fetching it from web services. For instance, OpenRefine could be used for geocoding addresses to geographic coordinates.
iii. Limitations of OpenRefine
- Open Refine is unsuitable for large datasets.
- Refine does not work very well with big data
c. KNIME
i. What is KNIME – Data Analysis Tools
KNIME helps you to manipulate, analyze, and model data through visual programming. It is used to integrate various components for data mining and machine learning.
ii. Uses of KNIME
- Don’t write blocks of code. Rather, you have to drop and drag connection points between activities.
- This data analysis tool supports programming languages.
iii. Limitation of KNIME
- Poor data visualization
d. RapidMiner
i. What is RapidMiner – Data Analytic Tools
RapidMiner provides machine learning procedures. And data mining including data visualization, processing, statistical modeling and predictive analytics.
RapidMiner written in Java is fast gaining acceptance as a Big data analytics tool.
ii. Uses of RapidMiner
- It provides an integrated environment for business analytics, predictive analysis.
- Along with commercial and business applications, it is also used for application development.
iii. Limitations of RapidMiner
- RapidMiner has size constraints with respect to the number of rows.
- For RapidMiner, you need more hardware resources than ODM and SAS.
e. Google Fusion Tables
i. What is Google Fusion Tables
When comes to data tools, we have a cooler, larger version of Google Spreadsheets. An incredible tool for data analysis, mapping, and large dataset visualization. Also, Google Fusion Tables can be added to business analytics tools list. This is also one of the best Big Data Analytics tools.
ii. Uses of Google Fusion Tables
- Visualize bigger table data online.
- Filter and summarize across hundreds of thousands of rows.
- Combine tables with other data on the web
- You can merge two or three tables to generate a single visualization that includes sets of data.
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- You can create a map in minutes!
iii. Limitations of Google Fusion Tables
- Only the first 100,000 rows of data in a table are included in query results or mapped.
- The total size of the data sent in one API call cannot be more than 1MB.
f. NodeXL
i. What is NodeXL
It is a visualization and analysis software of relationships and networks. NodeXL provides exact calculations. It is a free (not the pro one) and open-source network analysis and visualization software. NodeXL is one of the best statistical tools for data analysis. In which includes advanced network metrics. Also, access to social media network data importers, and automation.
ii. Uses of NodeXL
This is one of the data analysis tools in Excel that helps in the following areas:
- Data Import
- Graph Visualization
- Graph Analysis
- Data Representation
This software integrates into Microsoft Excel 2007, 2010, 2013, and 2016. It opens as a workbook with a variety of worksheets containing the elements of a graph structure. That is like nodes and edges.
This software can import various graph formats. Such adjacency matrices, Pajek .net, UCINet .dl, GraphML, and edge lists.
iii. Limitations of NodeXL
- You need to use multiple seeding terms for a particular problem.
- Running the data extractions at slightly different times.
g. Wolfram Alpha
i. What is Wolfram Alpha
It is a computational knowledge engine or answering engine founded by Stephen Wolfram.
ii. Uses of Wolfram Alpha
- Is an add-on for Apple’s Siri
- Provides detailed responses to technical searches and solves calculus problems.
- Helps business users with information charts and graphs. And helps in creating topic overviews, commodity information, and high-level pricing history.
iii. Limitations of Wolfram Alpha
- Wolfram Alpha can only deal with a publicly known number and facts, not with viewpoints.
- It limits the computation time for each query.
Any doubt in these Statistical tools for Data Analysis? Please Comment.
h. Google Search Operators
i. What is Google Search Operators
It is a powerful resource which helps you filter Google results. That instantly to get most relevant and useful information.
ii. Uses of Google Search Operators
- Faster filtering of Google search results
- Google’s powerful data analysis tool can help discover new information.
i. Solver
i. What is Excel Solver
The Solver Add-in is a Microsoft Office Excel add-in program. Also, it is available when you install Microsoft Excel or Office. It is a linear programming and optimization tool in excel.
This allows you to set constraints. It is an advanced optimization tool that helps in quick problem-solving.
ii. Uses of Solver
- the final values found by Solver are a solution to interrelation and decision.
- It uses a variety of methods, from nonlinear optimization. And also linear programming to evolutionary and genetic algorithms, to find solutions.
iii. Limitations of Solver
- Poor scaling is one of the areas where Excel Solver lacks.
- It can affect solution time and quality.
- Solver affects the intrinsic solvability of your model.
j. Dataiku DSS
i. What is Dataiku DSS
This is a collaborative data science software platform. Also, it helps a team build, prototype, explore. Although, it deliver their own data products more efficiently.
ii. Uses of Dataiku DSS
Dataiku DSS– Data analytic tools provide an interactive visual interface. As in this they can build, click, and point or use languages like SQL.
iii. Limitation of Dataiku DSS
- Limited visualization capabilities
- UI hurdles: Reloading of code/datasets
- Inability to easily compile entire code into a single document/notebook
- Still, need to integrate with SPARK
These were the top data analytics tools and this is all on Best Big Data Analytics tools.
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3. Conclusion: Big Data Analytics tools
As a result, we have studied Big Data Analytic Tools. Also, we learned these Data Analysis Tools: Tableau Public, OpenRefine, KNIME, RapidMiner, Google Fusion Tables, NodeXL, Wolfram Alpha, Google Search Operators, Solver, Dataiku DSS uses, limitations along with a description.
I hope this blog on analytics tools will help you to understand Data Analytic Tools. Data Analytic Tools is a booming topic nowadays. Furthermore, if you have any query regarding Big data analytics tools, feel free to ask in a comment section.
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This article is so well written it just made me understand the difference between all the analytics tools which made it so much easier for me to make the choice.
Hii Iyer,
Thank you very much for sharing your positive experience of Big Data Analytics Tools with us. You can check out more articles on Big Data, which will make Big Data learning easy for you. Sharing one with you, have a look.
https://data-flair.training/blogs/careers-job-roles-big-data-comprehensive-guide/
Hope it helps.
Very well written! Would love to see OpenText-Magellan on this list. It’s a great data visualisation and analytics platform for customised dashboards and interactive visual reports.
Sir, I m fresher for big data from where we should start bug data along with Hadoop with video lectures and concepts.