10 Best Big Data Analytics Tools for 2019 – With Uses & Limitations
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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.
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
- Google Fusion Tables
- Wolfram Alpha
- Google Search Operators
- Dataiku DSS
a. Tableau Public
- You can publish interactive data visualizations to the web for free.
- No programming skills required.
- 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.
- Cleaning messy data
- Transformation of data
- Parsing data from websites
- Open Refine is unsuitable for large datasets.
- Refine does not work very well with big data
- Don’t write blocks of code. Rather, you have to drop and drag connection points between activities.
- This data analysis tool supports programming languages.
- Poor data visualization
i. What is RapidMiner – Data Analytic Tools
- It provides an integrated environment for business analytics, predictive analysis.
- Along with commercial and business applications, it is also used for application development.
- 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
- 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.
- 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.
- Data Import
- Graph Visualization
- Graph Analysis
- Data Representation
- You need to use multiple seeding terms for a particular problem.
- Running the data extractions at slightly different times.
g. 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.
- 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
- Faster filtering of Google search results
- Google’s powerful data analysis tool can help discover new information.
- 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.
- 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
- 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.