Data Analytics is the process of analyzing data. You can use it to draw results on the basis of information. It is quite popular in scientists, researchers, and commercial industries, to make an informed business decision and to verify theories, models, and hypotheses.
These are Big Data Analytics tools, each having their own uses and limitations, which can help you to analyze the data. We will discuss them in length, below:
Tableau Public
It’s a very simple and intuitive tool for data analysis. It offers intriguing insights via data visualization. Since it’s easy to use, it fares better than the rest of the products in the data analytics market. With its visuals, you can investigate a hypothesis. You can easily explore your data and cross-check the insights.
Some of its advantages are:
- No programming skills are required to use it.
- You can publish the data visualizations for free.
- You can embed it into blogs and web pages, and share it using emails or social media.
- It can be made available for downloads.
However, this data processing tool has some limitations too. For instance, all the data is available to the public and does not have a good scope for restricted access. It has a size limitation, you can only read it via excel or txt, and you cannot connect it to R.
RapidMiner
It provides Machine Learning procedures and has many prospects. For instance, data mining including data processing, visualization, predictive analytics, and statistical modeling. It is rapidly gaining acceptance as a Big Data analytics tool. Some of its advantages are:
- It provides an integrated platform for predictive analysis and business analytics.
- You can also use it in application development, besides commercial and business use.
This data analytics tool also has some limitations. For instance, it has size constraints with respect to the number of rows that can be created, and you need more resources for RapidMiner than for various other tools.
OpenRefine
It was formerly known as GoogleRefine. It helps you to clean up data for analysis. This tool operates on a row of data. It is quite similar to relational database tables. It’s advantages are:
- It cleans up messy data.
- It transforms the data.
- You can parse data from different websites. For instance, you can use this tool for geocoding addresses to geographic coordinates.
Like every other tool, this also has some limitations. For instance, It is not suitable for large datasets, and it does not really work efficiently with Big Data.
KNIME
This data analytics tool helps you to model data, analyze, manipulate through visual programming. KNIME is used to integrate various components of machine learning and data mining. It has some advantages, and they are:
- You don’t have to write blocks of code. Instead, you have to drag and drop the connection points between activities.
- This tool supports programming language. For instance, this tool can be extended to run text mining, chemistry data, python, and R.
Its limitations are poor data visualization.
Google Fusion Tables
When it comes to data tools, we have a larger version of Google Spreadsheets. It is an incredible tool for mapping, data analysis, and large dataset visualization. It can also be added to the list of business analytics tools list. The advantages of these tools are:
- It can help to visualize bigger table data online.
- You can filter and summarize the data across hundreds of thousands of rows
- It can combine tables with other data on the web.
- It can merge two or three tables to generate a single visualization that includes sets of data.
- You can also create a map in minutes.
Some of the limitations of this tool are that the query results can only include 100,000 rows of data; and also the size of data sent in one API call cannot be more than 1 MB.
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