Over the past few decades, corporate firms and others have started using data statistical cum analytical strategies. Some instances of approaches and techniques include classification, clustering, regression, etc. These processes were actively followed to sort out the data problems of the company. In the past, there were limitations on the availability of data analysis tools and scalability. Plus, simpler data and models were the ultimate requirements then.
With the advent of newer technologies, the modern epoch has come up with Big Data. Such data come in huge volumes and hence, it needs new generation data manipulation cum statistical techniques. Moreover, the need to develop scalable models is significant as it can not only manage big data, but can also do the task quite efficiently, without a mistake.
Here I have profiled a list of top 5 such data visualization cum analytical tools. Let’s check how these tools are helpful in Big Data analysis.
Tableau is a communicative data analytics cum visualization software. It is utilized in effective data visualization and analysis in the corporate sectors and large scale industries. The drag and drop option of Tableau interface facilitates fast and easy task management.
With this inexpensive software tool, you won’t require to write codes separately and can support numerous data sources. The software is preferred by some of the globally renowned companies, like Amazon and others. On the other hand, QlikView gives hard competition to tableau and is widely used for its exceptional drag and drop feature.
- Easiest business data tool for efficient analysis and visualization
- Data scientists are free from the hassle of writing codes in Tableau
- It facilitates data mixing followed by real-time collaboration
QlikView, as mentioned above, is yet another business intelligence tool and closely resembles Tableau. However, to use this tool for commercial purposes, you have to pay in advance. This data analysis platform helps in turning data to useful information. Added to that, QlikView software helps in data visualization process improvement. This software is mostly preferred by established data scientists for analyzing large scale data. Currently, Qlikview is used by the industries from around 100 nations or more and hence, includes a strong and vast community.
- It collaborates with a wider range of varied data sources including EC2, HP Vertica, Impala, and more
- Conducts swift data analysis
- Too simple and easily configurable cum deployable data visualization tool
SAS is typically designed and developed for conducting statistical operations. It is a non-free proprietary tool, deployed in large scale firms for critical data analysis. SAS utilizes its base programming language to perform statistical modeling. Most pro-grade officials and companies, dealing with trusted commercial software, use SAS software. It provides a plethora of statistical tools and libraries that you, being a Data Scientist, might find effective in data modeling and organization.
Though it is reliable business software, SAS is quite costly and hence, is only found in large-scale organizations. Plus, SAS is relatively less significant compared to the other open-source modern business tools. Additionally, the software includes several packages and libraries in SAS but is only available after you pay for it in advance. Its base pack does not include such expensive upgrades.
- Closed source business software
- Includes numerous libraries and packages but don’t come in the base pack
- Offers a number of statistical data modeling tools and libraries
4. Apache Spark
Apache Spark or simply Spark is another strong business analytics engine and is an extensively used tool in Data Science. This software is particularly developed for managing Stream processing as well as Batch processing. The software includes several APIs which let a Data Scientist repeatedly access data for Storage in SQL, then Machine Learning, and etc. This tool is basically an improved cum advanced version of Hadoop and can do the task 100 times quicker compared to MapReduce. Spark enables a Data Scientist to make intuitive yet feasible business predictions by using the given data through its(Spark’s) Machine Learning APIs.
- Spark is far better than what Big Data Platforms do in terms of its capability to manage streaming business data. That means Spark can handle data in real-time compared to any other business visualization tools which do the batch processing of only historical data
- Spark APIs are programmable in Java, R, and Python. However, its collaboration with Scala programming language (cross-platform Java Virtual Machine based) is comparatively better than the above three languages
- Spark can efficiently conduct cluster management, which turns it into a better choice than Hadoop because Hadoop is only applicable for storage. This cluster management feature of Spark facilitates it to process applications much faster.
5. R Programming
R is currently one of the trending programming languages for data visualization cum statistical data modeling and analysis. R is widely utilized in the statistical platform, Big Data as well as Machine Learning. R is an open-source, free programming language that includes several enhancements that appear as user-written packages.
R has a sharp learning curve and therefore, you would require a good amount of practical knowledge on coding. However, while talking about syntax and consistency, this platform is a perfect choice. R is the leading preference in terms of EDA (Exploratory Data Analysis, in statistics). EDA is basically a strategy followed to analyze data sets and sum up the main characteristics, often by following the visual techniques. R has a wider community of developers for clientele support.
- Easy data manipulation with packages including tidy, plyr, and dplyr
- Excellent in terms of critical business data analysis and visualization with packages including ggvis, ggplot, and lattice, etc.
Overall, data science requires various types of analytical tools and data sources. The intelligence tools are deployed in data modeling, statistical modeling, and interactive visualizations followed by powerful predictive models through machine learning algorithms. Most of these tools deliver intricate business data processes in one place. This enables the user to implement data science functionalities without requiring you to write codes from the grass-roots level. Lastly, an array of other added tools like Google Excel Sheets cater to data science management by being flexible with faster data slicing, sorting/filtering and visualization, data analysis, and more.