Guide to choose the right Data Analytics tool types

Table of Content

Spreadsheets

Spreadsheets are by far the classic application that is in use for data storage and analysis. Though databases may sound like the advanced form of spreadsheets, databases require an interface to load data to it, whereas spreadsheets can be used standalone or as an interface for databases. The only drawback with spreadsheets is the data preparation process like data cleaning, data integration, and data reduction cannot be done. The most common tools are MS Excel and Google sheets.

Microsoft Excel

Excel is easy to use and convenient tool to view data. It is highly accessible and available to many people. Excel does not require any interface to add data to it and is excellent for data collection.

Google Sheets

Google sheets provide all and more tools than excel and are also cloud-based, making is an easily accessible, more efficient workspace and more security to the data being stored.

Databases

Databases are tools that store and organize data in a convenient user-oriented manner. Databases can be in a relational table format, column database, document, or graphical form.

Relational Database

Relational databases are widely used for data storage and transaction purpose. Popular relational databases are Microsoft Access, SQL, and Oracle, but not known for data analysis.

Column Data

Though databases cannot be used for the purpose of data analysis, rather data for analysis can be retrieved easily through query commands from these databases. The result of these queries is in the form of column data which can be applied for analysis. It is basically obtaining a small chunk of data from a larger set.

Self Service Data Visualization

Data visualization has inbuild service options making it easier to use even by a non-technical person or somebody not familiar with analysis tools. It is a simple, quick, and versatile tool to learn and use than spreadsheets. Let us look at some of the common tools available.

Tableau

Tableau is the leader in data visualization that started more than 10 years ago and has been upgrading since. However, Tableau has expensive license costs involved making it not easily accessible to everyone.

Power Bi and Qlik Sense

Power Bi and Qlik Sense are free to download but the data, work, and results need to be shared with the company making it vulnerable to expose crucial data. But if your data does not involve sensitive information, Power Bi and Qlik Sense are cost-effective and great tool to use.

AWS Quicksight

AWS Quicksight is a tool by Amazon that brought down the cost of these visualization tools and along with cloud technology, AWS Quicksight is very efficient for data analysis. Click here for a free trial.

Programming Languages

The world is becoming data-driven and free open-source software are available for data analysis. The widely used programming languages are Python and R.

Python

Python is a simple and intuitive programming language, which can be used to write simple programs to machine learning algorithms. Python has many add-on features and libraries to solve any kind of problem. It is a must-know language for data science professionals.

Apart from being an ideal language for analysis. it is also used in Artificial intelligence, and for internal infrastructure by Google and weather data by Forecast Watch, NLP with the ability of neural network.

R

R is suitable for analyzing large number of data sets and statistical computations. The build-in functions of R language are rightly suited for perfectly visualizing data. R is an open-source software application that can work across operating systems. The drawback of R being taking up too much memory space, slow operations, and security issues.

Big Data Tools

Big data is basically dealing with a massive amount of data and tools used to handle such large data use different strategies and techniques to handle the continuous flow of data, like from the internet, IoT and social media.

Hadoop

Hadoop is a framework with a collection of software to work on distributed data. The library has many tools and a variety of applications such as report generation, financial trading, forecasting, understanding customers, etc.

Data Lake

Data Lake is a repository of structures, unstructured, and raw data, which can be retrieved to do analysis procedures. It is mainly used by Data Scientist and Business Analysts since the query results from Data Lake is faster than from Data warehouse. Data Lake repository is available for free trial by Google, Microsoft and Amazon.

Spark

Spark is a unified analytical engine for large scale data processing. It’s a collection of many libraries with a different purpose from Spark SQL, MLlib for machine learning, and GraphX for graphs.

Cloud Platform

The cloud application or software does not run locally but in a cloud provider. Cloud providers typically provide you with all the infrastructure necessary like memory, server, tools, and even SaaS solutions. The cloud platform can be made use of in wide range, from independent use like students to an entrepreneur to run a company. There are three giants providing cloud platform.

AWS

Amazon Web Services provides a computing platform, Application Programming Interface (API), and different tools from simple accounts management to machine learning. These services are provided by AWS on the pay-as-you-go method. Amazon’s Elastic Compute helps you to easily scale up and down according to needs.

Microsoft Azure

Microsoft Azure is another cloud service provider by Microsoft and leads in providing SaaS, PaaS, and IaaS services through their data center. Microsoft Azure also supports many third-party software, frameworks, and tools.

Google Cloud Platform

The cloud platform provided by Google is top-notch with the high-efficient infrastructure that Google uses for its internal purpose as well. Apart from management tools, it also provides Machine Learning, Analytics, and Computation tools as well.

Conclusion

With all these tools and service providers available in the market, the right tool for analysis is based on the purpose of the business or work, the type of data, and the amount of data that needs to be processed. Having a general idea of all these available tools will come in handy when a new requirement arises at your business or for personnel use.