Data has become a buzz-word in the current era. A massive amount of data is being produced every year. According to Forbes, 1.2 trillion gigabytes of data were produced in 2010, which increased to 59 trillion gigabytes by the end of 2020. Data Analytics is the process of acquiring meaningful information from raw data, which can be used for effective decision-making. Almost all large companies are using data analytics to enhance their performance, reduce costs, and improve customer satisfaction. This has increased the job-value of data analysts in the market. In this post, I would share the best open-source data analytics software, which is being used in the industry.
1. Python Language
Python is the most-widely used open-source data analytics tool. It was introduced in the year 1991 by Guido Van Rossum, but it became popular in 2006. Then, with every passing year, an increasing trend in its popularity was observed.
Image Source: Stackoverflow
Python ranks among the top five most popular languages globally, especially in Machine Learning and Data Science; it is the best choice. It is easy to learn and provides a large set of libraries to handle the massive amount of data. It supports various frameworks, and it has an active community, which makes it the language of the future.
2. R Language
R is a statistical language. Ross Ihaka and Robert Gentleman developed it in the year 1993. It is an open-source language capable of solving statistics, Data Science, Analytics, and Machine Learning. It is one of the most widely used tools in the Data Science industry. It has a large set of packages, which can be effectively used to solve data science research problems. Its popularity is increasing with every passing year, and it has an active community, which contributes to its development.
3. Tableau
Tableau is an open-source data analytics software. It is one of the most popular tools in the data science industry, which supports a large number of file types. It has a simple user interface and has the capability to visualize large datasets effectively. It is an easy-to-learn software and comes with frequent updates.
4. Power BI
Power BI is an open-source tool by Microsoft. It is a popular data visualization/ Business Intelligence tool. It has a user-friendly interface and supports and many input files and can extract data from large databases and clouds. It can be integrated with programming languages such as Python. It is an easy-to-learn tool and comes with frequent updates.
Its functionality varies with edition. Popular editions of Power BI include:
- Power BI Desktop
- Power BI Mobile
- Power BI Pro
- Power BI Premium
- Power BI Embedded
- Power BI Report Server
5. Rapid Miner
Rapid Miner is the top predictive analytics tool being used in the industry. It has excellent capabilities also works in a cloud environment. It is used for data processing, building, and deployment of Machine Learning models. Its simple, user-friendly interface and wide range of file support is its core strength.
6. KNIME
KNIME Analytics Platform is open-source software for solving data science problems. It is an intuitive tool and supports many file types such as CSV, PDF, XLS, JSON, XML, etc. It can also be connected to databases and data warehouses such as Oracle, Microsoft SQL, Apache Hive, Load Avro, Parquet, HDFS, S3, and Azure, etc. It helps in understanding data and designing data science workflows and reusable components.
7. Talend
Talend is an open-source ETL tool developed in the Eclipse graphical development environment. It provides complete and clean data from various sources in an efficient way. It maintains data quality, feeding Big Data integration, cloud API services, Data Catalog, and Stitching Data Loader.
8. Apache Spark
Apache Spark is an open-source tool by Apache Software Foundation. It is a distributed processing system, which is used for big data workloads. It is the most commonly used tool in the field of Big Data Analytics and has the capability to handle very large-scale datasets effectively.
Conclusion
If you plan to build a career in Data Science, you must have a grip on all of these tools.
References
- www.forbes.com/sites/gilpress/2021/12/30/54-predictions-about-the-state-of-data-in-2021/?sh=2ef2063e397d
- codeinstitute.net/blog/what-is-python-used-for/
- emerline.com/blog/why-python-is-so popular#:~:text=But%20when%20did%20Python%20become,2007%2C%202010%2C%20and%202018
- stackoverflow.blog/2017/10/10/impressive-growth-r/
- Cover photo credit: niasra.uow.edu.au
It was useful. Thanks