Top 10 Open Source Big Data Tools for 2020

data scientist using big data

Data has become a powerful tool in today’s workforce, where it is helping to translate massive amounts of structured and unstructured information into valuable business insights. As a result, the current market is flooded with a range of big data tools to process all this information. 

Today’s big data tools offer endless functionalities from insight and forecasting to cost efficiency and time saving. Below we have outlined a list of the top 10 tools and how they can deepen our understanding of complex data

The Top 10 Open Source Tools for Big Data

1) MongoDB

MongoDB is a database of documents that offers data professionals flexibility and scalability in their work, and provides added convenience through indexing and querying capabilities. The idea behind MongoDB is that it models documents in a way that is easy for developers to use. At the same time, it can meet complex requirements with high scalability and has drivers for more than 10 languages — with dozens more in the community.

2) Pandas

There are so many uses for Pandas that it is impossible to list them all. Think of Pandas as a tool that is the home for your data. You will use Pandas to get to know your data and to put it to good use by transforming, cleaning and analyzing it. Pandas is also a very important package for  professionals who use Python in their work as data analysts or data scientists. It is frequently the backbone of many data projects. 

3) Hadoop

This open source software framework is often used when data volumes exceed available memory. Hadoop is recognized as the most popular big data tool for analyzing large sets of data because the platform is able to send data to different servers. It is also ideal for data exploration, filtration, sampling and summarization. If you plan on using data science in your career, then you definitely should learn Hadoop.

4) Apache Spark

Apache Spark is often the preferred tool for data analysis over other types of programs due to its ability to store computations into memory. The open source platform can quickly run complicated algorithms, which is necessary when dealing with large data sets. Plus, by caching memory, data scientists are less likely to lose valuable information.

5) Apache Storm

Apache Storm is a distributed real-time framework used to reliably process unbounded streams of data (or, streams that have a start but no defined end). This free and open source system offers a number of distinct advantages including fault-tolerance, multiple language support, scalability, easy setup and more. These features make Apache Storm a must-have for data processing.

6) Cassandra

Cassandra is a free and open source database management tool created in 2008 by Apache Software Foundation. Many data professionals recognize it as the best open source big data tool for scalability, as it is able to easily accommodate more data and users as per requirements. Additionally, Cassandra is popular for its proven fault-tolerance on commodity hardware and cloud infrastructure, making it invaluable for big data uses. 

7) RapidMiner

This data science platform is a tool for teams that combines data prep, machine learning and predictive model deployment. According to the official RapidMiner website, over 40,000 organizations trust the tool to drive revenue, reduce costs and avoid risks. The tool also offers a number of unique key features such as real-time scoring, enterprise scalability, graphical user interface, scheduling, one-click deployment and more. 

8) HPCC

High-Performance Computing Cluster (HPCC) is a fast, accurate and cost-effective platform built for high-speed data engineering. HPCC’s unique advantage comes from its lightweight core architecture that allows for enhanced performance, near real-time results and full-spectrum operational scale — without a large-scale development team, unessential add-ons or additional processing costs.

9) Neo4j

Neo4j Graph Platform offers a connections-first technique to applications and analytics across the enterprise. This unique tool is specifically optimized to map, analyze, store and traverse networks of connected data to uncover invisible contexts and hidden relationships. Some unique features include a flexible schema, built-in Neo4j browser web application, full ACID properties support and native graph storage. 

10) R (Programming Tool)

R is an open source language and environment for statistical computing and graphics. This simple and effective tool was developed by Ross Ihaka and Robert Gentleman. Some of the most important features include user-defined recursive functions, graphical facilities, effective data handling/storage and fast calculations.

 

Final Thoughts

Now that you have a stronger understanding of today’s big data tools, you can better determine how to build your data skill set. Our boot camp is a great way to learn several of the data analytics tools above. 

You can also explore our Beginner’s Guide to Data Science to help you understand the fundamentals for a successful career in the field. 

Visit the UofT SCS Data Analytics Boot Camp to discover how you can grow your data skill set. Attend online or in-class and learn the fundamentals of Pandas, MongoDB, R (Programming Tool), Hadoop and more in only 24 weeks. Get more information on our curriculum.