Top 7 Open Source Big Data Tools in 2020
Data has become a powerful tool in today’s workforce, where it is helping to translate tremendous 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 the information.
In the present scenario, big data tools offer endless functionalities from insight and forecasting to cost efficiency and time-saving.
Let’s look at the top 7 open source tools and how they can deepen our understanding of complex data.
#1 Hadoop
Hadoop is recognized as the most popular big data tool for analyzing large sets of data because the platform can send data to different servers. This open-source software framework is often used when data volumes exceed available memory. 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.
#2 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.
#3 Apache SAMOA
Scalable Advanced Massive Online Analysis (SAMOA) is an open-source platform built for mining big data streams with a special emphasis on machine learning enablement. SAMOA supports Write-Once-Run-Anywhere (WORA) architecture which allows for seamless integration of multiple Distributed Stream Processing Engines (DSPEs) into the framework. Apache SAMOA provides for the development of new ML algorithms while avoiding the complexities of directly dealing with the distributed stream processing engines like — Apache Flink, Storm, and Apache Samza.
#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 in 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 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 accommodate more data and users as per requirements easily. In addition, Cassandra is well-accepted for its proven fault tolerance on commodity hardware and cloud infrastructure, making it crucial for big data uses.
#6 Elasticsearch
It is a dependable and safe open source platform where you can take any data from any source, in any format to — search, analyze and envision in real-time. It is designed for horizontal scalability, reliability, and ease of management. All of this is achieved while combining the speed of search with the potential of analytics. It is based on Lucene a retrieval software library compiled initially in Java. It uses a developer-friendly, JSON-style, query language that works well for structured, unstructured and time-series data.
#7 HPCC
HPCC (High-Performance Computing Cluster) is an open-source, big data computing platform developed by LexisNexis Risk Solutions. Its public release was announced in 2011. It is a fast, accurate and cost-effective platform built for high-speed data engineering. Its 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.
Final Thoughts
Now that you have a more robust understanding of today’s big data tools, you can better determine how to build your data skillset.
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