We cannot deny that almost every enterprise extensively utilizes big data to achieve success in today’s market competition. For this reason, many enterprises opt for open source tools to process big data and analyze it. Considering the cost and benefits, they make a good choice.
However, there are so many open-source tools for big data in the market. However, we can group the available open source big data tools in these following categories: data stores, development tools, integration tools, development platforms, and reporting tools. But the ultimate question will still be: which tool is the best one?
Hadoop is no doubt one of the most popular tools. But, there are some other tools that are worth trying for out there. Since more and more organizations trying to reach the competitive benefit of the big data market, it’s no secret that the number of big data tools increasing time by time.
As mentioned above, Hadoop is considered as the most famous big data tool in the market. Thanks to its great capability in processing large-scale data, Hadoop is used by a lot of enterprises around the world. This tool is 100% open-source framework and caries on commodity hardware.
As one of the best open source big data tools, Hadoop allows you to run it on cloud infrastructure as well. This tool consists of four major parts including Hadoop Distributed File System (HDFS), MapReduce, YARN, and Libraries. Each part has its own meaning and function.
The HDFS, for example, refers to a distributed file system which is compatible with high-scale bandwidth. On the other hand, MapReduce is a programming model used for processing big data. Then, YARN is a platform utilized to manage and schedule Hadoop’s resources in Hadoop infrastructures. And Libraries help other modules to work with the tool.
Big names like IBM, Facebook, Intel, Hortonworks, and Amazon Web Services are among the users of Hadoop. There is no doubt that Hadoop is one of the most trustworthy open source big data tools out there. However, it also comes with some cons like disk space issues that sometimes happen.
Apache Cassandra is among the supports behind the success of Facebook. It enables to process structured set of data distributed across high-scale nodes around the globe. This tool works well under profound workloads because of its architecture. Interestingly, it is a free, open-source tool for big data.
This open-source tool distributes NoSQL DBMS structured to deal with big volumes of data spread around abundant commodity servers. It utilizes Cassandra Structure Language (CQL) to interact with the database. Some of the big companies utilizing this tool include American Express, General Electric, Yahoo, Facebook, and Honeywell.
As one of the most popular open source big data tools, Cassandra offers some great benefits for its users. It provides no single point of failure and handles big data rapidly. It also has log-structured storage and automated replication feature. You can also take advantage of its simple ring architecture.
Since nothing is perfect in this world, Cassandra also has some downsides. For instance, it takes some efforts in maintenance and troubleshooting. Moreover, it may need to improve the clustering method. Then, it doesn’t provide a row-level locking feature. But overall, Cassandra is still a dependable free big data tool.
Users can use this big data tool in a range of cloud computing and monitoring solutions. It has some prominent features like cloud-native deployment and massive configuration flexibility. It can also store any type of data. You can use it to manage data from integer and text to dates, arrays, strings, and Boolean.
MongoDB is another excellent example if you are looking for an open-source NoSQL database with an abundance of features. This is a cross-platform tool which is compatible with a lot of programming languages. As one of the best open source big data tools, you can count on MongoDB every time.
This one of the best open source big data tools is also easy to learn. There is nothing hard about installation and maintenance as well. However, it only has limited analytics. And sometimes the tool is slow for particular uses. But, it is still a great tool to try.
Aside from the most popular names mentioned above, there are several other alternatives for you. Apache Spark is one of the best examples. This is an alternative to the Apache Hadoop. This tool is capable to process both real-time and batch data. It operates faster than MapReduce as well.
There is also Apache Storm. This open-source tool is a real-time framework used for processing stream of data. This tool supports any programming languages. And it is one of the most versatile open source big data tools you can use today. It also offers excellent horizontal scalability.
If you need another good example, there is also R Programming environment. This tool is typically employed along with JuPyteR stack for allowing data visualization and wide-scale statistical analysis. This tool runs in a convenient environment and is highly portable. It can run on both Linux and Windows servers too.
In conclusion, there are numerous big data tools available out there. Considering the cost and other advantages, open-source tools are preferred by many enterprises including Facebook, IBM, Yahoo, and others. If you need some recommendations, these three open source big data tools can be the best choice.