Building a Better Tomorrow with Open Source Analytics Tools

There’s nothing more satisfying than building something yourself. You know what you want your creation to accomplish, the unique challenges…

There’s nothing more satisfying than building something yourself. You know what you want your creation to accomplish, the unique challenges you want to tackle, and you pick the features you need. Then you find the raw materials or the base kit to start building from.

That’s the whole idea of open source software: the world should all be one giant Lego kit, where developers of all stripes can share skills, code, and create new and different programs to build the world they want to see. Linux is one of the most prolific operating systems on the planet precisely because it is open source, freely available, and infinitely customizable. Communities that spring up around individual distributions to discuss their nuances and to marvel at (and share) what others have done with the software testify to its lasting value as a phenomenon and an operating system.

So when it comes to building a database, going open source is a natural fit. While all databases have commonalities, every use case has its own hangups, and enterprising builders want to tackle these challenges in their own ways. Open source data solutions like the ones we’ll discuss here allow them to do just that: take the software and make it theirs. Whether the goal is to present data via simple visualizations, connect it to a robust BI tool, or anything else you want to do, having an open source option gives you the power and control you need to get the job done.

In this article, we’ll dig into Hadoop, PostgreSQL, Apache Cassandra, and Elasticsearch.


The world moves fast: tons of data is generated every minute and organizations of all kinds need a powerful system that can keep up with that dataflow. That’s something Hadoop excels at. Originally created in 2006, it’s one of the most popular open source BI tools. The secret to its speed and success in adapting to the current market could be its distributed cloud-storage model, which allows it to process extremely large datasets. The Hadoop Distributed File System (HDFS) transfers data rapidly between nodes and helps provide redundancy—even if one node fails, the rest of the system keeps running smoothly. This also protects the data itself, as multiple copies are stored in multiple locations. If you’re paranoid about data loss then this is a vital feature, protecting you and your data in the case of unexpected power failure or equipment damage.

Additionally, Hadoop uses a Java-based programming framework. Given the popularity of Java and the availability of Java developers, this consideration could make a difference when building a team or using existing developers within an organization. Between the language undergirding it and the power of its architecture, Hadoop has found a sizable following, tackling core BI tasks like statistical analytics and Big Data processing, including handling huge volumes of data from fleets of IoT sensors and more!


As the name implies, PostgreSQL is built on and extends the SQL language. The result is a robust, open source BI tool built for the toughest, most complicated workloads. It’s also got a lengthy pedigree, especially for software, tracing its origins all the way back to 1986. Over the years, the core platform has been continually worked on and refined and is now an ideal option to safely store and scale almost any project.

One amazing asset that PostgreSQL brings to the table is flexibility. Not only does it run on all major operating systems (Windows, all your favorite Linux distros, etc.), but you can also write code in other languages and add it to your Postgres database without recompiling your database. For a dev team tackling a unique data challenge, this could be a major game changer. It also connects easily to a variety of PostgreSQL reporting tools to add even more functionality to this robust open source analytics tool.

Apache Cassandra

Got tons of data distributed across commodity hardware? Cassandra is a distributed NoSQL database that’s built to handle Big Data with high availability via an architecture that leaves no single point of failure. Builders turn to Cassandra for continuous availability, performance scaling, and easy data distribution. Apple, Netflix, and eBay have turned to this system to create the relational databases to suit their needs.

The power to customize Cassandra is in your hands. Choose synchronous or asynchronous replication when updates come around, even optimize highly-available asynchronous operations. Additionally, Cassandra’s unique “ring” architecture, helps ensure amazing uptime, since every node communicates with every other node equally. This even ensures that there’s no downtime when adding new machines. If scaling data is in your future, then thinking of how new machines will come online is an important consideration.


With a name like Elasticsearch, you’d expect it to be flexible and functional, and you’d be right. Elasticsearch tackles data from any source and in any format, allowing you to build the open source analytics solution of your dreams, no matter what data you’re dealing with or where it’s coming from.

Originally compiled in Java, it uses a JSON-style query language that handles structured, unstructured, and time-series data. Elasticsearch’s flexibility also applies to hardware: run it on whatever machines suit your needs, communicating with one or one hundred nodes in the same, seamless style. And its distributed architecture keeps your data backed up in case of failure. Whatever you build, you know your data will be there when you need it and that Elasticsearch will help you perform the analysis you need.

Wrapping Up

Sometimes the hardest part of building the world you want is figuring out where to start. When your analytics dreams were small, it didn’t matter what platform you picked. Now that you have big goals, you have to pick the tools to make them a reality. Choosing an open-source OS like Linux and pairing it with an open-source data tool like the ones mentioned here is a powerful first step towards giving yourself a wide array of options and leveraging both the power of open-source software and of the vibrant community of developers and other professionals who’ve dedicated their lives to working with these programs.

Now that you have the background on each, the decision is in your hands. Choose wisely, build well.

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