The Top 7 Big Data Tools for 2022

There’s no doubt you’ve heard the buzz by now: big data is the next big thing! The next sliced bread! It’s revolutionizing the world around us!

While these claims are in many ways true, what people often neglect to mention is how much work big data involves. If you were just reading the headlines, it would be easy to presume that big data is nothing but an eternal spring of information, pouring nothing but useful insights into the world. The truth is, big data is a mess—in fact, that’s its definition: the term ‘big data’ refers to datasets too large, too complex, and too disordered to make sense of. 

Fortunately, between the mess of raw data and the revolutions we read about, there’s an endless army of data scientists and analysts. Their job? To quietly bring order to the chaos. Their responsibility is to transform big data into structured formats that are useful for our needs. This takes time, technical expertise, and plenty of patience. Luckily, there are many big data tools available to help things along. In this post, we explore just a handful of our favorite big data tools.

  1. Apache Spark
  2. Apache Hadoop
  3. Apache Flink
  4. Google Cloud Platform
  5. MongoDB
  6. Sisense
  7. RapidMiner

To skip to a specific tool, just use the clickable menu.

Ready for the ride? Strap in, hold tight and let’s discover the top 7 big data tools for 2022.

1. Apache Spark

Pricing: Free and open-source.

Deployment: Broad deployment options available.

The Apache Software Foundation is an American non-profit organization that supports numerous open-source software projects. These are created and maintained by an open community of developers who regularly update and feed innovations into the tools. While there are dozens of Apache tools, one of the better-known ones is Apache Spark.

A unified analytics engine, Spark was first launched in 2012, built especially for processing big data via clustered computing. It supports both stream and batch processing. It has inbuilt data streaming modules, SQL, and machine learning algorithms, as well as high-level APIs for R, Java, Python, and Scala (meaning you can use your preferred language when programming). Because it’s open-source and has so much in-built functionality, Spark is adaptable to almost any field that utilizes data science.

Initially, Apache created Spark to address the limitations of another tool, Hadoop MapReduce. Spark is more efficient and versatile, and can manage batch and real-time processing with almost the same code. This means older big data tools that lack this functionality are growing increasingly obsolete. Apache says that Spark runs 100 times faster than MapReduce, and can work through 100 terabytes of big data in a third of the time, using a fraction of the machinery. Haven’t come across Spark yet? Fear not: you will!

2. Apache Hadoop

Pricing: Free and open-source.

Deployment: Broad deployment options available.

Okay, so we may have just said that Apache Spark is outperforming other big data tools—in particular Apache Hadoop—but that doesn’t mean the latter is completely useless. Like Spark, Hadoop is an open-source framework, consisting of a distributed file system and a MapReduce engine that store and process big data respectively. Although the framework is older (launched in 2006) and slower than Spark, the fact of the matter is, many organizations that once adopted Hadoop won’t simply abandon it overnight because something better came along. 

Plus, there are upsides to Hadoop. For starters, it is tried and tested. And while it is not the most user-friendly piece of software (and is inefficient at managing smaller datasets and real-time analytics) it is robust and reliable. Hadoop can be deployed on most types of commodity hardware and does not require supercomputers. Finally, because it distributes storage and workload, it’s also low-cost to run. And if that’s not enough, many enterprise cloud providers still support Hadoop. For example, IBM’s Analytics Engine. So… this is one tool that you may still come across on your data analytics travels.

Pricing: Free and open-source.

Deployment: Broad deployment options available.

 We don’t want to sound like a broken record, but there’s one more big data tool from Apache worth mentioning (although there are literally dozens to choose from). Apache Flink is another open-source unified processing framework, launched in 2011. Like Spark, it supports both batch and stream processing. The main difference is that Spark uses batch processing for both batch and streaming jobs, whereas Flink uses streaming to execute both types, this time in a pipelined manner. Without getting too heavily into the technical details, all this means is that Flink can process data much more quickly and with lower latency (or delay) than Spark, which measures latency in seconds, rather than microseconds.

None of this is to say that Flink is better than Spark. Indeed, both far outcompete their competitors in terms of big data processing speeds. Spark also has much greater support than Flink—it is supported by all major Hadoop frameworks, whereas Flink is not. While there will always be discussions about Flink overtaking Spark, the truth is there is plenty of space for both. And now, it’s time to bow out of the Apache projects for a moment—there are lots of Apache big data tools, but we want to cast the net a bit wider too.

4. Google Cloud Platform

Pricing: Starts at $0.01, but there is a free version, as well a free trial.

Deployment: Cloud, Mac, and Windows desktop and mobile (Android and Apple).

Google Cloud Platform unifies various cloud computing services that Google itself uses for end-user products like Google Search, Gmail, YouTube, and Google Docs (to name a few). Although not a dedicated big data tool, the platform has several big data tools embedded within it, including Dataflow (a managed streaming analytics service) and Data Fusion (for building distributed data lakes via the integration of on-premise platforms).

However, perhaps the most notable tool is BigQuery, a fully managed, petabyte-scale analytics data warehouse. A platform as a service, it has inbuilt machine learning tools and allows you to process vast amounts of big data in close to real-time. BigQuery gives users the ability to create and delete a variety of objects from tables and views, to customized user functions. Data can be imported from numerous formats, too, including CSV, Parquet, Avro, or JSON.

Perhaps most importantly, BigQuery is SQL compatible, making it very easy to use. The main drawback is that it’s a bit slow to catch up with the latest innovations compared to other platforms. However, considering its affordable price, scalability, and standard configurations, this is a small price to pay for most use cases. Plus, many organizations use it so it’s hard to escape!

5. MongoDB

Pricing: Priced per feature, but has a free trial version so you can test features out.

Deployment: Cloud, Desktop (Mac, Windows, Linux), and on-premise.

While Google Cloud’s BigQuery is excellent for structuring big data, MongoDB is a flexible, scalable non-relational database (also known as a NoSQL database.) This simply means that it’s designed for unstructured big data in the form of documents, rather than using rows and columns (as used in relational databases). As a big data tool, MongoDB is widely used by both small startups and large enterprises.

Benefits of MongoDB? It’s easy to set up and use. And because it’s designed to manage unstructured data, it’s schema-free (meaning it doesn’t have to conform to a particular data type which, in turn, means less work up-front). It’s not all perfect, mind—its search function is a little slow for instance—but what keeps the tool popular is the team behind it. They provide excellent support and are constantly releasing updates and feeding innovations into the product. And it seems to be paying off: with over 175 million downloads, MongoDB is the most popular NoSQL database in use, allowing all kinds of users to query, manipulate and analyze their unstructured data.

6. Sisense

Pricing: Pricing is available on request. A free trial is also available.

Deployment: Cloud, Desktop, on-premise (Windows and Linux), and mobile.

Some of the earlier entries on our list, such as the Apache big data tools, require a bit of programming expertise. However, if you’re seeking a big data tool that requires no specialist technical skills at all, then Sisense’s Big Data platform could be the right product for you. On its website, it claims to be ‘the only Big Data analytics tool and data visualization tool that empowers business users, analysts, and data engineers to prepare and analyze terabyte-scale data from multiple sources—without any additional software, technology, or specialized staff.’ Sounds almost too good to be true, right?

While there are tools that manage big data, and tools that offer excellent data analytics and visualization, Sisense straddles the gap between the two. Offering custom implementations for sectors including healthcare, manufacturing, and retail, the tool provides a fast analytical database, built-in ETL tools, Python, and R, and a solid data analysis and data visualization suite. Any drawbacks it has lie precisely where you might expect trade-offs for the easy functionality. For instance, its easy-to-use drag and drop dashboard limits how much you can customize. It also has a few stability issues and set-up can be complex, but overall, once it’s up and running and you’ve gotten used to its quirks, Sisense is a solid business intelligence tool.

7. RapidMiner

Pricing: Pricing on request. A free trial and a free version is also available.

Deployment: Cloud, desktop (Mac, Windows), on-premise (Windows, Linux).

Like Sisense, RapidMiner aims to give data professionals of all abilities the tools to rapidly prototype data models and execute machine learning algorithms without coding expertise. It brings together everything from data access and mining, to preparation and predictive modeling, all via a process-focused visual design. Built using Java, RapidMiner is easily integrated with existing java apps, although the no-code approach makes it a bit challenging for those who are more comfortable programming from scratch. That said, it has Python and Java modules that can be tweaked using code.

Although RapidMiner has an interface that’s more intuitive for academic users, there are support packages available (although these do cost quite a lot extra). As user familiarity with the tool and functionality grows, you can extend the software with additional packages. Perhaps its major downfall is that it does not handle vast amounts of data very well… not exactly ideal for a big data tool. However, it has still made it onto our list as it comes with such a minimal learning curve. Think of RapidMinder as a quick fix for your big data needs!

Wrap up and further reading

From code-heavy tools to those requiring no programming skills whatsoever, we hope this list has sparked your intrigue! Be sure to research more and you’ll find that there is no end to the different big data tools available on the market, all catering to different use cases and data analytics tasks. 

If you’re ready to start forging a new career in data analytics, check out this free, 5-day data analytics short course or read the following related articles for more details:

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