Top Big Data Tools: Pros & Cons

Data Analysis
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You’ve heard it before “quality over quantity.” In today’s business environment, it’s easy to have a lot of data, or big data. But, what really matters is the quality of the data and what you do with it. Big data is factored by its volume (amount of data), veracity (speed) and variety (the formats including structured and unstructured data). To help transform raw information into valuable business insights through data analytics, big data tools can handle the heavy lifting (collecting, processing, transforming) through automation. 

What is Big Data Software?

Big data software refers to the technology that can glean insights from your information (data). The software is able to compile data into a centralised location from different data sources. Whether the data is unstructured (i.e. text, images, etc.) or structured (numerical), it can be sorted and used by leaders in business to make better decisions. 

The software does so by identifying patterns and trends to provide a better understanding of user behavior and analytics to improve internal processes. 

Key Considerations & Common Functions When Choosing Big Data Software 

There’s no doubt that within the $200 billion industry of big data, there are a lot of options to choose from when selecting data processing tools that fit your business needs. To avoid the sentiment of buyer’s remorse, consider the following when shopping the marketplace for big data management tools:

  • Business goals: The most important thing you can do before you invest any time, energy or money in a data processing tool is to understand your ultimate business goals and use for your data. What types of business questions are you looking to answer? What is your optimum outcome? 
  • Data warehousing: To store your data, you’ll need a secure data warehouse and skilled staff that can interpret the data. This means you need the right support systems in place before deploying a big data tool. 
  • Scope of work: Decide whether you want a stand-alone tool that can integrate with other technologies or an all-in-one (best-of-breed, BoB) that has built-in analytics and everything you may need in one system. 

Let’s take a look at some of the best options in this abbreviated big data tools list:

Apache Storm 

Owned by Twitter, Apache Storm is an open-source, distributed real-time framework that processes unstructured data sets. It has the ability to process unbounded streams of data, which means the data has a beginning without a defined end. 

The big data tool is free and can support any programming language, including Java, Python, C# and more. Users can access the tool via web connectivity. Some of Apache Storm’s key features include being: fast and scalable, fault-tolerant, and reliable. Per second per node, it’s able to process one million 100-byte messages. Its use cases include: ETL, real-time analytics, online machine learning, fraud detection, network monitoring, social analytics, mobile engagement and distributed RPC, to name a  few. 

Additionally, the tool can integrate with current technology to process streams of data in complex ways. Businesses will look to Apache Storm to guarantee data processing and benefit from its easy-to-use and easy-to-deploy functionality. On the downside, Apache Storm cannot run scheduled jobs. 


For businesses looking for a tool that will help make quick decisions, MongoDB might be the answer. However, it’s important to note that it serves as a database of documents, and therefore, it’s best used by developers and those with a background in data. 

Additionally, it’s not recommended for use if you are looking for strong consistency across your database. For example, it could fall flat for finance teams that deal with billing, fraud detection, operations support, etc. That being said, MongoDB is an open-source, NoSQL database. It runs on NET applications, Java and MEAN software stack. 

Some of MongoDB’s features are that it can: store various types of data (array, object, integer, date, etc.), is flexible and can partition data easily across servers in the cloud, provides flexibility in the cloud as it uses dynamic schemas (making it possible to prepare data fast). If you have datasets that change frequently or are unstructured, MongoDB is a good alternative to a database. The tool can store data from content management systems, mobile apps and more. 


Apache Cassandra was developed by Facebook initially as a NoSQL solution. Now, many big organisations from Netflix to Twitter leverage its capabilities. It’s a big data tool that can be used on different types of data sets, from structured to unstructured and the semi-structured in between. 

That being said, it’s main focus is for structured data sets. Cassandra’s features are that it can process large amounts of data very quickly, has no single point of failure (every node is identical so there are no network bottlenecks), is distributed amongst data centers, offers cloud availability and linear scalability. Thousands of companies with large data sets trust the software because of its scalability and reliability. 

As new machines are added, Cassandra will linearly add data without any downtime to applications. Furthermore, there are third-party services available to offer support for Apache Cassandra. Here’s a look at some good use cases for Cassandra: transaction logging, tracking, event history, storing time series data, and telematics. 

Cloudera Data Platform 

Cloudera Data Platform (CDP) is an enterprise data cloud that provides analytics  for businesses with security and governance. It combines the technology of Hortonworks and Cloudera Technology across its hybrid and multi-cloud environments. Users can run CDP public cloud and CDP private cloud (on-premise). 

The system makes it possible to store, analyse and process all data in a unified location. Plus, it brings to the table machine learning services as well as a data warehouse. With theData Hub, organisations can create their own custom business applications. Here’s a look at some key features: audit trails, SAML authentication, cluster management, resource management, monitoring, diagnostics, automated deployment, and Kerberos authentication. 

Small, medium and enterprise customers have leveraged this web-based tool. However, if you’re looking for a tool that can function on mobile devices, then Cloudera is not the solution because it’s solely on desktop. 

Apache Hadoop

As one of the most popular data processing tools, Apache Hadoop is open-source and runs on commodity hardware in an existing data center. It’s a very popular solution because it can send data to different servers, so it’s a great solution to process large sets of data when available memory is overrun. 

It’s written in Java and used for processing and analysing big data. Hadoop is made up of parts, namely: Hadoop Distributed File System (HDFS) for storage, Map-Reduce for processing and YARN for resource management. 

Unlike many of the solutions above, Hadoop doesn’t support real-time processing. Instead it works through batch processing and there is no possibility for in-memory calculations. It’s main purpose is to store massive amounts of data for processing on multiple computers so that data processing can happen quickly and in parallel (rather than bogging down one large computer). 

Hadoop was initially created to process increasing amounts of data and became available to the public in 2012. Today, businesses benefit from the ability to query and analyse large data sets with this free framework and off-the-shelf hardware. 


SolveXia is a cloud-based platform that’s best known as a human analytical finance automation tool. The system can run on desktop and mobile through the web-based interface that makes it easy to automate business processes and reap analytics from big data in real-time. 

The tool eliminates the need for disparate spreadsheets and allows users to model data processes and run them automatically through the cloud. Furthermore, instead of spending time creating reports manually, SolveXia can do so with the click of a button and create real time online dashboards and visualisations so that your team can make better data-driven decisions immediately. 

Its automation is designed for a no/low code approach and through its existing data automation library, it’s easy to drag-and-drop to design processes. Importantly, the tool stores documentation and can easily procure audit trails, if need be. 

While many finance teams across industries rely on specific people to perform tasks, SolveXia reduces the risk of key person dependencies and makes it possible to expedite many business processes with utmost accuracy of data. In turn, your organisation can meet regulatory requirements and reduce compliance risk. 

The Bottom Line 

This glimpse into some of the most used data processing tools shows that there are many solutions in the market that are worthy of consideration. However, each tool is known for its own main feature or benefit, and that’s why it’s so necessary to first define your business goals and objectives before selecting the tool for your organisation. 
Should you have any more questions about big data tools, many tools such as SolveXia provide a free demo to showcase how automation and analytics can benefit your business.


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