In today’s business landscape, data is the key ingredient to long-term growth. However, specialized tools and software are required to translate data into actionable insights. Without it, the data is effectively worthless.
A recent report from Sigma Computing found that 63% of enterprise employees cannot gather insights from their data in the required timeframe, which means the data is more of a productivity inhibitor than a productivity booster. The right tool will provide valuable information and meet your business needs without being too cost-prohibitive.
What Is Big Data?
Big data describes the information that businesses use to automate processes, uncover high-level patterns, and innovate products or services. It is in contrast with traditional data sets, which are typically more homogenous, simpler, and less valuable.
At its core, big data is comprised of three concepts known as the Three V’s:
- Volume: The collective amount of data from a wide range of sources
- Variety: How the data is formatted (structured versus unstructured)
- Velocity: The rate at which the data is received and processed
Big data requires more advanced software and more computing power to process than traditional data sets, so a business that is looking to leverage big data should be prepared to make significant investments in the technology and the IT staff to manage it. However, big data is also more robust and precise, so it can provide more business value and opportunities in the long term.
Also read: No Digital Transformation Without Big Data
What Is Big Data Analytics?
Big data analytics describes the techniques and technologies that are used to analyze very large, diverse data sets. Enterprises can use big data analytics tools to process structured, unstructured, or semi-structured data from multiple sources. Without these tools, big data would be impossible to manage.
In addition to processing data sets, big data analytics methodologies and tools are involved with data visualization, business forecasting, and data-driven decision making. Unlike traditional data technologies, these tools take the raw data one step further by giving it context and meaning. Instead of a mere repository of individual records, big data analytics tools help organizations look at the bigger picture the data creates.
There are a variety of big data tools that help organizations with analytics. Some are all-in-one solutions, whereas others are more focused on a specific area like data visualization or data integration.
Tableau is a leading data visualization software that data analysts and business intelligence teams use to create engaging graphic representations of their data. It connects to data from a wide range of sources quickly and efficiently and has one of the most advanced feature sets on the market.
Although Tableau says its interface is built to meet any user’s skill level, many customers have reported that general users need a bit more training to get the most out of the platform. Experienced data analysts, however, shouldn’t have any trouble navigating Tableau to set it up and start digging into the data.
- No-code queries and visualization
- Easy setup
- Real-time collaboration
- Straightforward integrations
- More expensive than some tools
- Customer support frustrations
Apache Hadoop is an open-source data analytics software framework that’s been available for download since 2006, and it’s one of the most popular tools among data analysts. The Hadoop storage component is the Hadoop Distributed File System (HDFS), which “provides high-throughput access to application data,” and its processing component is Hadoop MapReduce, “a YARN-based system for parallel processing of large data sets.”
Hadoop was designed with the core understanding that hardware failures are inevitable, so the framework should be prepared to detect and address these issues at the application layer. Although Hadoop offers high availability and exceptional parallel processing capabilities, it does not support real-time processing or in-memory calculations—both of which are crucial to efficient data analysis.
- Easy to install
- Strong parallel processing capabilities
- High availability
- Strong data protection
- Advanced training needed
- Requires significant processing power
- Complex integrations
- No real-time processing capability
- No in-memory calculations
Apache Spark is another open-source utility that operates similarly to Hadoop with one key difference: instead of a file system, Spark caches and processes data using the underlying hardware’s RAM. This means Spark is able to fill in the real-time processing and in-memory calculation gaps that Hadoop can’t address, thereby making the Spark ecosystem more effective and efficient.
In fact, Spark’s data processing capabilities for small workloads is 100x faster than Hadoop’s MapReduce. Additionally, Spark is able to work with a wider range of data stores and HDFS, which makes Spark a much more versatile and flexible solution. However, Hadoop is still a more cost-effective option because it doesn’t require large amounts of RAM.
- High-level operators
- More flexibility and versatility than Hadoop
- Supports real-time and batch processing, plus in-memory calculations
- Advanced training required
- Documentation not always helpful
- Extra security measures required
For smaller organizations, Zoho Analytics is an affordable and accessible big data analytics solution. It has an intuitive user interface that makes it easy to create rich dashboards and find the most important information quickly.
Though it’s a solid standalone product, one advantage of using Zoho Analytics is that it can be directly integrated with the broader suite of Zoho business tools, including CRM, HR, and marketing automation applications. What Zoho Analytics lacks in advanced features, it makes up for in ease of use and price.
- Relatively affordable
- Integrates with other Zoho products
- Relatively easy to use
- Lackluster reporting features
- Less suited to large organizations with advanced needs
MongoDB is a NoSQL database that uses document-based collections rather than SQL-based rows and columns. It was built by developers, so it makes application development a much faster, more intuitive process.
It’s also an ideal choice for data-driven organizations that are embarking on their digital transformation journey or those that want to start small and scale up as the business grows. MongoDB’s processing speeds have been a barrier for some customers, so this potential limitation is something that larger enterprises should consider.
- Good at load balancing
- Serverless option
- Relatively easy to use
- Slower processing speeds
- Complex integrations
- Slow customer support
Xplenty is a cloud-based data integration platform that helps streamline data from a variety of structured, unstructured, and semi-structured sources. It’s a low-code ETL platform, which means it cleans, entriches, and transforms each data set before sending them to a data warehouse—all with minimal code needed to complete the process. It’s often used in conjunction with other tools like Tableau.
Xplenty integrates with a sweeping number of applications, including Zendesk, Oracle, and Salesforce. Once connected, Xplenty automates the data integration process from all of the tools you use to run your business and creates a single source of truth for all data-driven insights.
- Straightforward UX
- Easy to use for non-technical folks
- Helpful customer support
- Troubleshooting and debugging can be difficult
- Difficult to use for complicated pipelines
How to Choose the Right Big Data Tool
The right big data tool for your business will correspond to your unique needs. Consider the applications you use, the types of data your business needs to manage, and the information you need to understand from your data when it comes time to make decisions. Then, look for a tool (or combination of tools) that will help with your analytics needs without breaking the bank.