In today’s business landscape, data is the key ingredient for 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 within the required time frame, meaning that 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 expensive.
What is Big Data?
Big Data describes the information that companies use to automate processes, discover high-level patterns, and innovate products or services. This contrasts with traditional datasets, which are generally more homogeneous, simpler and less valuable.
At its core, big data is made up of three concepts known as the three Vs:
- Volume: The collective amount of data from a wide range of sources
- Variety: How the data is formatted (structured versus unstructured)
- Speed: The speed at which data is received and processed
Big Data requires more advanced software and more computing power to process than traditional datasets. Therefore, a business looking to leverage big data must be prepared to invest heavily in technology and IT personnel to manage it. However, big data is also more robust and accurate, so it can provide more business value and opportunities long-term.
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What is Big Data Analytics?
Big data analysis describes the techniques and technologies used to analyze very large and diverse data sets. Businesses 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.
Apart from processing datasets, big data analytics methodologies and tools are involved in data visualization, business forecasting, and data-driven decision making. Unlike traditional data technologies, these tools take raw data a step further by giving it context and meaning. Instead of just a repository of individual records, big data analytics tools help organizations see the big picture created by the data.
Read also : Data Management with AI: Making Big Data Manageable
There are a variety of big data tools that help organizations perform analytics. Some are all-in-one solutions, while others focus more 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 graphical 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 claims its interface is designed 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, should have no trouble navigating Tableau to set it up and start digging into the data.
- No-code queries and visualization
- Easy installation
- Real-time collaboration
- Simple integrations
- More expensive than some tools
- Customer service frustrations
Apache Hadoop is an open source data analysis software framework available for download since 2006, and it is one of the most popular tools among data analysts. The Hadoop storage component is the Hadoop Distributed File System (HDFS), which “provides high-speed access to application data”, and its processing component is Hadoop MapReduce, “a YARN-based system for parallel processing large data sets”.
Hadoop was designed with the basic understanding that hardware failures are unavoidable, so the framework should be prepared to detect and resolve 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 for efficient data analysis.
- Easy to install
- Strong parallel processing capabilities
- High availability
- Strong data protection
- Advanced training required
- Requires significant processing power
- Complex integrations
- No real-time processing capability
- No in-memory calculations
Apache Spark is another open source utility that works similarly to Hadoop with one key difference: instead of a file system, Spark caches and processes data using the RAM of the underlying hardware. This means that Spark is able to fill real-time processing and in-memory computing gaps that Hadoop cannot, making the Spark ecosystem more effective and efficient.
In fact, Spark’s data processing capabilities for small workloads are 100 times 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 remains a more cost effective option as it does not require large amounts of RAM.
- High Level Operators
- More flexibility and versatility than Hadoop
- Supports real-time and batch processing, as well as in-memory calculations
- Advanced training required
- Documentation not always helpful
- Additional security measures required
For small businesses, 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 quickly find the most important information.
Although a strong standalone product, one of the benefits of using Zoho Analytics is that it can be directly integrated with the wider suite of Zoho business tools, including CRM applications. , HR and Marketing Automation. 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
- Poor reporting features
- Less suitable for 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, which makes app development a much faster and more intuitive process.
It’s also an ideal choice for data-driven organizations embarking on their digital transformation journey or those looking to start small and scale as the business grows. MongoDB’s processing speeds have been a hindrance for some customers, so this potential limitation is something 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 cleanses, enriches, and transforms every dataset before sending it to a data warehouse, all with minimal code needed to complete. the process. It is often used in conjunction with other tools like Tableau.
Xplenty integrates with a large number of applications, including Zendesk, Oracle, and Salesforce. Once connected, Xplenty automates the process of integrating data from all the tools you use to run your business and creates a single source of truth for all data-driven insights.
- simple UX
- Easy to use for non-technical people
- 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 match your unique needs. Consider the applications you use, the types of data your business needs to manage, and the insights you need to understand from your data when it comes time to make decisions. Then look for a tool (or a combination of tools) that will help you meet your analysis needs without breaking the bank.