Big Data analytics technology combines various techniques and methods. As the name tells, it is about managing and analyzing enormous data sets. Big Data analytics helps gain valuable insights and hidden patterns from complex data.
Not limited to large organizations, even small and medium-sized industries are using big data analytics to gain the best possible outcome. They prove effective when organizations use them collectively to attain appropriate strategic management and execution results. Here we will look into the key technologies that enable big data analytics for businesses.
Big Data technology
Big Data is the software framework developed to develop, extract, assess and manage information arising from large data sets. This occurs when traditional data processing cannot handle large and complex data sets.
The four fundamental big data technologies include the following:1. Data Mining 2. Data Analysis 3. Data storage 4. Data visualization
Data Lake and Data Warehouse
Enormous data is building daily, and organizations maintain it at different locations. As a result, data analysis and reporting turn tough and complex. Big data technologies can help in this regard. Data warehouse and data lake are the two primary technologies used for this purpose.
A data lake is a large repository where the data is unstructured. It is a single store for all enterprise data. All kinds of data are stored here, irrespective of size, shape, and form. While a data warehouse is storage wherein, the data is segregated and ready to use for data processing or statistical purposes.
Though both technologies can be used for storage, their usage can vary based on the enterprise size, the amount of data processed, and the business profile.
Here is some important information useful for your decision:
Data Lakes find apt for multi-access data analytics purposes. However not effective if you expect to have consistent results. Hence, we find that data scientists use big data instead of business professionals. A Data warehouse works as a reliable data storage to attain consistency. This is the reason why business professionals use it for business monitoring and reporting.
- Predictive analytics
Data analytics is the key to big data technologies. Mainly, we will look into four kinds of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive. Concerning big data technologies, predictive analytics and prescriptive analytics are the key, which we will discuss here below:
Predictive analytics combines achievements in data mining, statistics, Artificial Intelligence, Machine Learning, and Predictive Modelling. Predictive analytics helps gain quick business insights, enhance customer experience, mitigate risks, detect fraud and attain process and performance optimization.
- Prescriptive Analytics
Prescriptive analysis is the big data technology that works beyond the future outcome. It gives information on when and why something will happen. If the information is more detailed, the prescription is more accurate. Analytics suggests strategies and solutions for working on a problem based on historical data.
Prescriptive analytics offer similar benefits, such as reduced costs, enhanced customer retention rate, mitigating risks, and improved profit margins.
- Edge computing
The primary objective of big data technologies is to attain accurate and quick data processing. Edge computing is one way to do it. Edge computing processes data nearby the source. This helps solve bandwidth and latency issues concerning sensor data management, including smart homes, IoT devices, and autonomous vehicles.
Edge computing processes data locally, close to the network edge, instead of in the cloud. Minimizing the communication distance between the client and the server reduces latency. In addition, it helps have better performance, keeps the systems secure, generates cost-effective solutions, and attains reliable and scalable solutions.
- In-memory computing
Big data technologies cause heaviness on the CPU. Thus, people began finding solutions that would enhance processing power. Here’s how in-memory computing comes into play. It is a process that involves processing data within the computer’s memory. As a result, the processing power enhances with the cluster of components that work parallelly and share RAM.
In-memory computing helps perform complex computations on extensive data sets with high processing speed. The in-memory computing enables real-time insights, easy scale, and fast performance.
- Stream processing
In the current digitalized world, things happen at a rapid pace. Accordingly, prompt actions require performing. So, to make a competitive edge and manage the real-time data, you have to seek stream processing.
Stream processing is a big data technology that enables real-time data flow. It primarily monitors and identifies threats or significant business information across the data stream. Whether you are talking about accurate time analytics, streaming analytics, event stream processing, or complex event processing, they all come under stream processing.
Some of the significant benefits of stream processing are enhanced risk management, real-time data monitoring, maximized efficiency, excellent fraud detection, and quick and actionable business insights.
Big data technologies add value to any business. You have to choose technologies that find apt for your business, after which you can initiate your project. Big data technologies make better use of creativity and attain bigger benefits it. AI and ML technologies play a significant part in big data development.