Areas of application

Examples of applications

Examples of applications within industries

Business reports and analysis

Data mining and dredging

Data warehouses

Big Data and stream processing

We primarily associate Big Data as datasets with a significant amount of data, speed of processing and diversity in the data.
When it comes to Big Data, we typically process large amounts of unstructured data. For some companies, this can be several dozen terabytes of data (e.g. corporate/corporate department level). For others, this may involve hundreds of petabytes (e.g. group entity level).
Speed means receiving and processing data from sources at high frequency and using it for presentation in an appropriate structure. Some smart devices with internet access operate in real or near-real time, and require real-time assessment and action. For example, IoT allows a company to monitor shipments in real time and incorporate data on route conditions into decisions that can save a lot of time and money.
Diversity means handling many types of data. With the development of Big Data, new and unstructured types of data have begun to be collected. Unstructured and semi-structured data types, such as text, audio and video, require additional pre-processing to bring out their business relevance.

Stream processing is a big data technology that focuses on the real-time processing of continuous streams of variable data – providing immediate results from their analysis.
Benefits include immediate detection of conditions and anomalies in a very short time, which is useful for tasks such as early fraud detection.
Streaming data processing systems in Big Data are effective solutions when we have operating scenarios that require: minimal latency, built-in features to handle imperfect data, SQL queries on data streams to build extended operators, guaranteed ability to generate predictable and repeatable results, stored and streamed data integration abilities, fault tolerance features, guaranteed data security and availability, real-time response ability with minimal load for high-volume data streams and the ability to automatically scale applications between multiple processors and nodes.