Data intake is an ongoing process, so the filtering and analysis process works best when it is continuous. However, most organizations struggle with this aspect and instead process data in batches. Streaming analytics, also known as event stream processing, solves this issue by employing methods such as continuous queries to process and analyze data in real time.
You can extract data from multiple sources at any given time. Traditionally, Data Management tools can only process data that is at rest – or is not being used at the moment – in large batches. While this might work for many sources, you sometimes need real-time data to act on it immediately.
With streaming analytics platforms, organizations can ensure that they can act on incoming data as and when it comes their way. These events or streams are triggered by actions such as equipment failure, a public social post, a website click, or anything else. For example, if a cloud server goes down, the company cannot wait 24 hours to get it back up again. But with streaming analytics, you can avoid these issues. The main benefit of such platforms is that they help organizations find value and meaningful patterns in their data in real time.
Other Definitions of Streaming Analytics:
- “The analysis of huge pools of current and ‘in-motion’ data through the use of continuous queries, called event streams. These streams are triggered by a specific event that happens as a direct result of an action or set of actions, like a financial transaction, equipment failure, a social post or a website click, or some other measurable activity.” (Databricks)
- “The processing and analyzing of data records continuously rather than in batches. Generally, streaming analytics is useful for the types of data sources that send data in small sizes (often in kilobytes) in a continuous flow as the data is generated.”(Google Cloud)
- “The continuous flow of data generated by various sources. By using stream processing technology, data streams can be processed, stored, analyzed, and acted upon as it’s generated in real-time.” (Confluent)
Use Cases Include:
- Call center monitoring: Call centers simultaneously monitor different issues from thousands of customers. Now, streaming analytics platforms that use technologies like sentiment analysis, service level alerts (SLA), and predictive analytics can handle issues no matter the volume or when they come.
- Real-time personalization: E-commerce stores and news media outlets can analyze the incoming data stream and personalize recommendations for specific customers or readers. For example, they can track product or article clicks, demographic information, and other parameters to offer better products or articles.
- Fraud detection: In the case of financial transactions, banks and similar institutions can analyze the incoming data stream from all their customers’ accounts and identify malicious behavior almost immediately. By doing so, they can implement the necessary security procedures for the accounts as soon as possible.
- Streamlines business operations by having specific processes in place to monitor data
- Helps stakeholders make effective decisions by digesting and extracting insights from incoming data in real time
- Highlights any issues within the network or servers on the dashboard in real time – and cuts the downtime
- Enables different teams to monitor several KPIs simultaneously without having to analyze them themselves
- Helps organizations maintain their competitive advantage by quickly identifying trends
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