What is data streaming?

In the past, data streaming and real-time insights were reserved for pretty specific businesses and online activities. For example, complex financial data streams from the stock markets have long been used by traders and analysts to make instant decisions.

These days, however, data streaming has become a core component of how we understand online activity and how we access business analytics. Understanding data streaming and its applications can ensure that your business operates efficiently and remains competitive.

Data stream definition

A data stream is an uninterrupted flow of data ordered by time. Typically, businesses will be dealing with data streams from many different sources. Data streaming is the process of transmitting and processing that data, usually to extract important insights into business activities.

When it comes to data streaming, your processing software is usually noting changes to the stream in order to derive those insights. Any event that indicates a deviation from the normal stream of data may represent useful information for the business to know.

The main benefit of data streaming is the ability to analyze it in real time, thereby allowing the business to make quick decisions.

Examples of streaming data

Data streaming has many applications across a vast amount of scenarios. One of the most common business applications is real-time insights. Continuous data streams allow many different departments in the business to maintain a constant overview of important changes to their data. On the consumer side, the most common application is media streaming. Data streams enable users to view media files such as videos without having to download large files onto their devices.

Online activity is another common source of data streams, including activity logs or online financial transactions. Consumers might recognize health monitors, such as their Apple Watch, as a source of data streaming that constantly provides real-time health information via their apps and on the device itself.

Benefits of data streaming

The timely analysis of data streams has many benefits to the overall business and to individual units within the organization.

Agile business response

Data streaming enables your systems to flag risks and events that require as timely a response as possible from the business. Having real-time insights like this helps organizations, and individual decision-makers within it, to avoid, minimize, and manage crisis events.

Storage saving

Unlike batch processing, which requires data to be stored in a warehouse and leveraged for insights after the fact, streaming enables users to view data in real time without downloading large files.

This is facilitated by the instantaneous analysis of data as it flows through the processor, allowing your systems to decide which data is worth storing and which is not.

Instant and continuous business insights

Lots of data streaming today is taken for granted. We’ve become accustomed to accessing real-time insights across many business systems. When you’re dealing with highly dynamic streams of data with a lot of variables, data streaming enables executives to keep a finger on the pulse of business performance and risk at all times.

The result of all this is an organization that is continuously provided with actionable insights to help improve the company’s overall performance.

Data stream components

There are three core steps to setting up and operating data streaming architecture. Large cloud service providers, like Amazon and Google, provide many of the components needed.

1. Data processors

Data must first be captured and processed before it can be fed back to users as real-time insights. So the first component of data streaming architecture is a processor that can capture the data from devices and/or applications. Again, providers such as Amazon, Google, and many more provide tools to help you build this component.

2. Data analysis

Once you are capturing and processing the data, your system needs to be able to analyze the stream continuously. This type of analysis requires tools that can query the data streams in real time for notable events and changes.

The second step in the analysis part of your architecture is the ability to quickly feed these insights into dashboards that users can easily interpret and analyze in order to take action.

3. Data storage

The final high-level component of data streaming architecture is storage. Data streaming processors analyze your data in real time to decide what is important enough to store and what can be discarded.

While it’s still going to be a large amount of data, cloud storage services are cost effective and provide a much cheaper method of storage than traditional data warehouses.

Stream data from your data warehouse with Cloud Connect

With much of your data locked away in a data warehouse, you need a way to make that data actionable for your business. That’s why we invented Lytics Cloud Connect, a reverse ETL solution that allows you to easily stream specific data segments from your data warehouse to all of your downstream tools. Using simple queries, you can organize and export data by specific buyer behaviors or criteria into your CDP.

(For more on Cloud Connect watch our explainer video below or read our introductory blog).

Get real-time decision-making capabilities with Decision Engine

Data streaming can provide invaluable insights into the behavior, preferences, and activities of your organization’s customers and target audiences. With Decision Engine, Lytics’ customer data platform, you can gain real-time insights that enable your marketing teams to create personalized digital experiences. Watch a demo of Lytics to see for yourself.

If you need help gathering real-time insights from your customer data, feel free to contact Lytics.

And if you want to see Cloud Connect in action, try it free and test your first segments today.