Wondering what data streaming and data streaming technologies can do for your business? In short: They can enable your team to execute smarter, more agile data-driven decision-making.

That’s a competitive advantage because although enterprises are collecting more data than ever—2.02 petabytes on average, up from 1 petabyte in 2020—only 57% of what’s being collected ends up being acted upon. With the remaining 43% going unleveraged, that unused data translates to missed opportunities to connect with customers, meet their needs, and drive additional lifetime value.

The fastest way to act on customer insights is by leveraging data streaming technologies that enable real-time analysis and action.

In this guide to all things data streaming technologies, we’ll explore:

• What is data streaming?

• What are data streaming technologies?

• Why are data streaming and data streaming technologies important?

• Batch processing vs. stream processing

• Data streaming technologies use cases

• 3 examples of real brands leveraging data streaming technology

• What to look for when considering data streaming technologies

• Data streaming FAQs

• Featured resources

Let’s get started.

What Is Data Streaming?

Data streaming processes data in real time as soon as it gets generated. This is a more modern approach to processing data that speeds up the time to insight and action for businesses, allowing organizations to improve and scale analysis and decision-making. While many technologies might say they’re real time, only technologies based on data streaming are able to actually make that vision a reality.

What Are Data Streaming Technologies?

Data streaming is made possible by data streaming technologies that are capable of processing data from across sources in the moment. More sluggish systems rely on a slower method of processing data—called batch processing—which tackles data in batches, after waiting until a certain amount of time has passed or data has been generated before doing something with the information.

Data streaming technologies allow companies to unlock a single, unified customer view by gathering and processing all of the touchpoints and interactions a given customer has had with the brand—from their app session activity to the emails they’ve opened to the items they’ve purchased in person and online—in real time. By streaming this information along the various layers of the MarTech stack, brands can make more agile and data-driven decisions, improve customer engagement, and drive stronger business results.

Why Are Data Streaming and Data Streaming Technologies Important?

Data streaming technologies help brands more effectively analyze and act on their customer data from across sources in the moment, evolve their marketing strategies, and:

• Gain a better understanding of their customers

• Make more informed marketing decisions

• Create truly personalized marketing campaigns

• Send more relevant customer engagement outreach based on the latest customer behaviors and preferences, even as these change in the moment

• Drive deeper customer engagement

• Improve outcomes across conversions and retention

Batch Processing vs. Stream Processing: What’s The Difference?

What is batch processing?

As the name suggests, batch processing is a type of data processing that handles the information in batches. This is done by bundling data together at set windows of time, such as every 24 hours, or when a certain threshold of data has been accumulated. As a result, this type of data processing is inherently not in the moment and has latency built into how it operates.

What is stream processing?

Stream processing, AKA data streaming or unbounded data, is handled in the moment—that is, in real time without any delays. Each unit of data is processed on an ongoing basis, enabling a live view of the various interactions that a customer has with a given brand.

Advantages of stream processing vs. batch processing

For marketers, stream processing delivers the greatest value compared to batch processing. Acting on and drawing insights from first-party data is most effective when that data is fresh and accurate up to the moment.

Due to the lag time involved in handling data, the major downside of batch processing is that it makes it challenging for brands to achieve an up-to-date picture of any individual customer’s activities, interests, and behaviors at any moment in time. After all, customers can continue engaging, browsing, shopping—you name it—between bundles of batch-processed data, making the information marketers have at their fingertips potentially obsolete.

Data Streaming Technologies Use Cases

1. Acting on customer behavior and preferences in the moment with real-time marketing personalization

Sending a post-visit message asking patients to rate their experience is a great way for providers to strengthen customer engagement, but only if the patient actually makes it to the appointment. If they’ve canceled, sending this kind of follow-up looks out of touch. Instead, a notification encouraging the patient to reschedule at their convenience would likely be more appropriate, depending on their most recent action.

The greater the time delay between when data is generated and when it’s available to be used for marketing purposes, the greater the chances of negative consequences for customer engagement efforts. That’s particularly true when customers take actions, such as completing a conversion or moving from one segment to another, between when the data is generated and when it is processed and put into action. Data streaming technologies like Braze help brands avoid data latency issues and avoid sending dated campaigns powered by inaccurate or incomplete data.

2. Democratizing access to your company’s data

Offering seamless connections across the various layers of any organization’s MarTech ecosystem, Braze enables team members across departments and regions to access the data they need on the platforms they use at any moment in time.

3. Unlocking instant data analysis

Not only is accessing large-scale data sets in real-time easier than ever, so is making sense of it all. The right data streaming technologies offer up-to-the-minute analytics and reporting to understand the impact of marketing strategies across platforms and channels. For example, the Braze Currents high-volume export feature can automatically pass insights, such as customer behavior events and messaging engagement events, from our platform to brands’ analytics platforms to support large-scale data analysis to optimize campaign performance and customer outcomes.

3 Examples of Real Brands Leveraging Data Streaming Technologies

1. REA Group Harnesses Data Streaming to Save Time and Maximize Marketing Agility

REA Group, the global online real estate advertising company based in Melbourne, Australia, has been able to eliminate two hours of production time in connection with the creation of every single campaign. They’ve made that happen by using Braze to implement personalization without having to rely on the data team for support and instead leveraging data streaming directly through their APIs.

Before selecting Braze for their customer engagement efforts, their old system required batching data—a process that was overly complicated due to the company’s growing send volume and personalization efforts. Data integration failures could delay campaign sends by an entire day, which in turn meant the content could potentially become dated. By using the Braze Currents high-volume data export feature along with Braze Alloys technology partner Tealium, REA Group has delivered impressive results:

• 94% increase in home loan leads

• 200% increase in property tracks

• 1,000 additional agent inquiries per week

2. Payomatic Uses Braze + Snowflake to Power Real-Time Personalization

Payomatic, New York’s largest provider of check cashing and financial services, leveraged Braze and Braze Alloys technology partner Snowflake to unlock a cloud-based 360-degree customer view. By ensuring their data was actionable, they were able to reach customers at the right stage in the customer journey with personalized marketing campaigns that have delivered stronger results, including a:

• 50% increase in prepaid cardholder mobile app penetration

• 32% uplift in direct deposit via the app

• 11% increase in mobile app engagement

3. Virgin Red Leverages Data Streaming for Timely, Data-Driven Personalized Marketing Campaigns

Before partnering with Braze, the marketing team at Virgin’s rewards club, Virgin Red, relied on disconnected customer engagement tools that created time-consuming data management and analysis challenges. To streamline operations and ensure GDPR compliance, Virgin Red turned to Braze Cloud Data Ingestion (CDI) to get the data they needed for their campaigns, without constant reliance on engineering resources. Now ingesting member attributes (location, points balance, product favorites, etc.) can be set up in a few clicks.

What to Look for When Considering Data Streaming Technologies

1. Integration Capabilities

By investing in advanced data streaming technologies that enable your systems to communicate with each other, you’ll unlock the flexibility to learn, grow, and iterate while reducing the burden of maintaining your tech stack.

Braze integrates with a variety of partners, including data warehouses like Amazon Redshift and Snowflake, BI tools like Looker, analytics platforms like Amplitude, data lakehouses like Databricks, and customer data platforms (CDPs) like mParticle and Segment.

2. Real-Time Streaming

The phrase “real-time data” gets thrown around a lot, but many platforms use it to refer to a type of data processing that doesn’t actually happen in the moment, but instead happens every few hours or even just once per day. True real-time streaming, however, means your brand can unlock granular data about your customers—such as how customers are engaging, when, and why—right as each interaction takes place, powering analysis, optimization, and iteration for campaigns and your company’s overall customer engagement strategy.

3. Two-Way Synchronization

Brands need support for direct connections between essential layers of the modern marketing tech stack. Data that comes in and out needs to be kept up to date across systems, making it possible to manage strategies and processes across tools and grant everyone on the team access to the information they need via the platforms they use.

4. Support for Both Speed and Scale

As important as it is to be able to process data in the moment without delays to stay in lockstep with customer interests and behavior, that speed isn’t enough on its own: Your tech stack must also be able to handle massive scale.

At Braze, our systems have been built to support truly epic scale—we supported more than 31 billion messages during the 2022 Black Friday and Cyber Monday period, maintaining 100% uptime and hitting peaks of up to 18.5 million sends per minute. In fiscal year 2023, we sent 2.2 trillion messages.

Final Thoughts

First-party data is incredibly valuable for driving marketing personalization and enhancing the customer experience. However, the value of these types of data is highest the moment they are generated. A customer looking to place a food order has a limited window of time they’re looking to do so. A guest who leaves feedback about a negative experience at a hotel likely is only willing to be wooed back within a short period of time before considering alternative brands. For data that’s not static—such as interests, behaviors, and contact information—the accuracy (and power) of it diminishes over time, which is why the faster brands can get, analyze, and act on data, the more effective their efforts will be.

Data Streaming FAQs

What’s unbounded data?

Unbounded data is another way to refer to data streaming, or the process of handling each unit of data individually as it comes in, enabling ongoing, real-time data processing across systems.

What’s microbatching?

This is a type of data processing that lands somewhere in between traditional batch processing and data streaming, with the data being processed in a series of small batches at a more frequent interval than batch processing.

What is data latency?

Data latency is the time delay that happens between when data gets created and when it’s available to be used.

Featured Data Streaming Resources

For more thought leadership, tips, and tricks related to data streaming and customer engagement, check out our resources:

• What Effective Data Integration and Activation Looks Like

• Is It Really Marketing Automation Without Streaming Data?

• Power Seamless Data Transfers with Braze Currents

• How Deliveroo Uses Braze Currents to Power Personalized, Consumer Experiences