Today, most data is being generated from a wide range of sources in continuous streams, including ecommerce transactions, system monitoring data and data from sensors and IoT devices. To make effective use of this data, companies must analyze it in real time. For this reason, organizations are implementing continuous data pipelines using stream processing. This article explores what stream processing is and why it’s so valuable to modern businesses.
What is stream processing?
Stream processing is a method of data processing that enables working with continuous flows of data that lose relevance quickly and are updated frequently. Streaming data may come from servers, internal or external systems, applications, security logs and IoT sensors.
Stream processing allows organizations to gain visibility into a wide range of customer and business activities since it can handle large volumes of data from various sources at speed. It’s used for real-time data aggregation, sampling and filtering, allowing teams to access data in motion instantly and gather actionable insights or make adjustments on the fly. Stream processing is particularly helpful for detecting data patterns for temporal events, such as customer engagement, supply chain monitoring or geolocation.
Examples of stream processing
It’s nearly impossible to exaggerate the prevalence of stream processing in today’s business world. A wide array of industries are using stream processing on a daily basis. Here are just a few examples.
Fraud detection: Monitor activity in real time and identify possible fraudulent activity.
Real-time stock trades: While some platforms still process trades at the close of the day, many now facilitate real-time trading.
Marketing and sales analytics: Track customer behavior and serve up relevant offers and promotions in real time.
Inventory management: Track and manage inventory across all locations for accuracy and a better customer experience.
Healthcare: Monitor health in real time, conduct clinical risk assessments and receive alerts.
Finance: Log transactions, monitor markets and currency states.
Manufacturing: Monitor production lines in real time, conduct predictive maintenance and run risk assessments.
Stream processing vs. batch processing
In the past, the primary method of data processing was batch processing, where large data volumes were processed at fixed intervals. Before the rise of technologies that generate streaming data, batch processing worked well. And it continues to have advantages in loading extensive data sets during time windows where resource allocation has been freed up. However, there are typically long down periods between data batches, making it insufficient for use cases that require real time data.
While the traditional method of processing data sequentially ingests, processes and then structures data prior to analysis, stream processing gives you the ability to aggregate, sample, filter and analyze data as it’s being generated to consider it in real time. Data queries and processing occur simultaneously.
Stream processing is ideal for projects that require speed and nimbleness, which are common in today’s world. For example, fraud detection, supply chain monitoring, rideshare apps, and ecommerce websites all rely on stream processing.
Benefits of stream processing
Stream processing has advantages for a variety of reasons. Let’s look at three of the most significant.
1. Look at data from multiple streams at the same time
Because this method of data processing allows you to analyze real-time streaming data from various sources simultaneously, you can more easily identify causes and correlations. Using real-time data analytics with streaming data, you can quickly see the why behind trends and easily conduct what-if analyses.
2. Handle huge amounts of data but retain only what you need
With stream processing, there’s no need to store large batches of data. You can simply load the data when you’re ready to query it. And because the process is so fast, there’s no need to keep data on hand for another analysis at a later time.
3. Reduce infrastructure requirements
Stream processing simplifies your data infrastructure since the data pipeline sits on a centralized architecture. For example, the Snowflake Data Cloud serves as a robust platform where data is loaded and available for analysis almost as soon as it is created.
Snowpipe for streaming data
The Snowflake Data Cloud supports fast, efficient, at-scale queries across multiple clouds. Streaming and non-streaming data pipelines are both fully supported, and Snowflake's Streams and Tasks features enable you to build data pipelines to turn Snowflake into an agile data transformation engine.
While Snowflake’s platform supports a full range of data ingestion tools and processes, we’ve built a proprietary system called Snowpipe that allows organizations to ingest and integrate data at lightning speed. Snowpipe helps companies seamlessly load continuously generated data into Snowflake. It’s an automated service that utilizes a REST API to asynchronously listen for new data and load it into Snowflake as it arrives, whenever it arrives.
Snowpipe doesn’t require any manual effort to configure or run, and it utilizes servers separate from the customer environment to ensure workload isolation. In addition, customers only pay for the server time they use, keeping ingestion costs predictable and affordable.
Spireon: Using Snowpipe to help customers be more efficient, better protected and more profitable
Spireon, a vehicle intelligence company and a Snowflake customer, has nearly four million vehicles on its platform that gathers billions of events about its customers’ vehicle activity. The company uses this data to help customers improve their operations.Spireon’s Director of Information Architecture Dave Withers, explains, “We have a constant stream of data we need to analyze in real time, and Snowpipe enables us to do just that. Spireon is a data-driven company, and Snowpipe is part of our platform that provides valuable insights to help our customers be more efficient, better protected and more profitable.”
See what the Snowflake Data Cloud can do
The Snowflake platform makes working with streaming data quick and efficient. Spin up a Snowflake free trial to:
Explore the Snowflake UI and sample data sets
Process diverse data types with full JSON support
Instantly scale compute resources up and down to handle unexpected workloads and unique concurrency needs
Set up and test-run data pipelines and connect to leading BI tools
Experiment with programmatic access
To test-drive Snowflake, sign up for a free trial.