Streaming data pipelines enable processing data in various formats from disparate sources and are capable of handling events of different speed. It can range from processing millions of events every second to processing and delivering data in hours. These pipelines dismantle data silos by seamlessly streaming data between different environments, facilitating the continuous flow of real-time data. From cybersecurity to ecommerce to public services, nearly every industry relies on streaming data in one way or another. But what does “streaming” and “real-time” actually mean? In this article, we’ll delve into why streaming data pipelines are so desirable, and we’ll highlight a variety of ways they’re being used today.
What is streaming?
The term “streaming” is often defined to only include sub-second data applications. However, many use cases do not require sub-second latency but are still better thought of as streaming. For example, having an updated view of retail inventory every 10 minutes is a streaming use case. A better approach is to focus on business needs to define whether stream or batch processing is the best choice.
Why build modern streaming data pipelines
Streaming data pipelines perform a series of actions on a continual basis, including ingesting raw data as it's being generated, cleaning it, standardizing it and making it available for a variety of target destinations. It’s important to note that streaming ingestion is not meant to replace file-based ingestion but rather to augment it for data-loading scenarios where it makes sense. Additionally, because data generated in low latency is more valuable when paired with historical data that provides context, it’s invaluable to have a single infrastructure for batch data and streaming data. Here’s what to consider when building a streaming data pipeline.
Access to a wider variety of data
Batch processing is fine for many use cases. However, certain business processes require more up-to-date data. For example, stock exchanges rely on streaming data processing for spotting market anomalies, including insider trading and stock price manipulation. In other situations, businesses benefit from acting on information quickly, such as a manufacturer that receives a reading from a production line sensor indicating a machine is producing products that do meet quality control guidelines.
Agile pipeline development and testing
By removing the need to manually build and maintain individual data pipelines, organizations can focus on innovation and growth, not maintenance. With less cumbersome infrastructure to manage, business leaders can give their full attention to maximizing the value of their data, using it to respond quickly to changing conditions, boost efficiency, reduce risk and improve decision-making. Modern streaming data pipelines are also simpler to modify when accommodating new data sources than their traditional counterparts. Testing is also less complex since streaming data pipelines are serverless and cloud-based.
Rapid scalability
Powered by a modern cloud data platform such as Snowflake, streaming data pipelines can automatically scale to handle high volumes of data. With their ability to move data from multiple sources to multiple destinations in real time, streaming data pipelines are incredibly flexible, enabling organizations to seamlessly scale their deployment both horizontally and vertically.
Built-in fault tolerance
With a continuous flow of data from multiple sources, built-in fault tolerance protects against data loss and the opportunity costs associated with losing real-time visibility. Powering your streaming data pipelines with a solution such as Snowflake means fault tolerance is baked in across a comprehensive set of failure scenarios, ensuring your data and operational capabilities remain secure.
Streaming data use cases
It would be difficult to point to an industry where instantaneous access to data hasn't sparked innovation and growth. Here are four examples that illustrate the transformative impact of streaming data pipelines.
Cybersecurity
Streaming data enables cybersecurity teams to shift from a reactive stance to a proactive one. Detecting and responding to threats as they emerge allows teams to react quickly, minimizing damage and containing threats. For instance, anomaly detection, which is crucial in network security. SIEM providers feed security log data into the system as it is created and the system evaluates whether the observed pattern deviates significantly from expected normal behavior.
Ecommerce
Personalization engines rely on streaming data pipelines to continuously monitor and analyze users’ online behavior, including clicks, searches, page views and interactions with content or products. With this information, ecommerce companies can deliver tailored content, product recommendations and experiences to individual users.
Streaming data pipelines also allow retailers to optimize inventory levels, striking an ideal balance to prevent overstocks and stockouts. Ingesting data from sensors installed in warehouses and distribution centers, point-of-sale systems and demand-sensing sources, streaming data pipelines deliver the information retailers need to more accurately predict demand for specific products.
Supply chain
IoT devices embedded in cargo containers, vehicles and warehouses provide real-time data that manufacturers can use to assess the health of their supply chains. Delivered via streaming data pipelines, this information allows organizations to track the location and status of raw materials and finished goods. Predictive analytics models can also use this and other data to forecast potential disruptions, delays or bottlenecks in the supply chain, helping manufacturers proactively address supply chain issues before they escalate.
IoT
Most types of IoT solutions generate real-time data to be ingested into streaming pipelines. For example, smart cities rely on IoT solutions. Air quality sensors, security cameras, traffic sensors and other remote monitoring systems help municipalities manage traffic, optimize waste collection, monitor air quality and improve public safety.
Streaming data from IoT devices also plays an essential role in energy management. Organizations can use streaming data from energy meters, HVAC systems and lighting controls to optimize energy consumption in buildings, manufacturing plants and industrial facilities. Using this data-driven approach to energy management, businesses can realize substantial cost savings while reducing their environmental impact.
Snowpipe Streaming and Dynamic Tables: A better way to build a streaming data pipeline
Snowflake is changing what’s possible with streaming data pipelines, delivering innovations that help organizations make the most of their real-time and historical data. With Snowpipe Streaming, data engineering teams no longer have to manage separate infrastructure for batch data and streaming data. Now in public preview, Snowpipe Streaming enables low-latency streaming data pipelines to support writing data rows directly into Snowflake from business applications, IoT devices or event sources such as Apache Kafka, including topics coming from managed services such as Amazon Kinesis.
Dynamic Tables is a new table type that drastically simplifies continuous data pipelines for transforming both batch and streaming data. With Dynamic Tables (in public preview), you can use SQL or Python to declaratively define data transformations. Snowflake will manage the dependencies and automatically materialize results based on your freshness targets. Dynamic Tables only operate on data that has changed since the last refresh to make high data volumes and complex pipelines simpler and more cost-efficient. As business needs change, you can easily adapt by making a batch pipeline into a streaming pipeline with a single latency parameter change.
Learn how to build declarative streaming pipelines with Dynamic Tables in our webinar.