Data streaming is the ongoing transfer of data at a high rate of speed. Most data streams are continuously collecting data from thousands of data sources, and typically send large clusters of smaller sized data records simultaneously.
Batch processing has traditionally been the primary method of data processing, where large data volumes are processed at fixed intervals. While batch processing may have some advantages in loading especially large data sets during time windows where resource allocation has been freed up, there are usually long down periods between data batches.. This can impact data timeliness, especially with high-volume Web and IoT (Internet of Things) data sets.
Data streams work particularly well when the goal is to detect data patterns for temporal events, such as web engagement, eCommerce transactions, instrument telemetry, or geolocation and traffic monitoring. Streamed data is used for real-time data aggregation, sampling, and filtering, allowing analysts to access data instantly and gather actionable insights or make adjustments on the fly.
Data stream analysis provides organizations with visibility into a wide range of customer and business activity, including website behavior; employee, device, equipment, and goods geo-location; and metering or billing data. With this visibility, businesses can quickly react to changes in customer sentiment in eCommerce or address delivery, equipment, or supply chain issues in a timely manner.
Snowflake and Data Streaming
Near real-time analytics on growing data volumes can provide a key advantage to business in competitive and fast-moving industries. Seizing that advantage requires delivering data to decision makers and applications in a form suitable for easy and fast consumption. Snowflake's platform allows organizations to mobilize their data so they can attain competitive advantage. For many industries, streaming data pipelines make it much easier to efficiently transform data into the most suitable form.
Snowflake’s platform supports fast, efficient, at-scale queries across multiple clouds. Streaming and non-streaming data pipelines are a key piece of this cloud data platform. Snowflake's Streams and Tasks features enable you to build data pipelines and turn Snowflake into a nimble data transformation engine in addition to a powerful data warehouse.