Word of the Day: streaming data architecture

Word of the Day Daily updates on the latest technology terms | October 26, 2018 streaming data architecture A streaming data architecture is an information technology framework that puts the focus on processing data in motion and treats extract-transform-load (ETL) batch processing as just one more event in a continuous stream of events. This type of architecture has three basic components -- an aggregator that gathers event streams and batch files from a variety of data sources, a broker that makes data available for consumption and an analytics engine that analyzes the data, correlates values and blends streams together. The system that receives and sends data streams and also executes the application and real-time analytics logic is called the stream processor. Because a streaming data architecture supports the concept of event sourcing, it reduces the need for developers to create and maintain shared databases. Instead, all changes to an application's state are stored as a sequence of event-driven processing (ESP) triggers that can be reconstructed or queried when necessary. Upon receiving an event, the stream processor reacts in real- or near real-time and triggers an action, such as remembering the event for future reference. The growing popularity of streaming data architectures reflects a shift in the development of services and products from a monolithic architecture to a decentralized one built with microservices. This type of architecture is usually more flexible and scalable than a classic database-centric application architecture because it co-locates data processing with storage to lower application response times (latency) and improve throughput. Another advantage of using a streaming data architecture is that it factors the time an event occurs into account, which makes it easier for an application's state and processing to be partitioned and distributed across many instances. Streaming data architectures enable developers to develop applications that use both bound and unbound data in new ways. For example, Alibaba's search infrastructure team uses a streaming data architecture powered by Apache Flink to update product detail and inventory information in real-time. Netflix also uses Flink to support its recommendation engines and ING, the global bank based in The Netherlands, uses the architecture to prevent identity theft and provide better fraud protection. Other platforms that can accommodate both stream and batch processing include Apache Spark, Apache Storm, Google Cloud Dataflow and AWS Kinesis. Quote of the Day "The rise of streaming data architectures is connected with a larger change that is happening: the enterprise is becoming more real time. With streaming data architectures, data can be processed on the fly and no longer needs to be collected in a data store so that queries can be run against it." - Kostas Tzoumas Trending Terms extract, transform, load batch real-time analytics Apache Flink event-driven architecture Apache Spark event-stream processing Learning Center Big data platform broadens place in analytics architecture Structured data and streaming analytics are broadening the role of big data platform technologies if the 2018 Strata Data Conference in New York is any indication. This podcast sorts through the signs for users looking to add big data systems to their analytics architectures. Information architecture applied to big data streaming, AI Data management expert William McKnight looks at big data streaming, AI and GDPR in an interview. While these issues challenge data professionals, a look at their basic composition can provide a guide to their future status as part of the enterprise information architecture. Big data tooling rolls with the changing seas of analytics On the eve of the Strata conference in New York, big data tooling continues to morph. This news story tracks some recent product activity of noted Hadoop vendors, uncovering the paths they're taking from alternative data warehousing to full-fledged big data analytics systems. Streaming data analytics puts real-time pressure on project teams Streaming data analytics systems give companies useful information in real time, but a plethora of technology options complicates efforts to build them. 5 trends driving the big data evolution Big data evolution stems from factors like the convergence of structured and unstructured data platforms, practical machine learning and cheaper compute resources that have brought big data use into the mainstream. It's time to pay attention to big data trends and put the technologies to use. Quiz Yourself Although big data is getting bigger all the time, much of the data being collected ___ useless. a. is b. are Answer Stay in Touch For feedback about any of our definitions or to suggest a new definition, please contact me at: mrouse@techtarget.com Visit the Word of the Day Archives and catch up on what you've missed! FOLLOW US About This E-Newsletter This e-newsletter is published by the TechTarget network. To unsubscribe from Whatis.com, click here. Please note, this will not affect any other subscriptions you have signed up for. TechTarget, Whatis, 275 Grove Street, Newton, MA 02466. Contact: webmaster@techtarget.com Copyright 2018 TechTarget. All rights reserved.