UPDATED 00:00 EST / JUNE 13 2018

BIG DATA

Stream computing transforms businesses into digital dynamos

Digital businesses succeed by achieving greater real-time intimacy with their customers across every touchpoint and channel. And nothing delivers that intimacy — plus speedier business insights and faster business results — quite like stream computing.

In the 21st century, stream computing is becoming the foundation for transformation of all customer-facing and back-end business processes. Streaming is as fundamental to today’s always-on economy as relational data architectures were to the prior era of enterprise computing.

At the heart of this revolution are advances in real-time event processing, continuous computing, in-memory data persistence and change data capture. When deployed within an enterprise’s cloud computing infrastructure, these technologies drive a continual feed of real-time data updates, contextual insights, optimized experiences and fast results into all business processes.

Over the coming decade, data-at-rest architectures — such as data warehouses, data lakes and transactional data stores — will become less central to enterprise data strategies. In Wikibon’s recent big data analytics market update, we uncovered several trends that point toward a new era in which stream computing is the foundation of most data architectures:

  • Media and entertainment is a key vertical market for stream computing, relying on back-end cloud infrastructure that supports real-time packaging, loading, processing and artificial intelligence-driven personalization of content delivery.
  • Stream computing is the foundation of many new edge applications, including access by mobile, embedded and “internet of things” devices, with back-end infrastructure providing real-time device management and in-stream analytic processing.
  • Enterprises are expanding their investments in in-memory, continuous computing, change data capture and other low-latency solutions while converging those investments with their big data at-rest environments, including Hadoop, NoSQL and RDBMSs.
  • Streaming environments are evolving to support low-latency, application-level processing of live data in any volume, variety, frequency, format, payload, order or pattern.
  • Stream computing backbones are being deployed to manage more stateful, transactional workloads, execute in-stream machine learning and handle other complex orchestrated scenarios that have heretofore been the province of relational databases and other at-rest repositories.
  • Online transactional analytic processing, data transformation, data governance and machine learning are increasingly moving toward low-latency, stateful streaming backbones.
  • Vendors are introducing innovative solutions that incorporate streaming platforms ensuring they can serve as a durable source of truth for diverse applications.
  • Cloud providers have integrated streaming technologies into the heart of their solution portfolios for mobility, IoT, serverless computing and other key solution patterns.
  • Enterprises are migrating more inferencing, training and other workloads toward edge devices that process real-time streams of locally acquired sensor data.
  • Open-source streaming environments such as Kafka, Flink, and Spark Structured Streaming are becoming important enterprise big-data platforms.
  • Batch-oriented big data deployments are giving way to more completely real-time, streaming, low-latency end-to-end environments.

For a further discussion of these trends, please register here for the webcast “Digital Business Transformation In the Streaming Era.” On Thursday, June 28, at 1 p.m. EDT, I’ll be joined by Clive Bearman of Attunity Ltd. and Mike Boyarski of MemSQL Inc. in a lively session in which we will provide guidance for enterprise data professionals looking to migrate their legacy architectures to support all-streaming architectures for complex cloud and edge applications.

Image: 44833/Pixabay

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