The Emergence of Dataflow Chaos

The rise of Big Data sources, multi-cloud architectures and real-time applications have created multiple dimensions of growing complexity when it comes to data in motion.


It has become impossible for IT departments to keep pace with this ever-changing web of data movement.  If they react to each new problem ad hoc, they further fuel the chaos.  When dataflow chaos takes hold, confidence in the timeliness and trustworthiness of data erodes and the promise of a data-driven future is put in jeopardy.

StreamSets conquers dataflow chaos.

StreamSets has designed the industry’s first data operations platform.

Three factors are driving this immense increase in complexity for data in motion;
data sprawl, data drift and data urgency.

Data Sprawl

First, the well-structured model of transactional databases feeding a data warehouse is giving way to “data sprawl” based on big data processing systems assembled from a complex web of open source and commercial products, each with its own upgrade cadence and interdependencies. These systems are deployed on-premises or in private or public clouds.  Businesses expect to run their analysis wherever they get the best price/performance for a given use case.

Data Drift

Second, “data drift” has become a fact of modern life.  Wild data flows in from new digital systems, from IoT sensors to web clickstreams to log files, which are often controlled by others with precious little formal notification of changes. Unexpected alteration of the schema and semantics that occur across these sources undermines the quality of incoming data and wreak havoc with applications that rely on that data. 

Data Urgency

Third, similar to when FedEx revolutionized expectations for delivery in the physical world, streaming analytics platforms have created a new sense of“data urgency”. In the same way that overnight or even same day delivery is the new normal, business users expect that data can be put to use within minutes or even seconds of it having been spawned at the source.  While this opens up enormous opportunities such as real-time personalization or fraud detection where minutes do matter.

Together, data sprawl, data drift and data urgency create a nightmare for managing data movement across the enterprise, with the complexity in each layer combining to create dataflow chaos.

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