The business climate today feels a bit like a battleground, and everyone’s feeling the pressure. A recession looms, competition is fierce, ongoing supply chain issues wreak havoc, and customer expectations are higher than they’ve ever been. Dodge left, dodge right… your business users are under pressure to keep pace, no matter where they turn.
Organizations are making a number of investments to respond to these pressures, such as moving to the cloud, implementing AI, and adopting elastic and hyper-scalable data platforms — in other words, initiatives that land squarely on your team’s already fully loaded plate. And while there’s no doubt that these enabling technologies can increase business agility, that outcome is not a foregone conclusion. All of these can present their own challenges.
For example, to get to the cloud quickly, some businesses take a lift-and-shift approach to moving to the cloud that leads to significant pipeline rework after the fact. And data platforms often require different skill sets than what organizations initially plan for. Not only can you end up with higher operating costs, but you’re also limited by infrastructure, technology, and data constraints, as well as skill and resource shortages. All of this hampers your ability to make competitive advances and can even expose you to data and compliance risks.
36% of over 650 data leaders and practitioners surveyed say their data pipelines break at least weekly — with an additional 39% admitting they break at least every 2-3 months.
There’s a way to safeguard against these challenges.
Insulate Your Data Pipelines From Unexpected Shifts
StreamSets insulates your data pipelines from unexpected shifts, so you can keep up with constant changes in data structure, semantics, and infrastructure. With StreamSets, you can:
- Introduce change without worrying about breakage. Dynamic data pipelines — decoupled, schema-light pipelines split into independent ingest, store, process, and consume layers — let you ingest more and more data without building more infrastructure, and different teams can innovate at their pace and without any repercussions to the data engineering team. You’ll operate continuously in the face of change and gain the freedom to innovate.
- Easily capture, reuse, and refine business logic. Pipeline fragments let you encapsulate expert knowledge in portable, shareable elements to be reused across multiple pipelines — without specialized knowledge — and kept up to date no matter where they are used. You’ll maximize the impact and reach of specialized skill sets and ensure consistent implementation of data best practices.
- Flexibly run your data pipelines on any cloud or on-premises environment. Platform- and infrastructure-agnostic centralized engine configuration management lets you add, remove, and upgrade compute as needed. Remove the constraints past decisions impose on you so you can make technology decisions based on what’s best for your use cases right now. You’ll take timely advantage of key capabilities — such as using GCP for compute and AWS for storage, with the ability to move to a different cloud provider in the future if it offers the same features at half the price — to maintain competitiveness and control price on an ongoing basis.
Download the ebook: How can you insulate your data pipelines from unexpected shifts?
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