Dataflow Complexity is the New Normal
April 07, 2017

Pat Patterson
StreamSets

Share this

The process of wrangling big data is fraught with pitfalls for enterprises. Data-driven enterprises are buckling under the burden of gathering, analyzing and making actionable an incredible and growing amount of data flowing in from a variety of sources. It's not just the amount of big data that is confounding data-driven companies: The speed at which data must be collected and analyzed, and the variety of data types (think: IoT sensors, log files, web clickstreams) are overwhelming enterprise data architectures, which are increasingly defined by a complex tangle of big data sources and processing systems. Topping all this is the problem of data drift, the unexpected changes that consistently plague big data sources and result in corrupt and unusable data.

In short, the complexity of data in motion is growing and risks undermining the success of the modern data-driven enterprise. A recent survey of data engineers and architects, conducted by StreamSets, sought to bring some perspective to the new reality in the enterprise, leading to some interesting insights about the enterprise data landscape.

As we expected, use of streaming data has become quite common, with a high number of respondents — 72 percent — collecting this data for a variety of uses. Of these, two-thirds (48 percent) collect a combination of batch and streaming data, since real-time data requires context to provide intelligence. In contrast, 28 percent move batch data only, and 24 percent ingest streaming data only.

Survey results also showed that enterprises are gathering data from a range of sources: 61 percent collect from transactional databases, 53 percent from log files, 42 percent from analytics databases, 27 percent from clickstream data and 18 percent from IoT devices.

Moving on from their use of streaming data, the survey reveals that enterprises are also experiencing a sense of data urgency — that is, expeditious analysis of their incoming data sets. In fact, according to the survey, 56 percent of respondents say they require data analysis within minutes of receiving the data, and 16 percent require analysis within seconds. The world has certainly evolved from the daily or weekly business intelligence report to a live dashboard, or even analysis that drives automated actions like website personalization that can have a direct impact on a business' effectiveness in engaging with its customers. These requirements put extreme pressure on enterprise data architectures not necessarily designed to deliver consumption-ready data with this type of speed.

Our survey responses indicate that enterprises funnel their data into a range of destinations, making them much more complicated and expensive to manage than ever before. In addition, respondents keep some of their data on premises (58 percent), some in private clouds (48 percent) and some in public clouds (27 percent). The combination of diverse data stores and multiple deployment models is a new phenomenon we call data sprawl, and it is a key driver of dataflow complexity.

The challenges of increased dataflow complexity are here and now and, given the unprecedented growth of data each day, must be considered the new normal. With this information as a bird's-eye view of the state of data in motion, savvy enterprises will adopt technologies and solutions that will help them evolve with the big data landscape.

Pat Patterson is Community Champion at StreamSets.

Share this

The Latest

March 21, 2019

Achieving audit compliance within your IT ecosystem can be an iterative process, and it doesn't have to be compressed into the five days before the audit is due. Following is a four-step process I use to guide clients through the process of preparing for and successfully completing IT audits ...

March 20, 2019

Network performance issues come in all shapes and sizes, and can require vast amounts of time and resources to solve. Here are three examples of painful network performance issues you're likely to encounter this year, and how NPMD solutions can help you overcome them ...

March 19, 2019

"Scale up" versus "scale out" doesn't just apply to hardware investments, it also has an impact on product features. "Scale up" promotes buying the feature set you think you need now, then adding "feature modules" and licenses as you discover additional feature requirements are needed. Often as networks grow in size they also grow in complexity ...

March 18, 2019

Network Packet Brokers play a critical role in gaining visibility into new complex networks. They deliver the packet data and information IT and security teams need to identify problems, recognize security issues, and ensure overall network performance. However, not all Packet Brokers are created equal when it comes to scalability. Simply "scaling up" your network infrastructure at every growth point is a more complex and more expensive endeavor over time. Let's explore three ways the "scale up" approach to infrastructure growth impedes NetOps and security professionals (and the business as a whole) ...

March 15, 2019

Loyal users are the key to your service desk's success. Happy users want to use your services and they recommend your services in the organization. It takes time and effort to exceed user expectations, but doing so means keeping the promises we make to our users and being careful not to do too much without careful consideration for what's best for the organization and users ...

March 14, 2019

What's the difference between user satisfaction and user loyalty? How can you measure whether your users are satisfied and will keep buying from you? How much effort should you make to offer your users the ultimate experience? If you're a service provider, what matters in the end is whether users will keep coming back to you and will stay loyal ...

March 13, 2019

What if I said that a 95% reduction in the amount of IT noise, 99% reduction in ticket volume and 99% L1 resolution rate are not only possible, but that some of the largest, most complex enterprises in the world see these metrics in their environments every day, thanks to Artificial Intelligence (AI) and Machine Learning (ML)? Would you dismiss that as belonging to the realm of science fiction? ...

March 12, 2019
As a consumer, when you order products online, how do you expect them to get delivered? Some key requirements are: the product must arrive on time, well-packed, and ultimately must give you an easy gateway to return it if it is not as per your expectations. All this has been made possible via a single application. But what if this application doesn't function the way you want or cracks down mid-way, or probably leaks off information about you to some potential hackers? Technical uncertainty and digital chaos are the two double-edged swords dangling over this billion-dollar ecommerce market. Can Quality Assurance and Software Testing save application developers from this endless juggle? ...
March 11, 2019

Of those surveyed, 96% of organizations have a digital transformation strategy, with 57% approaching it as an enterprise-wide priority, with a clear emphasis on speed of business, costs, risk, and customer satisfaction, according to IDC’s Aligning IT Strategies and Business Expectations for Digital Transformation Success, sponsored by EasyVista ...

March 08, 2019

One of my ongoing areas of focus is analytics, AIOps, and the intersection with AI and machine learning more broadly. Within this space, sad to say, semantic confusion surrounding just what these terms mean echoes the confusions surrounding ITSM ...