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Dataflow Complexity is the New Normal

Pat Patterson

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.

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Dataflow Complexity is the New Normal

Pat Patterson

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.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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