Real time, streaming data is king in today's modern, data driven business world. Industry leaders have harnessed the power that reliable, performant and highly sophisticated, real-time data architectures can bring. Immediate and even predictive response to customer behavior is made possible. Strategic decision making for future innovations is enabled with real-time data for analytic and BI systems. Operational efficiencies and cost reduction opportunities are quickly identified before spiraling into millions lost. More importantly, data teams are crushing it - moving quickly past labor intensive remediation and stitching of systems to strategic initiatives and testing new approaches to data integration.
For many organizations, however, the road to real time data hasn't been as smooth or fruitful. Over time, as various initiatives took precedence, data integration use cases were implemented with a slew of different tools specific to those needs. As data architecture complexity grew, so did data silos, the number of source systems and the units across the business needing access to different pools of data. As Cloud migration started gaining steam, additional pressures were being added onto the shoulders of over-taxed IT teams trying to dig a way out.
With the numerous tools that most companies have acquired over time to perform their data integration needs, data architectures are often overly complex. All of these technologies facilitate critical, necessary use cases (replication, batch, streaming ETL, streaming ELT, change data capture) but in many cases, they are stand alone solutions. A replication specific tool may not offer complex transformations. A tool providing streaming ETL might not offer comprehensive, modern Change Data Capture. You also can't forget about batch. Not every data pipeline needs to push real time data to the source, and many companies still rely on batch data processing for important business data without the real time imperatives for delivery. The unintended consequence of acquiring these tools over time is high TCO plus significant expertise required to support, install, integrate, operate and maintain.
What's worse is the pressure on teams to deliver accurate, reliable data in real time. Many existing streaming tools cannot meet throughput and latency requirements that are imperative to the business. Additionally, legacy data integration tools can struggle when connecting to more modern sources, leaving data behind as it attempts to capture changes in real-time. Data being pushed into analytic systems is stale, giving AI and BI yesterday's news and sometimes data duplicates. As IT teams scramble to try and fix the issues, they get stuck in lengthy and complex configurations, and a sea of patch and fixes.
There are a number of common constraints that prevent organizations and IT teams from optimizing their data architecture towards a real-time, streaming first approach.
There is often a lack of end to end visibility when trying to evaluate system health across multiple tools. Managing 3 separate solutions with 3 different monitoring systems and no UI is a true challenge. As data volumes grow, new company acquisitions occur and systems merge, and the velocity of data coming into the system accelerates, data silos can plague organizations without performant, real-time streaming.
When streamlining your data architecture and data integration approach towards real time, there are a few, core elements that should guide you along the way.
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