Achieving Continuous, Real-Time Data Integration

by Caroline Maier

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November 15, 2021 5:43pm

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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.



MODERN DATA INTEGRATION REQUIREMENTS

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.





PRIMARY CONSTRAINTS ON IT TEAMS TRYING TO MODERNIZE DATA ARCHITECTURES

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.

  • Their IT team is too taxed get to everything needed
  • They have a shortage of deep data integration & coding skills (Python, Spark, Kafka, etc)
  • They don’t have enough time to appropriately deal with everything they should
  • There isn't enough budget for tools PLUS skilled IT and/or outside help
  • Their existing systems are complex with expensive licenses and lots of data silos/formats, etc

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.



WHAT TO LOOK FOR AS YOU MODERNIZE DATA INTEGRATION

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.

  1. Look for a solution that offers all of the core data integration use cases under one, unified platform. Combining Change Data Capture, Streaming ETL / ELT / EtLT, Batch ETL and Replication under one umbrella means easier management and monitoring, visibility into what's working and what isn't, and consolidation of tools. That translates into less cost and more time leveraging your data.
  2. Simplicity is paramount (i.e. ease of use, rapid deployment and seamless maintenance). If you are trying to consolidate, then another underlying goal is building simplicity into your data integration. Look for a solution that puts the user first with an easy to navigate and well designed UI. Power and performance don't have to live hand in hand with heavy coding and manual intervention. When designed well, the right data integration solution will do the heavy lifting for you through automations, built in frameworks and pre-configurations.
  3. Flexibility offers future-proof protection. Your data is constantly evolving, as are the sources you pull from and the targets where data lands. Don't land in the same trap of tools that have limited capability and are specific to just one use case. Look for a data integration solution that can be flexible where you need it most. On-prem, on-cloud, a hybrid approach or SaaS is a necessity as you build a multi-cloud framework, or explore moving some data to cloud but keep sensitive business data on-prem. Ensure your solution can meet your needs, and a future-proof data integration strategy.
  4. Don't sacrifice performance. A well designed solution should be able to offer best in class performance across the board that can grow with your business, not buckle under pressure. IT teams should be able to set expectations high, knowing that various business units will continue to demand data from all corners. A highly performant system can support these requests and more without a hitch.
  5. Scalability - Grow with your data. If our current moment is any indication, data volume, velocity, and formats will continue to shift and expand. With successful business growth comes growing pools of data that need to be processed in real-time. Make sure that your solution can scale as you need it to. Your data integration framework must be able to grow quickly and seamlessly with your business.
  6. Rapid Time to Value. Don't waste time waiting weeks, months or even years for your project to head to production. Find a solution that offers all of the above WITH rapid deployment. Your streamlined, modern, real-time data architecture should be a few clicks away.

Interested in learning more?

Check out our ebook, written in collaboration with Eckerson Group.

Flexible Data Pipelines: Balancing Standard and Custom Approaches

READ MORE




Achieving Continuous, Real-Time Data Integration - a blog by Equalum


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