Throwing more hardware and compute capacity at increasing processing times doesn’t always help.
Join us to learn how to design CloverDX graphs that actually leverage parallel processing, so you can dramatically improve performance using the resources you already have.
What you'll learn:
This session goes beyond theory and dives into practical design strategies you can apply immediately:
-
Why performance degrades — and how to fix it
Understand why sequential graph design might be your biggest bottleneck
-
How CloverDX really executes your graphs
Pipeline parallelism (streaming) and phases explained in simple terms
-
Designing for parallelism at the graph level
Leveraging branching, subgraphs and jobflows for concurrent execution
-
Scaling with data parallelism
Splitting large datasets for faster processing
Running transformations in parallel for heavy workloads
-
When parallelism backfires
Avoiding database and I/O bottlenecks
Preventing thread starvation and resource contention
Understanding CPU and memory trade-offs
Who should attend?
- CloverDX users working with growing or large-scale data pipelines
- Data engineers looking to optimize job performance
- Architects designing scalable ETL/data integration workflows
Why attend?
This is a practical session where we’ll show you how to:
- Design graphs that fully utilize the resources you already have
- Reduce processing time without increasing infrastructure costs
- Identify and eliminate hidden performance bottlenecks
Nick Taylor is a Technical Solutions Consultant at CloverDX committed to ensuring clients don't just implement the platform, but master it. Acting as both architect and mentor, Nick helps engineering teams navigate the product - transforming users into confident experts capable of driving their own long-term success.
Petr Drabek has spent over 13 years with CloverDX, championing customer success and helping customers maximize value from their data integration initiatives.
