Part 2 of a 5-part series on Performance Benchmarking and its use in DW Analytics Engineering and Maintenance activities.
In this installment, I look at the use of some simple analytics and visualizations that can inform a range of DW platform development and implementation choices, and can drive more performant and effective deployments.
Part 1 of a 5-part series on Performance Benchmarking and its use in DW Analytics Engineering and Maintenance activities.
While formal database benchmarks with Full Disclosure Reports have fallen out of favor, vendors and practitioners alike are finding new and creative uses for benchmarking, including the old stalwart, TPC-DS. This is especially true in understanding how the new wave of DW/Analytics solutions — Cloud Data Warehouses, Spark and SQL-on-Hadoop, and GPU-accelerated SQL engines — augment traditional on-prem Data Warehouse appliances.
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nnouncing a 5-part series on Performance Benchmarking and its use in DW Analytics Engineering and Maintenance activities.
Benchmarks have long been used by vendors to highlight their products’ performance or price-performance advantages, and by enterprise developers and procurement teams to select an appropriate solution for their needs.
But Performance Benchmarking can be used for so much more …
Well, maybe not so much a leap as a shove.
After 3 years at Yellowbrick Data in a variety of System Engineering and Performance Benchmarking roles, I’m back to the consulting lifestyle. Sad to be moving on after a 5+ year relationship that began with my technical due diligence and Seed/A-round investment recommendation while at Samsung, but I take with me a bunch of new “chops”, perspectives, and most importantly a set of relationships with a number of colleagues who have already been extremely supportive as I start a difficult transition.
So I’m very much looking forward to what lies ahead.
Moving forward, I’m planning to continue my work in scale out computing and storage architectures applied to “big and fast” data applications — scalable databases, analytics, and machine learning.
I’ll do an initial blog series on Performance Benchmarking in Data Warehousing Analytics environments, sharing some of the insights I’ve developed over the last 5 years of performance engineering work. I’m hoping to redirect some of my skills to big compute / big data challenges in life sciences, possibly in conjunction with COVID research. I spent a number of years working on genomics and proteomics “big data” applications with pioneers Affymetrix, Perlegen Sciences, Celera Genomics, and PE Biosciences, and am looking forward to returning to that sphere.
I also hope to refresh my teaching skills, and to re-engage in STEM mentoring with underprivileged youth.
(And yes, that’s my son again in that photo).
