Case Study Data Engineering Scalability 
TechCrunch
MARCH 7, 2023
Executives should, of course, have in mind a clear idea of the problem they want to solve as well as a business case. But the AI core team should include at least three personas, all of which will be equally important for the success of the project: data scientist, data engineer and domain expert.
Mobilunity
MAY 3, 2024
When it comes to financial technology, data engineers are the most important architects. As fintech continues to change the way standard financial services are done, the data engineer’s job becomes more and more important in shaping the future of the industry.
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Netflix Tech
MARCH 5, 2019
While our engineering teams have and continue to build solutions to lighten this cognitive load (better guardrails, improved tooling, …), data and its derived products are critical elements to understanding, optimizing and abstracting our infrastructure. Give us a holler if you are interested in a thought exchange.
Kentik
MARCH 7, 2023
Tenets of network observability A detailed explanation of network observability itself is out of the scope of this article, but I want to focus on its core tenets before exploring a couple of brief case studies. Network observability, when properly implemented, enables operators to: Ingest telemetry from every part of the network.
Kentik
OCTOBER 25, 2016
How Scalable Architecture Boosts Accuracy in Detection. This scalable, adaptive approach to monitoring and anomaly detection has been field-proven to be far more accurate than legacy approaches. For more detail, read our PenTeleData case study. Deep analytics.
d2iq
FEBRUARY 19, 2021
Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.
Perficient
APRIL 1, 2024
Case Study A private equity organization wants to have a close eye on equity stocks it has invested in for their clients. They want to generate trends, predictions (using ML), and analyze data based on algorithms developed by their portfolio management team in collaboration with data scientists written in Python.
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