Data is central to modern business and society. Depending on what sort of leaky analogy you prefer, data can be the new oil, gold, or even electricity. Of course, even the biggest data sets are worthless, and might even be a liability, if they aren’t organized properly. Data collected from every corner of modern society
Noa ShavitBlog
Today, balancing performance requirements with cost efficiency is a critical challenge for both data engineers and executives. A leading advertising analytics platform faced this significant challenge. With strict customer-facing runtime Service Level Agreements (SLAs) to meet efficiently, the company needed a solution that could optimize their data infrastructure for both performance and compute costs. This
Noa Shavit
Blog, Case Study
A comprehensive comparison for modern data processing The exponential growth of data in recent years has revolutionized the way businesses operate and make decisions. From small start-ups to large enterprises, organizations across industries are leveraging data to gain competitive advantages, optimize operations, and drive innovation. However, with this data surge comes the challenge of efficiently
Noa ShavitBlog
In the complex ecosystem of big data and cloud computing, understanding your return on investment (ROI) isn’t just a nice-to-have—it’s a critical business imperative. Enter Gradient by Sync, an AI compute optimization engine that transforms data processing expenses into quantifiable savings and decodes ROI with unprecedented precision. The Gradient advantage: Precision engineering for cloud economics
Kartik NagappaBlog, Releases
Today, data engineers face considerable challenges optimizing the performance of their data pipelines while managing ever rising cloud infrastructure costs. On a recent episode of the Unapologetically technical podcast, Sync Co-founder and CEO, Jeff Chou, shed light on the innovative solutions that are reshaping how we approach these challenges. Read on for the key insights,
Noa ShavitBlog, Videos
Many data engineers and platform managers at companies want to understand “How are my pipelines doing from a cost and performance perspective?” It turns out this very benign and simple question is riddled in complexity. Fundamentally there are so many things that can change from run to run, that even determining if it’s the same
Jeffrey Chou
Blog, Case Study
