Data Science

February 03, 2026 at 01:33 PM | Est. read time: 11 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Modern data and AI products don’t fail because the model is bad-they fail because pipelines are brittle, dependencies are unclear, retries are inconsistent, and nobody trusts the outputs. That’s why […]

February 03, 2026 at 02:18 PM | Est. read time: 11 min By Valentina Vianna Community manager and producer of specialized marketing content Modern analytics teams are expected to deliver trusted, “always-on” data-fast. But speed without consistency creates a familiar mess: conflicting definitions, undocumented logic, broken dashboards, and a growing lack of confidence in reporting.

January 29, 2026 at 04:59 PM | Est. read time: 12 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. The data and analytics world isn’t just evolving-it’s reorganizing itself. In the last few years, we’ve watched traditional reporting teams turn into modern data platforms, data scientists expand into AI

January 29, 2026 at 01:51 PM | Est. read time: 12 min By Valentina Vianna Community manager and producer of specialized marketing content Building a data-driven company isn’t just about “hiring data people.” It’s about hiring the right kind of data expertise for the outcomes you need-reliable pipelines, trustworthy metrics, predictive insights, or all of

January 29, 2026 at 01:18 PM | Est. read time: 12 min By Valentina Vianna Community manager and producer of specialized marketing content Amazon Redshift has been a go-to cloud data warehouse for years, especially for teams already invested in AWS. But by 2026, the data stack is more crowded (and more capable) than ever:

January 22, 2026 at 10:31 AM | Est. read time: 15 min By Valentina Vianna Community manager and producer of specialized marketing content Modern analytics and machine learning live or die by data quality. A single upstream schema change, a silent null explosion, or a “helpful” ETL tweak can break dashboards, degrade model performance, and

January 20, 2026 at 01:00 PM | Est. read time: 16 min Data science can feel like a buzzword-filled universe-models, pipelines, MLOps, dashboards, A/B tests, and “AI transformations.” In practice, successful data science is much simpler (and more disciplined) than it sounds: it’s the process of turning messy, real-world data into decisions, products, and measurable

January 20, 2026 at 12:49 PM | Est. read time: 18 min By Valentina Vianna Community manager and producer of specialized marketing content Data is no longer a back-office concern. It’s how modern companies decide faster, serve customers better, and run with fewer costly handoffs. The organizations pulling ahead aren’t just collecting more data-they’re building

January 15, 2026 at 03:15 PM | Est. read time: 16 min By Valentina Vianna Community manager and producer of specialized marketing content When most developers hear “Redis,” they immediately think cache-and for good reason. Redis is famously fast, simple to operate, and perfect for reducing database load. But stopping there leaves a lot of

January 09, 2026 at 02:15 PM | Est. read time: 14 min By Valentina Vianna Community manager and producer of specialized marketing content When data teams scale, “integration” becomes less about moving data from A to B—and more about coordinating people, permissions, tooling, and standards across analytics, engineering, data science, and operations. That’s where an