CI/CD with GitHub Actions: Efficient Pipelines for Data Projects and Modern Apps

Software Development /

February 16, 2026 at 03:22 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. CI/CD (Continuous Integration and Continuous Delivery/Deployment) isn’t just a “nice-to-have” anymore-it’s the difference between shipping confidently and shipping cautiously. GitHub Actions has become one of the most practical ways to […]

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Real-Time Analytics: When It Adds Value-and When It Doesn’t

Data Analytics /

February 16, 2026 at 03:44 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Real-time analytics has become one of those “must-have” buzzwords-right up until teams discover the hidden costs: streaming infrastructure, always-on monitoring, and the operational complexity of acting on data in seconds.

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Qlik Agentic AI: From Reactive Analysis to Agent-Oriented Operational Intelligence

Artificial Intelligence /

Corporate data analysis is entering a new phase. Instead of dashboard-centric interactions, the experience is now driven by agents capable of investigating changes, connecting evidence, and suggesting actions based on governed data. This advancement gained momentum with the evolution of the Qlik Agentic AI experience. The public announcement took place at the Qlik Connect 2025

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PostgreSQL vs MongoDB vs DynamoDB: How to Choose the Right Database for Your App in 2026

Data Engineering /

February 13, 2026 at 03:52 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Choosing a database isn’t just a technical checkbox-it shapes your product’s performance, scalability, developer experience, and long-term costs. If you’re comparing PostgreSQL vs MongoDB vs DynamoDB, you’re likely building something

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How Startups Accelerate Product Development With Managed Platforms (Without Sacrificing Quality)

Business Intelligence /

February 13, 2026 at 03:49 PM | Est. read time: 11 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Speed is the startup advantage-but it can quickly become a liability if you move fast by cutting corners in infrastructure, security, or reliability. That’s where managed platforms come in. A

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Supabase: Um backend moderno com Postgres, autenticação e tempo real (sem a complexidade usual)

Software Development /

February 13, 2026 at 05:55 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Building a modern app often means stitching together a database, authentication, file storage, APIs, and realtime updates-then keeping it all secure, scalable, and maintainable. That “glue work” can slow teams

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Database Decisions That Turn Into Expensive Mistakes (and How to Avoid Them)

Data Engineering /

February 13, 2026 at 03:54 PM | Est. read time: 11 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Database choices often feel “technical” and therefore easy to postpone-or delegate without much oversight. But in practice, database decisions are business decisions. They shape performance, reliability, security, reporting, delivery speed,

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MLflow and Kubeflow: Practical MLOps for Machine Learning Teams (Without the Hype)

Data Engineering /

February 12, 2026 at 02:28 PM | Est. read time: 11 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Machine learning teams often hit the same wall: it’s not the model that’s hard-it’s everything around it. Reproducibility, experiment tracking, consistent deployments, automated retraining, monitoring, and governance quickly become the

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From Prototype to Production: Why Most AI Projects Fail-and How to Make Yours Succeed

Innovation /

February 12, 2026 at 02:37 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. AI demos can be dazzling. A prototype that classifies documents, forecasts demand, or chats like a support agent can win instant buy-in. But moving from a promising proof of concept

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TensorFlow vs PyTorch: Production-Driven Technical Differences (What Actually Matters When You Deploy)

Artificial Intelligence /

February 12, 2026 at 03:24 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. Choosing between TensorFlow and PyTorch is rarely about which framework is “better.” In real-world ML, the deciding factors are usually production constraints: deployment targets, latency requirements, hardware acceleration, monitoring, model

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