Data Analytics

The Evolution of Data Platforms: From Lakehouses to the Era of AI Agents

The evolution of data platforms is currently at a historic turning point, where Data Engineering and Artificial Intelligence are no longer silos but move forward hand in hand. While the previous focus was on transitioning from Data Warehouses to Data Lakehouses to support BI and Data Science, today’s market demands architectures capable of supporting Generative […]

The Evolution of Data Platforms: From Lakehouses to the Era of AI Agents Read More »

Snowflake Internals Explained: How Storage, Compute, and Scaling Really Work (and How to Use Them Better)

January 22, 2026 at 12:08 PM | Est. read time: 14 min By Valentina Vianna Community manager and producer of specialized marketing content Snowflake is often summarized as “separation of storage and compute.” True-but the real value shows up in the details: how Snowflake stores data in micro-partitions, how virtual warehouses execute queries, and how

Snowflake Internals Explained: How Storage, Compute, and Scaling Really Work (and How to Use Them Better) Read More »

The Future of SQL in a Distributed Data World: Why the “Old” Language Still Powers Modern Analytics

February 20, 2026 at 04:19 PM | Est. read time: 10 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. SQL has been declared “dead” more times than most technologies get updated. And yet, in a world of distributed data, real-time analytics, cloud warehouses, lakehouses, and streaming platforms, SQL remains

The Future of SQL in a Distributed Data World: Why the “Old” Language Still Powers Modern Analytics Read More »

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

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.

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

Microsoft Fabric, Explained: Architecture, Key Benefits, and Common Adoption Challenges (Plus How to Overcome Them)

February 06, 2026 at 02:42 PM | Est. read time: 18 min By Valentina Vianna Community manager and producer of specialized marketing content Microsoft Fabric is Microsoft’s end-to-end analytics platform designed to bring data engineering, data integration, data science, real-time analytics, and business intelligence into a single, cohesive experience. Instead of stitching together multiple products,

Microsoft Fabric, Explained: Architecture, Key Benefits, and Common Adoption Challenges (Plus How to Overcome Them) Read More »

Beyond Pretty Charts: Why Dashboards Often Fail to Drive Real Decisions (and How to Fix It)

February 06, 2026 at 01:21 PM | Est. read time: 13 min By Valentina Vianna Community manager and producer of specialized marketing content Dashboards are everywhere-BI tools, product analytics, sales reporting, ops command centers. Yet many teams share the same frustration: the dashboard exists, the numbers update, but decisions don’t change. If you’ve ever heard,

Beyond Pretty Charts: Why Dashboards Often Fail to Drive Real Decisions (and How to Fix It) Read More »

Why Data Quality Matters More Than Data Volume (and How to Get It Right)

February 05, 2026 at 02:56 PM | Est. read time: 11 min By Laura Chicovis IR by training, curious by nature. World and technology enthusiast. It’s tempting to believe that more data automatically means better decisions. After all, we live in a world of analytics dashboards, customer data platforms, IoT streams, and AI models that

Why Data Quality Matters More Than Data Volume (and How to Get It Right) Read More »

Great Expectations for Data Quality: How to Build Trust From Your First Pipeline

= 0) Date bounds (e.g., created_at Notes: > – This example uses a Pandas DataFrame batch for readability (great for first implementations and many warehouse extracts). > – In production, you can point GX at your warehouse/lake (e.g., via SQLAlchemy, Spark) using the same expectation concepts. 1) Define an Expectation Suite (GX 1.0-style example) `python

Great Expectations for Data Quality: How to Build Trust From Your First Pipeline Read More »

How to Build a Data Roadmap Aligned With Business Strategy (A Practical, Step-by-Step Guide)

February 03, 2026 at 02:23 PM | Est. read time: 12 min By Valentina Vianna Community manager and producer of specialized marketing content A data roadmap is only “successful” if it moves the business forward-not if it simply lists tools to buy, dashboards to build, or platforms to migrate. When organizations treat a data roadmap

How to Build a Data Roadmap Aligned With Business Strategy (A Practical, Step-by-Step Guide) Read More »

Azure Synapse Migration Strategies Ahead of the Move to Microsoft Fabric

January 29, 2026 at 04:23 PM | Est. read time: 14 min By Valentina Vianna Community manager and producer of specialized marketing content Microsoft Fabric is becoming a central part of Microsoft’s analytics direction-bringing data engineering, data science, real-time analytics, and BI into a more unified experience. For teams running workloads in Azure Synapse Analytics,

Azure Synapse Migration Strategies Ahead of the Move to Microsoft Fabric Read More »