Data Analytics

Data Observability with Monte Carlo and Bigeye: How to Catch Data Issues Before They Hit the Business

January 16, 2026 at 12:13 PM | Est. read time: 18 min By Valentina Vianna Community manager and producer of specialized marketing content Modern analytics stacks move fast-new pipelines, new sources, new transformations, new dashboards. But the faster you ship, the easier it is for data incidents (broken dashboards, incorrect KPIs, silent pipeline failures) to

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Connecting SAP BW to Azure Synapse for Modern Analytics Reporting (A Practical Guide)

January 16, 2026 at 12:17 PM | Est. read time: 15 min By Valentina Vianna Community manager and producer of specialized marketing content Organizations that rely on SAP BW (Business Warehouse) often reach a point where traditional reporting feels limiting-especially when teams want faster exploration, broader data blending, and scalable performance for advanced analytics. Azure

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DBT in practice: how to automate data quality and data cleansing

DBT in practice addresses one of the most critical challenges faced by modern organizations: the lack of trust in management reports and analytical outputs. When data inconsistencies reach dashboards and executive reports, strategic decisions are directly affected. By embedding data quality tests and cleansing rules directly into the transformation layer, DBT enables engineering teams to

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DuckDB for Local Analytics: When It Can Replace a Data Warehouse (and When It Can’t)

January 14, 2026 at 12:12 PM | Est. read time: 13 min By Valentina Vianna Community manager and producer of specialized marketing content DuckDB makes “query-in-place” analytics on Parquet fast enough that some teams can skip-or at least delay-a traditional data warehouse. The trade-off is concurrency, governance, and organization-wide standardization. This guide helps you decide

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Custom Extensions and Connectors: Bringing Qlik, Power BI, and SAP Into One Analytics Ecosystem

January 09, 2026 at 02:12 PM | Est. read time: 14 min By Valentina Vianna Community manager and producer of specialized marketing content Most organizations don’t struggle with a lack of data—they struggle with data living in different places, using different standards, and speaking different “languages.” SAP runs critical processes, Qlik powers associative exploration for

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Snowflake vs Redshift Benchmarks for Intensive Analytics Workloads (What Actually Matters)

January 08, 2026 at 11:25 AM | Est. read time: 13 min By Valentina Vianna Community manager and producer of specialized marketing content If you’re comparing Snowflake vs Amazon Redshift for analytics-heavy workloads, it’s tempting to search for a single “winner” based on a few benchmark charts. But in real life, benchmarks can be misleading

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Monitoring Agents and Flows with Grafana and Sentry: A Practical Playbook for Real-World Observability in 2026

December 19, 2025 at 01:22 PM | Est. read time: 13 min By Valentina Vianna Community manager and producer of specialized marketing content Modern systems are powered by agents and flows—background workers, orchestration pipelines, AI agents, cron jobs, and event-driven microservices that quietly keep the business running. When these behind-the-scenes components misbehave, customer-facing systems degrade,

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Apache Superset vs Metabase: The No‑Nonsense Guide to Choosing the Right Open‑Source BI Platform in 2026

December 17, 2025 at 03:37 PM | Est. read time: 17 min By Valentina Vianna Community manager and producer of specialized marketing content Open‑source BI has moved from “nice to experiment with” to “production‑ready” for many organizations. Two names keep rising to the top: Apache Superset and Metabase. Both help teams explore data, build dashboards,

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The Convergence of Data Engineering, Data Science, and Analytics: How to Build a Unified Data Value Chain

December 16, 2025 at 02:11 PM | Est. read time: 12 min By Valentina Vianna Community manager and producer of specialized marketing content Data-driven organizations don’t win because they hire data engineers, data scientists, and analysts separately. They win when those disciplines work as one. The convergence of data engineering, data science, and analytics isn’t

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