Qlik Cloud and the Future of Open-Source Extensions: What’s Next for Modern Analytics Teams?

January 12, 2026 at 11:15 AM | Est. read time: 14 min By Valentina Vianna Community manager and producer of specialized marketing content Qlik Cloud has steadily evolved from “a BI tool in the cloud” into a broader analytics platform—one that supports governed self-service, automation, and embedded experiences. At the same time, the open-source ecosystem […]

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LangGraph Agent Supervision: How to Control, Log, and Monitor AI Agents in Real Time

January 12, 2026 at 11:10 AM | Est. read time: 14 min By Valentina Vianna Community manager and producer of specialized marketing content AI agents are moving from demos into production workflows—handling data analysis, customer operations, internal support, and even parts of software delivery. But once an agent starts making decisions and calling tools, a

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Building Reactive Agent Pipelines with Kafka: A Practical Guide to Event-Driven AI Automation

January 12, 2026 at 11:07 AM | Est. read time: 15 min By Valentina Vianna Community manager and producer of specialized marketing content Reactive pipelines are quickly becoming the backbone of modern automation—especially when you’re coordinating multiple AI agents that need to respond to changes in real time. Whether you’re routing user requests, reacting to

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Building an Internal MCP Server for Seamless Integration Between Data Teams

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

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Agent-to-Agent Communication with LangGraph Protocol-Based Workflows: A Practical Guide

January 09, 2026 at 02:17 PM | Est. read time: 14 min By Valentina Vianna Community manager and producer of specialized marketing content Agent-to-agent communication is quickly becoming the backbone of modern AI systems. Instead of building one “all-knowing” assistant, teams are increasingly designing multiple specialized AI agents—each responsible for a slice of the problem—and

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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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Building Multi-Cloud Infrastructure with Terraform and Automated CI/CD Pipelines (Without the Headaches)

January 08, 2026 at 12:28 PM | Est. read time: 13 min By Valentina Vianna Community manager and producer of specialized marketing content Running infrastructure across more than one cloud provider can feel like juggling flaming torches: you gain resilience and flexibility, but the complexity can spike fast. The good news is that Terraform multi-cloud

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AI Models for Classifying Logs and Events in Data Pipelines (Without Drowning in Noise)

January 08, 2026 at 11:28 AM | Est. read time: 15 min By Valentina Vianna Community manager and producer of specialized marketing content Modern data pipelines generate an overwhelming volume of logs and events—from batch jobs and streaming consumers to API gateways and orchestration tools. The challenge isn’t collecting observability data anymore. It’s making sense

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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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Integrating AI Agents with Databricks to Automate Queries (Without Losing Control)

“How did weekly churn change after the pricing update for SMB customers in EMEA?” The agent then: Interprets the intent (metrics, time range, filters, definitions) Translates that intent into SQL (or Spark SQL) Runs the query against Databricks (typically via SQL Warehouses) Returns results plus a human-readable explanation Optionally creates a chart, a dashboard snippet,

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