Systems | Development | Analytics | API | Testing

Build vs Buy Streaming for Real-Time RAG: 2026 Guide

Moving a retrieval-augmented generation (RAG) prototype from a Python notebook into production isn't an API orchestration challenge. It's a distributed systems problem. For engineering managers and data platform leads, the build-versus-buy decision on streaming infrastructure will dictate your artificial intelligence (AI) feature velocity for the next three to five years. This guide assumes you've already prototyped a RAG pipeline.

Qlik Live Stream Friday: Choose Your Champion 2026

Join Ouadie Limouni and Mike Tarallo on this week's Qlik Live Stream Friday for a look at Choose Your Champion 2026, an interactive World Cup prediction experience powered by. Ouadie will walk through the application, highlighting how machine learning, interactive analytics, and conversational were combined to create a unique fan experience for World Cup 2026 predictions.

The Numbers You Can't Trust: Why multi-entity finance has a data problem - and what CFOs are doing about it.

The board asks a question. You know the answer, roughly. But "roughly" is not what you say in a board meeting. So you confirm later. Three days later, the board has moved on. This is not a knowledge problem. It is a data infrastructure problem. This whitepaper is about that problem, and the CFOs who fixed it without replacing a single ERP.

Put Your CRM Pipeline Data to Work: Announcing the Integrate.io SugarCRM Source Connector

Pull accounts, contacts, opportunities, and custom module data from SugarCRM into your warehouse, BI tools, or downstream pipelines, fully transformed, on schedule, with no manual exports required. SugarCRM is a CRM platform built for mid-market and enterprise sales, marketing, and service teams.

How ThoughtSpot Fixed This CIO's Biggest Headache

The secret to a seamless customer experience? Embedding your intelligence. Ligentia wanted one consistent, branded experience across their entire supply chain offer. The fix? Partnering with ThoughtSpot. Catch Ligentia CIO Boris R. and Cindi Howson on podcast discussing how to turn standard apps into data powerhouses. Music: “The Clermont” by Flash Fluharty Licensed via PremiumBeat, ID: P9IHFMDYNZCKLEFZ.

Architectural Decision Guide: When to Use Apache Kafka (And When You Shouldn't)

Your team just shipped a microservices refactor. Services are smaller, deployments are faster, and boundaries are clearer. Then, during a design review, someone inevitably suggests: “We should use Kafka.”That suggestion might be the exact architectural breakthrough you need—or it could quietly introduce months of unnecessary operational complexity.This article serves as a practical decision framework.

Agentic Workflow for Petabyte-Scale Data Analytics | Cloudera Agent Studio

Struggling to get clear, reproducible insights from petabytes of data? Join Charu Anchlia, Principal Engineer II at Cloudera, to see how Cloudera Agent Studio brings business users and tech analysts together under one simple interface. See how multi-agent orchestration—using specialized SQL and coding agents—can solve complex data analysis challenges, generate real-time visualizations, and seamlessly transform LLM outputs into repeatable Airflow pipelines.

Build Your Super Team: What 150 Years of Soccer Data Says

Soccer is a game of stories, but the most fascinating stories are often buried deep inside the numbers. And this year on the world's biggest stage, the tournament has expanded by nearly 60% – traditional scouting reports and pundit hot-takes simply can't keep up with the sheer volume of new data. That’s why we’re looking at the tournament through a much wider lens.

Gallus Insights: From Dashboard Overload to Instant Answers

I had the distinct pleasure of hosting a Snowflake Summit ‘26 session with Agustin “Augie” Del Rio, CEO and Founder of Gallus Insights, an analytics platform tailored specifically for mortgage lenders. As we sat down to discuss the future of analytics, one core truth echoed throughout the room: the most ambitious AI goals live or die by the quality of the underlying data.

The Optimization Paradox

Even if you can see exactly what is wrong with your data platform. Why is none of it getting fixed? A 30-minute conversation on closing the gap between what your dashboards see and what your team can actually get done, across Databricks, Snowflake, and BigQuery. Why it matters Most data teams are not short on insight anymore. The dashboards are full. Cost reports flag cost overruns. Observability platforms catch infrastructure misallocations. New AI assistants will even draft query rewrites for you.