Systems | Development | Analytics | API | Testing

AI is reshaping software testing, but real quality does not come from hype alone. This session explores where AI truly adds value in automation and where human testers remain essential. It focuses on building a practical partnership between AI and testers, where speed and scale are balanced with judgment, context, and responsibility.

Following your migration assessment, it is time to execute the transfer of your data and SQL queries into Google Cloud. This video dives into the specific tools and services that simplify migrating workloads from Snowflake, Teradata, Cloudera, and Databricks into BigQuery, Dataproc, and Google Cloud Storage.

Learn more: speedscale.com.

Learn more: speedscale.com.

Professional services speed up onboarding by helping teams get maximum value fast: through setting clear expectations, calibrating the tool, and working side-by-side so users learn by doing. This builds confidence and leaves teams fully empowered, which is valuable because they can achieve meaningful impact quickly without relying on long-term vendor dependence. — Mush Honda, Chief Quality Architect at Katalon.

As software systems grow more complex and business cycles demand faster releases, traditional approaches to quality engineering are no longer enough. AI brings a new dimension to the Software Development Life Cycle—augmenting decision-making, predicting risks, automating quality checks, and continuously assuring value delivery. Armed with GenAI and Agents we can reshape how we think about software quality. This discussion explores practical ways to embed AI across the SDLC to accelerate delivery, reduce risk, and achieve a step-change in quality outcomes.

Healthcare organizations can’t achieve true Patient 360 or power accurate AI and analytics when identity data is fragmented across EHRs, EMRs, CRMs, and countless clinical and operational systems. Check out the full video to see how Verato MDM Cloud delivers industry-leading healthcare identity resolution and master data management (MDM) directly inside Snowflake to unify, enrich, and master patient and provider data with unmatched accuracy.

What if you could ask ChatGPT "which customers haven't ordered in 90 days?" and get an instant answer from your actual database? I set this up live in under 5 minutes. No code. No SQL. Just questions and answers.

Stop manually juggling mismatched data formats! This video demonstrates how to join Parquet and JSON files directly within ThoughtSpot Analyst Studio’s Python Notebook to create a single, enriched dataset. What you will see: This is a must-watch for data professionals looking to unify complex, multi-format data sources and deliver searchable, AI-ready insights in one continuous workflow.

Retrieval-Augmented Generation can sound convincing while still being wrong. This session focuses on moving beyond surface-level metrics and turning stochastic AI outputs into evidence-backed, verifiable results. It explores how to test the entire RAG pipeline, from ingestion and indexing to retrieval, grounding, and answerability, ensuring every response is traceable to the right source, policy, and user context.