Data Forward: AI and Data Management News

Beyond Data: Why Context Intelligence Is the Missing Link in AI Success with Guy Vorster from Reltio

Abstract: In this episode of Data Forward, Ramon Chen sits down with Guy Voster to explore why AI initiatives are failing—not due to a lack of data, but a lack of context. As enterprises race to adopt AI, the conversation is shifting toward context intelligence—understanding where data comes from, what it means, and whether it can be trusted. Ramon and Guy unpack how unified, entity-centric data combined with semantics and real-time delivery creates a reliable foundation for both humans and AI agents. They also dive into: Why AI without context leads to confident—but incorrect—decisions The role of master data management and data observability in building trust How fragmented data environments create risk for AI-driven automation What it takes to move from raw data to actionable, context-rich intelligence This episode is a must-listen for data and AI leaders looking to bridge the gap between data, meaning, and action in the age of intelligent systems.

Modernizing Hadoop with VAST and Acceldata

Abstract:  On this Data Forward episode, Jason Russler of VAST Data and Ramon Chen of Acceldata discuss how enterprises can finally modernize their legacy Hadoop environments, cutting costs dramatically while positioning their data infrastructure for the AI era. Listeners will learn: How swapping HDFS for the VAST AI OS with S3A-compatible object storage eliminates 3× replication overhead and shrinks storage footprints by up to 8× — without sacrificing performance Why stable workloads like Spark ETL are moving back on-premises as cloud bills soar, and how private AI environments deliver both cost savings and data sovereignty How Acceldata's observability tooling surfaces hidden cluster inefficiencies, turning existing hardware into found money before any new infrastructure spend

Tech Series - Flow Controller: A Solution to Kubernetes Releases - Ashwin Rajeeva ft. Neeraj Nayan

What if Kubernetes releases moved like messages on a bus — immutable, traceable, and fully observable? In this episode, Ashwin Rajeeva ft. Neeraj Nayan, Senior Kubernetes experts from Acceldata, unpack the architecture behind Flow Controller — a system designed to eliminate environment drift and bring identity, immutability, and auditability to Kubernetes releases at scale. If you’ve ever heard “it worked in dev” and knew the code wasn’t the problem, this conversation will resonate. The real issue is often configuration drift, manual production fixes, broken promotion chains, and reconstructed intent. Adding more pipeline checks doesn’t solve that. Identity does. Ashwin and Neeraj break down: * Why traditional Kubernetes promotion workflows fail at scale * The concept of a Release Bus and immutable release descriptors * How UUID-based release identity removes guesswork from promotions * Designing a GitOps-native system that works with ArgoCD, not against it * Template-driven release generation to prevent copy-paste drift * Real-time promotion tracking for incident-time clarity * How to definitively answer: “Is production running exactly what we tested?” Flow Controller doesn’t replace Git or ArgoCD. Git remains the source of truth. ArgoCD remains the reconciler. What changes is the orchestration layer — adding traceability, structured promotions, and verifiable release identity across environments. This episode is for platform engineers, DevOps leaders, SREs, and Kubernetes practitioners building at scale — especially those navigating GitOps workflows, release governance, and production reliability. Because velocity without identity is just faster confusion. And when releases carry identity end-to-end, Kubernetes finally becomes predictable.

Tech Series - Founders’ Voice: AI, ADM, and Everything in Between - Ashwin Rajeeva ft. Raghu Mitra

In this first episode of the Acceldata Engineering Podcast, Acceldata founders Ashwin Rajeeva (CTO) and Raghu Mitra (Co-founder & VP Engineering) sit down to unpack how AI is transforming Agentic Data Management (ADM) and modern data engineering. They share real, first-hand insights on: 1. How AI fits into today’s data engineering and analytics stacks 2. The role of ADM in building reliable, scalable data platforms 3. Where abstraction layers help—and where they hurt 4. What founders think differently about when building data platforms with AI at the core The future of AI-driven data observability, reliability, and operations This conversation goes beyond buzzwords, diving into practical engineering decisions, architectural trade-offs, and real-world lessons from building Acceldata. Whether you’re a data engineer, platform architect, analytics leader, or founder, this episode offers a grounded perspective on AI, ADM, and the evolving data ecosystem—straight from the brains behind the platform. Watch now to hear how Acceldata’s founders think about building data platforms in the age of AI.

Build Trustworthy, Reliable Enterprise Data Systems in the Age of AI w/ Jay Sivasamy from Workday

🚀 Data Forward: Building Reliable Data Systems in the Age of AI In this episode, Joe Murphy, Principal Architect at Acceldata sits down with Jay Sivasamy, Principal, Enterprise Data & AI at Workday to explore how enterprises can build trustworthy, reliable data systems in today’s AI-driven world. They dive into: 🔹 Evolving Frameworks: From ETL scripts to AI-ready pipelines 🔹 Trust & Governance: Making reliable data a business-critical asset 🔹 AI Integration: Using AI agents to maintain and improve system reliability 🎧 Tune in and learn how to future-proof your data systems: https://linktr.ee/dataforward #DataForward #DataReliability #AI #EnterpriseData #DataGovernance #MachineLearning #DataEngineering