Priti Gangrade

In-memory databases are the architectural backbone of real-time AI. They’re a necessary advantage for memory-intensive workloads that rely on instant access to context. Modern systems like Redis blend durability, multi-model flexibility (JSON, vectors, time-series), and tiered storage to deliver the speed of memory with the reliability of disk. The result? Real-time analytics, instant personalization, and AI systems that actually feel intelligent. Here’s how we deliver the speed of memory with the reliability of disk: https://lnkd.in/gSm65kdB

49

Planned software maintenance shouldn’t break apps. Smart client handoffs in Redis Software and Redis Cloud keep apps online during maintenance by proactively reconnecting clients to new endpoints and temporarily relaxing timeouts until upgrades complete. The result: reliable, disruption-free maintenance for critical workloads. Read more in our blog and docs: - https://lnkd.in/gBbbNscA - https://lnkd.in/gfNwa-NK

76

🔍 Elasticsearch + LangGraph RAG LangGraph's new Retrieval Agent Template integrates with Elasticsearch to build powerful RAG applications, featuring flexible LLM options, debugging tools, and query prediction. Learn more on Elastic's blog 👉 https://lnkd.in/g4ju9pwF

775

19 Comments

From Terraform to Crossplane Kubernetes-native IaC is evolving—are you keeping up? Crossplane is redefining how we manage infrastructure: declarative, secure, and fully integrated with the K8s ecosystem. Think Terraform, but closer to your cloud-native apps. -YAML over DSL -Native RBAC & multi-tenancy -Seamless integration with Helm, Operators & more Explore how Crossplane offers the next-gen control plane—bridging DevOps and Platform Engineering like never before. #InfrastructureAsCode #Crossplane #Kubernetes #CloudNative #DevOps #PlatformEngineering #MSysTechByte #Infraunlocked #MSysTechnologies

13

What’s new in GitLab 18.7? Watch as GitLab CEO Bill Staples and Distinguished Engineer Shekhar Patnaik break down the newest updates. This release takes another important step in evolving GitLab Duo Agent Platform, bringing automation, control, and clarity to engineering teams at scale. Watch the full video to see it in action.

204

3 Comments

Traditional Kafka consumers for analytics = expensive, complex, and fragile. Replay, deduplication, schema drift… it’s a lot. On Oct 9, we’ll show how Estuary + MotherDuck simplify all of this with a fresh approach to streaming ingestion. 🔗 https://lnkd.in/dncWFDGm

8

Anyscale 🤝GKE Through our continued collaboration with the Google Kubernetes Engine (GKE) team, we’re advancing the experience to scale Ray and Kubernetes in the cloud. This work includes: Label Selectors for improved scheduling: https://lnkd.in/gUZ8iVUW New Features for AI Scaling: https://lnkd.in/gAGSMiir Enhanced TPU Support: https://lnkd.in/gsKDvwpv

65

3 Comments

From the start, Metronome knew real-time billing couldn’t be built on batch. That’s why they chose Confluent as the foundation for their data architecture, streaming billions of events daily and processing tens of thousands of invoices per second with ease. We handle the infrastructure complexity so 90% of their engineers can focus on building and improving the product. Explore how they’re delivering a modern billing experience powered by real-time data infrastructure → https://cnfl.io/40S0YDS

75

Treating an Internal Developer Platform (IDP) like a real product is the key to scaling developer experience🔑 Join Ninad Desai & Ruturaj Kadikar at #KubeCon to learn how to design IDPs that scale with your teams without slowing them down👇 https://lnkd.in/g8UprYSR #KCIN25

21

This interview breaks down the secret sauce behind how Intuit is bringing data, AI, and human intelligence to life for consumers and businesses. As Chairman and CEO Sasan Goodarzi puts it, the real value of AI is unlocked when it’s applied to solve the customer problems that really matter. That’s where we lead. Read the full interview here: https://lnkd.in/gwCArm_q

202

5 Comments

What happens when observability platforms hit their limits? In 2025, teams stopped asking "are we collecting enough data?" and started asking "can we actually use this data?" Mike Shi's year-in-review covers the shift from volume to cardinality, the rise of trace-first architectures, early wins in AI-driven SRE (shoutout to Traversal, Resolve AI, Wild Moose), and the maturation of the OpenTelemetry ecosystem with tools from Odigos and Bindplane https://lnkd.in/g7FXySvr

95

4 Comments

The most effective architectural changes are often subtractive - removing entire layers of cost and complexity. Behind the scenes, OpsHelm, Inc.'s streaming backbone was migrated from a costly MSK and unreliable NATS setup to a single diskless Kafka architecture. The results were immediate: - Annual spend dropped from >$50,000 to <$10,000. - Data loss incidents went from recurring to zero. - Manual scaling and cross-cloud networking fees were eliminated. Read the full story of their migration and 78% cost reduction: https://lnkd.in/dS6UK9-7 #disklesskafka #datastreaming #Aiven

31