Unify Batch and Stream Data for Real-Time Intelligence at Google Scale
Leverage unified batch and streaming analytics for real-time insights and actions, enabling competitive advantage through instant analysis of growing data volumes.
Building Next-Generation Data Pipelines with Containers - Fast Track to Modernization
Build efficient, scalable data pipelines using containers. Learn to leverage Cloud Run for streaming, batch, and on-demand data processing, enhancing your business's data management capabilities.
KubeRay: A Ray Cluster Management Solution on Kubernetes
Explore KubeRay's integration with Kubernetes, its capabilities for Ray cluster management, and performance benchmarks for simplified deployment of Ray applications.
Fabricator - Streamlining Declarative Feature Engineering at DoorDash
Découvrez comment DoorDash a développé Fabricator, un framework complet pour rationaliser l'ingénierie des caractéristiques déclaratives, orchestrant efficacement 1400 tâches quotidiennes et gérant 2,2 billions de valeurs.
Seamless Flow: Evolving From Batch to Streaming Data Flows Using DLT
Explore how 84.51° leverages Delta Live Tables to evolve data flows from batch to streaming, combining real-time and historical data for enhanced retail insights and customer-centric strategies.
High Performance Computing Workloads on Google Cloud
Explore Google Cloud's HPC solutions for critical workloads, infrastructure advancements, and customer deployments in computational sciences and AI/ML.
Assuring Data Quality at Scale
Explore strategies for implementing data quality at scale, covering challenges, metrics, and platform approaches for ensuring high-quality data in modern enterprises.
Google Dataflow - The New Open Model for Batch and Stream Processing
Explore Google Dataflow's unified batch and stream processing model through real-time demonstrations, practical insights, and visualization techniques for managing data streams.
Building ML Workflows with Java, Python & Apache Beam
Learn to create multi-language ML pipelines using Apache Beam, combining Java for data processing and Python for ML modeling in batch and streaming workflows.
Where We're Going, We Don't Need Batch Jobs
Discover how to eliminate batch jobs by modeling future events, improving system reliability and performance. Learn real-world strategies for long-running business processes.
Building and Deploying Generative AI Applications with Google Cloud Run
Discover how to leverage Cloud Run for deploying GenAI applications with seamless scaling and cost efficiency, featuring real-world examples from L'Oreal and Ford's successful implementations.
Azure OpenAI Deployment Types and Resiliency - Understanding Models, Capacity, and High Availability
Master Azure OpenAI deployment strategies, focusing on types, resiliency patterns, capacity management, and high availability considerations for optimal service implementation.
Building a Real-Time AI Data Platform with Apache Kafka
Explore building a real-time AI data platform using Apache Kafka, focusing on migrating from batch to stream processing without stateful components. Learn about Forecasty.ai's journey and innovative solutions.
Off-Label Data Mesh - A Prescription for Healthier Data
Explore data mesh application beyond analytics, using event streams for real-time and batch operations. Learn practical guidelines, overcome hurdles, and implement a minimally viable data mesh for improved data management.
The End of History? Convergence of Batch and Realtime Data Technologies
Explore hybrid approaches like Lambda and Kappa architectures for combining batch and real-time systems. Learn about SaaS platforms and managed services that simplify implementation of these powerful data technologies.
When to Move from Batch to Streaming and How to Do It Without Hiring an Entirely New Team
Demystify the transition from batch to real-time processing. Learn arguments, architectures, pitfalls, and open-source tools for stream processing without overhauling your team.
Introduction to DataFlow
Explore fully managed streaming analytics with Dataflow, minimizing latency and costs through autoscaling and batch processing.
Azure ML: Creating and Using Pipelines with Batch Endpoints
Create and deploy a multi-component pipeline with Azure ML, including component registration, data management, and Batch Endpoint setup for efficient machine learning workflows.
How to Increase the Throughput of Kubernetes Scheduler by Tens of Times
Explore advanced techniques to significantly boost Kubernetes scheduler performance, including algorithm optimization, parallel computation, and batch scheduling for improved cluster scalability and resource utilization.
Deep Dive into Delta Lake and UniForm on Databricks
Explore Delta Lake's architecture, innovations, and use cases for reliable data lake management. Learn how it enhances performance, governance, and quality across various industries.
Building a Fraud Detection Model with Feature Stores - Includes Shopify Case Study
Develop a real-time fraud detection system using ML data pipeline tools. Learn to build, deploy, and serve data pipelines with Feast and Tecton. Explore architectural patterns and model stages.