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Master streaming data into Iceberg with advanced techniques for managing small files, optimizing compaction strategies, and building efficient real-time pipelines.
Discover how Pulsar schemas solve real-time data consistency issues, enforce governance, and enable smooth schema evolution to prevent breaking changes in streaming systems.
Explore the challenges of implementing Kafka API compatibility in streaming platforms and discover how Apache Pulsar offers architectural flexibility while maintaining ecosystem integration.
Explore advanced topic compaction techniques on Ursa, a leaderless S3-based Kafka server that balances storage costs with efficiency for scalable cloud-native streaming.
Discover how to build scalable real-time data pipelines for capital markets using SQL-based analytics to process trade events, detect anomalies, and enable high-frequency trading decisions.
Explore data serialization formats beyond JSON to optimize streaming performance, manage schemas effectively, and enhance data processing efficiency in modern systems.
Discover how Oxia transforms data streaming by serving as both metadata system and index storage engine, optimizing scalability, latency, and cost efficiency for high-throughput pipelines.
Discover how StreamNative's Ursa Engine integrates with PuppyGraph to enable real-time graph analytics on streaming data without dedicated graph databases using Gremlin and openCypher.
Discover how to simplify data streaming infrastructure management across Apache Pulsar, Kafka, and StreamNative Cloud using unified CLI and Model Context Protocol for AI integration.
Discover how StreamNative's Ursa engine reduces streaming costs by 90% while seamlessly integrating with catalogs like S3 Tables, Snowflake, and Databricks Unity for real-time lakehouse data.
Discover how to apply SRE principles to build resilient streaming AI platforms that automatically detect and remedy model drift in real-time environments.
Discover how to stream real-time data from StreamNative Cloud into Unity Catalog-backed Iceberg tables with schema validation, lineage, and security for AI workloads.
Explore how real-time vehicle data transforms into AI-driven fleet intelligence using Apache Pulsar, edge streaming, and agentic AI for autonomous decision-making.
Discover how Uber engineers build streaming ingestion pipelines using Apache Flink and Apache Hudi for real-time ML and analytics in hybrid cloud environments at scale.
Master streaming pipelines with Apache Iceberg, covering Flink, Kafka, and Spark integration for scalable data lakehouse architectures and real-time analytics.
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