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Learn to manage massive IoT device networks using Kafka and MQTT for real-time data ingestion, analysis, and control in applications like vehicle communication and predictive maintenance.
Explore the architecture and design of a real-time analytics platform, featuring sub-second latency, cloud-native services, and CDC streams for efficient data processing and customer insights.
Explore event-driven architecture for managing long-term customer data, addressing data quality issues, and evolving critical data structures in Social Security Administration systems.
Explore Flink's connector ecosystem, learn about source and sink interfaces, and discover how to integrate with various data sources. Gain insights into contributing to Flink's development.
Explore Pinterest's evolution in real-time event processing using Apache Flink, improving data quality and efficiency while saving millions in infrastructure costs and boosting user engagement.
Explore the evolution of real-time machine learning techniques, addressing low latency, dynamic data adaptability, and resource efficiency challenges in modern ML systems.
Develop a real-time threat detection system using Kafka Streams. Learn to handle dynamic rule modifications, implement external data feeds, and apply best practices for stream processing.
Explore Apache Flink's core principles for real-time data processing, including shared-nothing architecture, event-time processing, and state snapshots, through practical examples and demos.
Explore data modernization with Confluent and AWS. Learn migration strategies, cloud capabilities, and real-time streaming potential. Gain insights from success stories and best practices for unlocking data value.
Explore the evolution from batch to streaming data pipelines using Apache Flink and Kafka. Learn strategies for seamless transition, including query-based connectors, change data capture, and handling late events.
Explore tradeoffs between workflow engines and event streaming brokers for microservices communication. Compare Apache Kafka and Temporal, examining their strengths and implementation challenges in various use cases.
Discover 10 essential tips for enhancing data discovery and governance in your organization, from establishing a common data language to implementing data contracts and fostering collaboration.
Explore Apache Pinot's real-time analytics capabilities for user-facing applications, leveraging streaming data from Apache Kafka to deliver rapid insights across diverse business domains.
Explore a unified architecture for stream processing, combining fast data storage to enhance simplicity, performance, and cost-efficiency in real-time data platforms.
Explore a real-time analytics architecture using RocksDB to isolate streaming data ingestion from query serving. Learn about compute-storage separation and memtable replication for efficient data processing.
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