End-to-End Data Engineering with Apache Airflow, Docker, and Spark Clusters - Using Python, Scala, and Java
Build an end-to-end data engineering project using Apache Airflow, Docker, Spark Clusters, Scala, Python, and Java. Create and submit jobs in multiple languages to a Spark cluster for processing and real-time results.
Creating and Submitting Java Spark Jobs to Spark Clusters
Create and submit Java Spark jobs to clusters. Learn end-to-end data engineering with Airflow, Docker, Spark, Scala, Python, and Java. Build and process jobs in multiple languages for live results.
Kaoto: The Low Code and No Code Apache Camel Editor
Explore Kaoto, a visual editor for creating and deploying integration workflows using Apache Camel. Learn how it enhances productivity through Zero-ETL and accelerates development for users of all experience levels.
Fact vs. Delta Event Types in Event Streaming - Module 2
Explore properties, scenarios, and examples of Fact, Delta, and Composite event types. Learn usage recommendations and avoid pitfalls in event design for effective event streaming.
Apache Kafka 3.5 - Core, Connect, Streams, and Client Updates
Explore key updates in Apache Kafka 3.5, including KRaft migration preview, improved error handling, versioned state stores, and enhanced client-side features for better performance and flexibility.
Challenges Using Apache Kafka for Data Streaming Systems
Explore key capabilities of streaming platforms and compare self-managed Apache Kafka with Confluent Cloud's fully-managed services for efficient data streaming operations.
Robust Disaster Recovery with Kafka and Confluent Cloud - Data Streaming Systems
Explore Kafka resilience, mitigate data loss, and implement disaster recovery strategies using Confluent Cloud for robust data streaming systems.
Using GitOps as an Efficient CI/CD Pipeline for Data Streaming
Implement GitOps for efficient CI/CD in data streaming platforms, enhancing operations and traceability with Apache Kafka and Confluent Cloud.
Cloud Kafka Resiliency and Fault Tolerance for Data Streaming Systems
Manage cloud volatility, configure Kafka clients for resilience, and explore error handling in Kafka Streams. Learn idempotent operations and exactly-once processing for robust data streaming applications.
Operationalizing Pluralsight's Data with Materialize
Discover how Pluralsight leverages Materialize to replace complex streaming systems, enabling real-time analytics and applications through SQL-based materialized views for faster, cost-effective data operations.
Boosting Kafka Performance with JVM Optimization
Optimize Kafka performance by leveraging Azul Prime JVM. Reduce latency by 40%, improve SLAs, and lower infrastructure costs through efficient JVM utilization.
From Edge to Cloud: Creating Data Pipelines Using Open-source with Strimzi on Kubernetes
Learn to create data pipelines from edge to cloud using open-source Strimzi on Kubernetes, enabling real-time data streaming across your organization.
Accelerating Path to Production for Generative AI-powered Applications
Leverage large language models and real-time data to build intelligent applications with improved accuracy using Retrieval Augmented Generation. Explore AI-driven experiences and vector search capabilities.
Squeezing the Most From Your Kafka Infrastructure
Explore challenges and clever solutions for expanding Kafka infrastructure. Learn to optimize resources, streamline pipelines, and refocus on core business objectives as your system grows.
3 Flink Mistakes We Made So You Won't Have To
Discover key lessons and avoid common pitfalls when adopting Apache Flink for stream processing. Learn to optimize Kafka integration, memory configuration, and checkpointing for efficient and reliable data processing.
How to Use Streaming Joins with Apache Flink
Explore streaming joins in Apache Flink®, covering stateless, materializing, and temporal operations. Learn through demos on updating, appending, and versioned table joins for effective data combination.
Event-Driven Architecture vs Request-Response Architecture - Comparison and Tradeoffs
Explore the tradeoffs between Event-Driven and Request-Driven Architectures. Learn about reactivity, coupling, consistency, historical state, flexibility, and data access in both approaches.
Event-Driven Architectures: Fundamentals and Advanced Concepts
Explore event-driven architectures, including event immutability, windowing techniques, and practical applications in Apache Kafka and Flink ecosystems. Gain insights from industry experts in an engaging Q&A format.
Retrieval Augmented Generation (RAG) with Data Streaming for GenAI Applications
Explore Retrieval Augmented Generation (RAG) with data streaming to enhance LLM outputs. Learn how Apache Kafka and Flink enable real-time, contextualized data for reliable GenAI applications.
What is a Headless Data Architecture? - Understanding the Modular Approach to Data Management
Explore headless data architecture: decouple computation from storage, enable modular ecosystems, and streamline data processing for real-time and analytical use cases.
Understanding Data Streaming Platforms: Components and Evolution
Explore the six key components of a Data Streaming Platform, from Apache Kafka to Tableflow, and learn how they work together to provide a complete solution for unified end-to-end data streaming across your organization.