Take a deep dive into the architectural concepts of data pipelines along with a hands-on tutorial for implementation, demonstrating the concepts in action.
The techniques we use for dealing with bad data in event streams differ from those in the batch world. Here, learn more about overcoming bad data in streaming.
Explore the differences and strengths of PolyBase and Snowflake external tables to optimize data querying strategies and achieve efficient data integration.
Cover specific characteristics related to DynamoDB migrations and strategies employed to integrate with and migrate data seamlessly to other databases.
Learn the differences between batch and real-time data processing, and explore the decision-making factors for choosing the right approach to optimize data pipelines.
Learn more about Apache Flink, a powerful stream processing tool, for building streaming data pipelines, real-time analytics, and event-driven applications.
Retrieval augmented generation (RAG) needs the right data architecture to scale efficiently. Learn how data streaming helps data and application teams innovate.
AI and LLMs streamline user story creation, optimize backlog, and predict trends, improving agile product development speed, relevance, and user engagement.
Learn how to handle schema versioning and updates in Kafka and other event streaming platforms without using schema registries through custom deserializers.
Unravel the complexities of streaming data joins in this guide covering key concepts, design, and best practices for optimal real-time data enrichment.
Discuss the strategic importance of Spark code upgrades and explore an introduction to a powerful toolkit designed to streamline this process: Scalafix.
Let's explore the features, applications, and top choices for microcontrollers in industrial IoT, achieving reliability, efficiency, and security in harsh environments.