
Explore the shift from enterprise data warehouses to lakehouse architecture, focusing on benefits, challenges, and Starburst's role in powering SQL-based analytics on Delta Lake.

Explore building end-to-end Delta pipelines on GCP, covering data reliability, performance, and the Bronze-Silver-Gold architecture pattern, with practical code examples and Big Query integration.

Explore how Salesforce built an engagement activity platform using Delta Lake to support Einstein Analytics and Sales Cloud Einstein, covering data ingestion, incremental reads, and mutation handling.

Learn to accelerate MLOps using Apache Spark and SingleStore. Discover how to build fast analytical applications, manage supervised learning models, and scale AI/ML operations for data science success.

Learn to build a robust streaming ETL pipeline that handles dynamically changing schemas and new event types without downtime, using Delta Lake and Databricks for real-time data lake implementation.

Explore cost-efficient strategies for Azure Databricks, including resource management, tier selection, and best practices for optimizing Apache Spark workloads in a managed cloud environment.

Learn to migrate Airflow-based Apache Spark jobs to Kubernetes, reducing costs and improving stability. Explore challenges, best practices, and native Airflow integration for seamless transition from AWS EMR to K8s.

Learn to deploy and optimize Apache Spark on Kubernetes with hands-on demos, covering environment setup, application sizing, performance tuning, and monitoring for efficient data pipeline management.

Explore Delta Lake merge optimization techniques, including inner and full outer joins, partition pruning, and file pruning. Learn tips for handling large merges efficiently.

Learn how to implement stateful streaming with Spark, focusing on updating decision logic at runtime for real-time offer personalization in loyalty programs like PAYBACK.

Efficient data encryption solution for privacy compliance using Apache Spark and Delta Lake. Automates consumer deletion requests, enhances security, and maintains data integrity while ensuring accessibility when needed.

Unifying big data ecosystems with Fugue: SQL-like framework for ETL and ML pipelines, compatible with Spark, TensorFlow, and more. Simplifies development, improves performance, and enhances maintainability.

Explore the integration of Presto and Spark, combining low-latency evaluation with robust execution for enhanced SQL experience in both interactive and batch use cases at Facebook scale.

Learn to productionize data pipelines using Apache Airflow, bridging data science and engineering. Explore collaborative workflows, custom operators, and scalable solutions for efficient model deployment and iteration.

Zillow's data engineering team shares their journey in redesigning data pipelines using Apache Spark, focusing on scalability, maintainability, and robustness while reducing code complexity.

Explore integrating Spark 3 with VMware Pacific for scalable, reliable, and high-performance big data analytics deployments using hypervisor-native Kubernetes on vSphere 7.

Explore data pipeline observability using OpenLineage, enhancing reliability and auditability. Learn how metadata collection enables understanding of data flow and dependencies across teams and technologies.

Techniques to create configurable, maintainable data pipelines. Learn to externalize configurations, validate inputs, and leverage Scala features for robust, easily deployable ETL processes.

Discover how Databricks simplifies data science with a unified platform for analytics workloads, from data preparation to predictive analytics, enhancing collaboration and productivity at scale.

Explore lightweight data validation for Spark using Fugue and Pandera, enabling partition-specific rules and efficient big data processing without compromising on functionality.

Explore Delight, a free monitoring dashboard for Apache Spark. Learn to troubleshoot and optimize data engineering pipelines using system metrics and Spark information for improved performance and cost-effectiveness.