
Learn a reproducible, auditable workflow for data science projects using Literate Statistical Programming principles, tools, and techniques for improved experimentation and iteration.

Explore machine learning insights with industry experts Amit Jain and Ronaldo Felipe at TMLS 2019, gaining valuable knowledge and perspectives on cutting-edge ML developments.

Explore the evolution of big data to data lakehouses, focusing on open architecture development, security, governance, and augmentation using Dremio, Apache Spark, and Apache Iceberg.

Discover the journey of a data visualization freelancer, exploring challenges, motivations, and valuable insights for success in the field.

Explore Databricks' Lakehouse Assistant: Chat in English to generate SQL queries, scripts, and code explanations while navigating the Lakehouse with ease.

Explore Power Query's internals and gain a deeper understanding of this increasingly prevalent tool in Power BI and Fabric technologies.

Discover how to transition from teaching to data analytics through self-learning, leveraging community support in the tech industry.

Stand out in Power BI job interviews with a simple yet effective trick to showcase your report creation skills, even without extensive experience.

Learn intuitive report design techniques based on how the brain processes information, enhancing data visualization and communication effectiveness.

Generate terabytes of proper test data quickly and easily using Databricks Labs Data Generator for dev environments across Azure, AWS, and GCP.

Optimize exabyte-scale data lakes with open-source caching framework. Improve performance, reduce costs, and tackle challenges in complex data environments using Hadoop, Parquet, Hudi, and Alluxio.

Explore four key technical value drivers of data lakehouses and how Apache Iceberg enhances these capabilities for data practitioners across various roles.

Explore Apache NiFi's latest features, processors, and best practices. Build efficient data flows using cutting-edge techniques, with tips and guides for optimal implementation.

Optimize travel using real-time transit data from NYC's MTA. Learn to process and analyze public data feeds with Apache tools for efficient route planning and decision-making.

Learn to deploy and scale machine learning models efficiently using Apache Beam for distributed inference on CPUs and GPUs, with practical insights on parallelizing workloads.

Explore unstructured data processing using ML, LLMs, and Apache tools. Learn to handle audio, images, and text with vector embeddings for natural language analysis in data engineering pipelines.

Explore architectural differences between Apache Flink and Kafka Streams, gaining insights into stream processing fundamentals and design choices for improved system performance.

Explore declarative reasoning with timelines for advanced event processing. Learn stream-native query abstractions and simplified time-based reasoning techniques for modern data processing challenges.

Explore batch and stream analysis using Beam's Typescript SDK. Learn API choices, usability, and functionality through demos. Discover how to leverage Typescript for efficient data processing workflows.

Explore Apache Arrow's Go implementation, covering Arrow and Parquet libraries, Flight server/client, C Data API integration, and performance optimization techniques.

Explore Zomato's journey scaling Apache Solr to handle 64 million daily searches across 100 million restaurant items in 13 languages, powering their homepage and overcoming technical challenges.