Designing a Data-Intensive Future - Expert Talk
Explore data landscape evolution, local-first collaboration, and career insights with experts Martin Kleppmann and Jesse Anderson. Gain valuable perspectives on data engineering and software development trends.
Auto Feature Engineering - Rapid Feature Harvesting Using DFS & Data Engineering Techniques
Explore rapid feature harvesting for machine learning using Deep Feature Synthesis and data engineering techniques to accelerate model development and shorten time-to-market.
Unveiling Much Simplified Functional Programming in Scala for Data Engineering
Simplified functional programming in Scala for data engineering, focusing on feature generation abstraction and optimization techniques without complex libraries or boilerplate code.
Garbage in Garbage Out - Generating Useful Log Data in Complex Environments
Explore effective log data generation in complex environments, focusing on meaningful data, thresholds, and log management systems for improved security and analysis.
Stable 34 - Waking Up the Data Engineer in You
Explore threat intelligence, attack techniques, and data analysis for enhanced cybersecurity. Learn from an expert about remote services, OSEM, and effective data modeling strategies.
MLOps - What's the Deal Behind It?
Explore MLOps, its industry impact, and challenges in real-world AI systems. Learn key strategies for success, including open-source adoption and engineering best practices.
Best Practices When Accessing Big Data or Any Other Data
Explore best practices for efficient data access on local and shared file systems, backed by real-world examples. Learn to improve performance, reliability, scalability, and portability in data-intensive applications.
Keeping Your NodeJS Promise in Production
Explore Node.js event loop architecture, asynchronous tasks, and production system challenges with live code samples for file I/O and data pipelines.
Bridging Data Science and Data Infrastructure with Apache Airflow
Explore how Apache Airflow bridges data science and infrastructure, enabling collaboration between teams and facilitating the development of flexible, observable, and resilient data pipelines.
Dask-SQL - Empowering Pythonistas for Scalable End-to-End Data Engineering
Explore dask-sql for Python-based SQL data extraction from enterprise datalakes, enabling seamless integration with PyData tools for machine learning, analytics, and data transformation workflows.
Too Big for DAG Factories?
Learn to scale data workflows using Apache Airflow, covering dynamic DAG generation, automation, and best practices for managing big data pipelines. Suitable for beginners and intermediate developers.
Developing on Google Cloud with Python and DataFrames
Explore Google Cloud's enhanced Python experience for building applications, pipelines, and analytics, focusing on BigQuery integration and DataFrame support for impactful business solutions.
Getting Started with Polars
Explore Polars, a fast data manipulation library, through hands-on exercises with real-world datasets. Learn key operations, compare with Pandas, and optimize data pipelines.
PL-Python - Python Inside the PostgreSQL RDBMS
Explore Python integration within PostgreSQL, enabling powerful database programming and advanced data manipulation capabilities for enhanced RDBMS functionality.
Designing NRT - Near Real Time Stream Processing Systems - Using Python with Storm and Kafka
Learn to build near-real-time stream processing systems using Python, Storm, and Kafka for handling and computing large volumes of data as it arrives, with a focus on creating efficient pipelines.
Hyperconvergence Meets BigData
Explore deploying Wendelin, a Big Data & Machine Learning platform, using SlapOS, a hyperconverged OS. Learn to create and manage a complete Big Data infrastructure with Python-based tools.
Simple ETL in Python 3.5+ with Bonobo
Learn to create simple, efficient data pipelines using Bonobo, a Python 3.5+ ETL framework. Explore writing processors, running jobs, and engineering testable systems for data transformation.
Feeding Data to AWS Redshift with Airflow
Explore Airflow for efficient data pipelines and learn to integrate it with AWS Redshift for effective ETL processes, schema management, and data transformation techniques.
I Don't Like Mondays - What I Learned About Data Engineering After 2 Years on Call
Insights and best practices for data engineering on-call duties, including minimizing emergencies, effective problem-solving, and implementing robust monitoring systems for improved reliability.
Using Bonobo, Airflow and Grafana to Visualize Your Business
Hands-on introduction to building business dashboards using Bonobo ETL, Airflow, and Grafana. Learn to create data pipelines, visualize metrics, and improve business insights through practical examples and real-world data.
Industrial Machine Learning Pipelines with Python & Airflow
Learn to build scalable, distributed machine learning data pipelines using Airflow with Celery backend. Gain insights on industry challenges, comparisons with other technologies, and practical knowledge for implementation.