The Real Modern Data Stack - Building ETL Pipelines with Open Source Tools
Discover how to build powerful, cost-effective ETL data stacks using open-source tools like sling, dbt, duckdb, and dagster - transforming months of implementation into rapid deployment.
SQL You Need as a Data Scientist
Learn essential SQL skills for data science, including SELECT, JOIN, aggregations, CTEs, and window functions. Apply these techniques to time series forecasting and fraud prediction.
How a Data Scientist Fights Fraud - With Code!
Explore data science techniques for fraud detection, including feature engineering, SQL querying, model creation, evaluation, and interpretation, with hands-on coding examples.
Real-Time Analytics and Built-In Machine Learning Models With a Single Database
Explore real-time analytics and machine learning with HTAP databases. Learn to leverage SQL for model building and discover tools for efficient data infrastructure in this hands-on webinar.
Beginner's Excel Tutorial - Data Science-Analysis and Beyond
Learn essential Excel skills for data analysis, including functions, lookups, formatting, and advanced features like pivot tables and macros. Gain practical knowledge for data-driven decision-making.
Data Analyst Setup for Productivity - Python, R, SQL
Explore a data analyst's productivity setup, including Miniconda, Python environments, VSCode, R Studio, and essential hardware recommendations for efficient data analysis workflows.
Detecting Price and Volume Patterns with SQL and TimescaleDB
Learn to detect stock price patterns using SQL and TimescaleDB. Explore PostgreSQL functions, TimescaleDB features, and analyze real-world examples to identify bullish engulfing and three-bar breakout patterns.
Learn Data Science Full Course - 2020
Comprehensive introduction to data science, covering key concepts, tools, and methodologies. Ideal for beginners seeking to understand the field's fundamentals and career prospects.
Funnel Analysis with Apache Spark and Druid for Advertising Campaign Effectiveness
Learn to perform funnel analysis at scale using Apache Spark, Druid, and DataSketches. Discover techniques for measuring campaign effectiveness and analyzing user behavior in chronological order for large-scale advertising campaigns.
Advanced SQL for Data Scientists
Master essential SQL skills for data science: statistics, data prep, advanced filtering, window functions, and analytics tool integration.
Photon: Accelerating SQL Queries on Databricks Lakehouse Platform
Explore Photon, a C++ component for efficient query execution in Databricks Lakehouse. Learn about its vectorized processing, compatibility with Apache Spark, and performance enhancements for data-driven decision-making.
Why Lakehouse Architecture Now - Exploring Enterprise Data Warehouse Failures and the Need for Lakehouse Paradigm
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.
Accelerating MLOps with Apache Spark and SingleStore
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.
DBT Using Databricks and Delta
Learn to leverage Data Build Tool (DBT) with Databricks and Delta for efficient data lake management, including SQL-first approach, upsert capabilities, and Azure integration for enhanced observability.
Fugue: Unifying Big Data Analytics Ecosystems for ETL and Machine Learning
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.
Presto on Apache Spark - A Tale of Two Computation Engines
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 Use Databricks for Data Science
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.
Deep Dive into New Features of Apache Spark 3.1
Explore Apache Spark 3.1's new features: ANSI SQL compliance, streaming enhancements, Python improvements, and performance optimizations for faster, easier, and smarter data processing.
Comprehensive View on Intervals in Apache Spark 3.2
Explore Apache Spark 3.2's interval enhancements, including ANSI SQL conformance, new interval types, and improved APIs. Learn to construct, use, and manage intervals effectively in Spark SQL and PySpark.
Large Scale Geospatial Indexing and Analysis on Apache Spark
Explore large-scale geospatial indexing and analysis using Apache Spark, covering challenges, frameworks, data structures, and practical applications in cloud-first architecture with Databricks.
Delta Lake 2.0 Overview - New Features and Community Collaborations
Overview of Delta Lake 2.0 features, integrations, and community collaboration. Highlights include data skipping, Change Data Feed, column mapping, multi-cluster S3 writes, and support for various languages and platforms.