#databricks (25)
- Build a Real-time Supply Chain Platform with Databricks Lakehouse
Develop a real-time supply chain intelligence platform using Databricks Lakehouse to gain visibility, predict delays, and analyze tariff impacts.
- Setting Up Your Databricks Lakehouse Environment
Learn how to set up a secure and scalable Databricks Lakehouse environment for real-time supply chain analytics.
- Ingesting Raw Supply Chain Events with DLT Bronze Layer
Learn how to ingest raw supply chain events into a Databricks Delta Live Tables Bronze layer using Apache Kafka.
- HS Code-based Tariff Impact Analysis with DLT
Learn how to build a real-time tariff impact analysis pipeline using Databricks Delta Live Tables.
- Streaming Logistics Cost Monitoring with Spark Structured Streaming
Learn how to build a real-time logistics cost monitoring pipeline using Apache Spark Structured Streaming on Databricks.
- Building the Customs Trade Data Lakehouse & HS Code Validation
Learn how to build a robust Data Lakehouse for customs trade data analysis using Databricks Delta Live Tables and HS code validation.
- Anomaly Detection for Trade Data and Logistics Costs
Learn how to build robust anomaly detection systems for trade data and logistics costs using Databricks, PySpark, and MLflow.
- CI/CD for Databricks Pipelines with Databricks Asset Bundles
Learn how to set up a CI/CD pipeline for Databricks using Asset Bundles and GitHub Actions.
- Securing Your Lakehouse with Databricks Unity Catalog
Learn how to secure your Databricks Lakehouse using Unity Catalog with fine-grained access control and auditing.
- Production Deployment, Monitoring, and Cost Optimization
Learn how to deploy, monitor, and optimize a real-time supply chain analytics platform on Databricks.
- Databricks: From Zero to Production-Ready Solutions
Learn to build robust, scalable data solutions using Databricks from zero to production-ready.
- Advanced Data Manipulation with Spark SQL
Learn advanced data manipulation techniques with Spark SQL, including window functions and Delta tables.
- Advanced Architectural Patterns and Best Practices
Learn advanced architectural patterns and best practices for building robust data solutions on Databricks.
- Data Ingestion: Loading Data into Databricks
Learn how to load data into Databricks using various methods, including CSV, JSON, and Parquet files.
- Data Transformation with PySpark DataFrames
Learn how to use PySpark DataFrames for data cleaning, enrichment, filtering, and aggregation in Databricks.
- Getting Started with Your Databricks Workspace
Learn how to set up and use Databricks for data engineering, machine learning, and analytics.
- Mastering Delta Lake Fundamentals
Learn how to master Delta Lake fundamentals, including ACID transactions and schema enforcement.
- Introduction to Apache Spark on Databricks
Learn to use Apache Spark on Databricks for large-scale data processing and analysis.
- Machine Learning Lifecycle Management with MLflow
Learn how to manage the entire machine learning lifecycle with MLflow, from tracking experiments to deploying models.
- Performance Optimization: Queries and Clusters
Learn how to optimize Databricks queries and clusters for faster performance and cost efficiency.
- Monitoring, Cost Management, and Production Readiness
Learn how to monitor, manage costs, and prepare your Databricks solutions for production.
- Building an End-to-End ETL Pipeline Project
Learn how to build a complete ETL pipeline using Databricks, PySpark, and Delta Lake for data processing.
- Real-time Data with Structured Streaming
Learn how to process real-time data using Apache Spark's Structured Streaming API on Databricks.
- Understanding Databricks Clusters and Compute
Learn how to configure and manage Databricks clusters for efficient data processing.
- Data Governance and Security with Unity Catalog
Learn how to use Unity Catalog for centralized data governance and security on Databricks.