Deep Dive into GPU Support in Apache Spark 3.x - Accelerator-Aware Scheduling and RAPIDS Plugin
Explore GPU acceleration in Apache Spark 3.x, covering task scheduling, columnar processing, and the RAPIDS plugin for enhanced ETL, ML, and DL performance without code changes.
Building a Real-Time Feature Store for Machine Learning at iFood
Discover how iFood built a real-time feature store using Databricks and Spark Structured Streaming to process event streams, store data, and develop reliable code for efficient machine learning model predictions.
Using Apache Spark and Differential Privacy for 2020 Census Data Protection
Explore how Apache Spark and differential privacy protect 2020 Census data, balancing accuracy and confidentiality. Learn about the innovative TopDown Algorithm and custom monitoring systems.
Building a Streaming Microservice Architecture with Apache Spark Structured Streaming
Explore building a scalable, fault-tolerant streaming microservice architecture using Apache Spark, Kafka, gRPC, and Delta Lake for real-time data processing and reliable insights.
Productionizing Machine Learning with Apache Spark, MLflow and ONNX - Cloud Deployment Using SQL Server
Explore productionizing machine learning using Apache Spark, MLflow, and ONNX with SQL Server. Learn model management, lifecycle orchestration, and leveraging SQL Server for ML model storage and deployment.
Building the Petcare Data Platform with Delta Lake and Spark ETL Pipeline
Explore Mars Petcare's data platform using Delta Lake and Spark ETL pipeline 'Kyte'. Learn about advantages over Azure Data Factory and leveraging Delta Lake for ETL configurations and data science.
Faster Data Integration Pipeline Execution Using Spark-Jobserver
Optimize data integration pipelines using Spark-Jobserver for faster execution, context reuse, and efficient resource management. Learn configuration, security, and API integration for improved performance.
Running Apache Spark Jobs Using Kubernetes
Learn to run Apache Spark jobs on Kubernetes, unifying analytics and data science on a cloud-native architecture. Discover how this approach simplifies infrastructure, enables on-demand deployment, and eliminates big data cluster overhead.
Time Series Analysis Visualizations with Matplotlib, Seaborn, and Plotly
Explore time series visualization techniques using Matplotlib, Seaborn, and Plotly. Learn to style charts and analyze trends, seasonality, and moving averages in data science applications.
Three Profiles of OAuth2 for Identity and Access Management - 2016
Explore OAuth2 profiles for identity and access management, covering OpenID Connect, UMA, and Google's approach. Learn strategies for implementing OAuth2 in enterprise security infrastructure.
Securing the Spark Fire Hose - 2016
Learn to secure Apache Spark in big data analytics. Explore code and configuration security, data protection strategies, and integration with common technologies to safeguard critical information throughout its lifecycle.
How to Invest with Data Science - Technical Analysis and Portfolio Optimization
Learn data science techniques for investing, covering stock analysis, portfolio optimization, and advanced indicators to make informed investment decisions.
Testing Stock Trading Strategies with Ichimoku Analysis
Learn to test stock trading strategies using Python, focusing on Ichimoku analysis with real-time data, customizable charts, and performance evaluation techniques.
Finding the Best Investments Using Technical Analysis
Learn technical analysis techniques to identify top-performing securities across sectors, create diversified portfolios, and use the Ichimoku Cloud for momentum trading. Includes hands-on Python coding for data analysis and visualization.
Getting Started with Technical Analysis 2 - Bollinger Bands and Advanced Indicators
Learn advanced technical analysis techniques including Bollinger Bands, moving averages, support/resistance lines, and candlestick patterns using Python and data science libraries for stock market analysis.
Getting Started with Technical Analysis Using Python
Learn Python-based technical analysis for stock market predictions, covering data retrieval, calculations, and visualization using libraries like Pandas and Plotly. Ideal for beginners in financial data science.
Technical Analysis: RSI and Bollinger Bands Using Python
Learn to perform technical analysis using Python, focusing on RSI and Bollinger Bands. Create customizable charts with real-time data for stock market predictions using various data science libraries.
Technical Analysis: MACD and Stochastic Oscillator Using Python
Learn to use Python for stock market technical analysis, focusing on MACD and Stochastic Oscillator. Covers data retrieval, chart creation, and multiple technical indicators using various data science libraries.
Technical Analysis with Python for Stock Market Predictions
Learn Python-based technical analysis for stock market predictions, covering data retrieval, chart patterns, moving averages, and advanced visualization techniques using data science libraries.
Python for Data Science - Mastering NumPy, Pandas, and Plotly
Comprehensive tutorial on using NumPy, Pandas, and Plotly for data science in Python. Covers core concepts and practical applications with hands-on examples and free resources.
Investment Analysis: Python for Finance - Part 9
Learn investment analysis using Python: calculate Alpha, Beta, CAPM, expected returns, and Sharpe Ratio. Practical coding examples provided for hands-on experience in financial data analysis and portfolio management.