Big Data Processing | Apache Spark

The content focuses on the applications and functionalities of Apache Spark and related technologies in big data processing and analytics. It covers workshops, real-time data pipeline implementations, challenges in data engineering, and the integration of machine learning with Spark. The resources also discuss the transformation of batch to streaming data, automation in feature extraction, and the migration from Spark to other frameworks. Users can explore scalability, performance, and practical applications of data streaming solutions in various industries.

[Open Lakehouse + AI] Declarative Pipelines in Apache Spark
Fundamentos de Big Data com Python: Tecnologia e Aplicações Praticas
Big Data Management and NoSQL Strategies in AI-Driven E-Commerce Systems.pdf
Extraction of association rules in a diabetic dataset using parallel FP-growth algorithm under apache spark
Unlock faster data-driven business decisions with Azure Databricks - Infographic
Unlock faster insights with Azure Databricks
BigData - Apache Spark Sqoop Introduce Basic
If You Use Databricks, You Definitely Need FME
Apache Sparkに対するKubernetesのNUMAノードを意識したリソース割り当ての性能効果 (Open Source Conference 2025 Tokyo/Spring 発表資料)
FEATURE EXTRACTION AND FEATURE SELECTION: REDUCING DATA COMPLEXITY WITH APACHE SPARK
data pipelines complexity human expertise and LLM era
scalable air quality analytics with apache spark and apache sedona
A quick presentation on Big Data Analytics and Applications
Top 5 Data Science Tools You Should Be Using in 2024 | IABAC
 
Amazon's Exabyte-Scale Migration from Spark to Ray
Introduction to Big Data Engineering.pdf
Real-Time Data Analytics with Apache Kafka and Spark.pptx
A Master Guide To Apache Spark Application And Versatile Uses.pdf
Building Real-Time Data Pipelines: Stream & Batch Processing workshop Slide
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, PostgreSQL, Redpanda, Debezium, and Benthos to master building advanced real-time data pipelines.