How does MapReduce work for Big Data? All you need to know
How MapReduce processes large datasets in a Big Data environment, step by step, and what business benefits the Hadoop framework brings to companies.
Data Engineering - Page 5
How to Design an Effective GenAI Agent: Best Practices and Challenges
Design principles for GenAI agents in retail: how GPT models and conversational AI make shopping more accessible, plus common development challenges.
Introduction to Databricks: What Is It and Why Consider Migrating?
What Databricks is, how its architecture and core features work, and when migrating your data engineering and analytics workloads to it makes sense.
Introduction to koalas and databricks
Koalas brings the pandas API to Apache Spark. See how to move pandas code to Databricks and scale data analysis from a single machine to big data.
Zbigniew Pomianowski
Maximize Efficiency: Automate Your Processes with Data Engineering
How data engineering automates business processes: pipelines, storage, and transformation that enable real-time analytics, plus steps to implement it.
DS Stream
Microservices in Data Engineering: How to Break a Monolith into Smaller Parts
Discover how microservices revolutionize data engineering by replacing monolithic systems, boosting scalability, flexibility, and deployment efficiency.
Optimizing Apache Spark
Speed up Apache Spark jobs with efficient file formats, smart partitioning, data skew fixes, caching, and careful use of UDFs. Practical tips for big data.
Marcin Orliński
Overview of Google Cloud Capabilities for Building Efficient ETL Pipelines
How to build ETL pipelines on Google Cloud, using Cloud Storage and BigQuery to extract, transform, and load data in secure, cost-efficient workflows.
Real-Time Big Data Analytics: Examples and Benefits
Real-time big data analytics explained: how instant processing improves decisions and fraud detection, which industries use it, and what it delivers.
Laura Kszczanowicz
Strategize for Success: Implement Automation to Boost Productivity
Business automation depends on data engineering. How pipelines, analytics and integration feed automated processes, with practical steps to get started.
DS Stream
Streaming Twitter data with Google Cloud Pub/Sub and Apache Beam
Build a streaming pipeline that pulls Twitter data through the API, publishes it to Google Cloud Pub/Sub and processes it continuously with Apache Beam.
Pawel Jedrzejewicz
Data Streaming Tools and Solutions: A Practical Guide
Data streaming tools compared: Kafka, Kinesis, Pub/Sub, Flink, and Spark Structured Streaming. See what each one does, when to pick it, and how to choose.