Data Pipelines Pocket Reference

Synopsis

Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack.

You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions.

You'll learn:

• What a data pipeline is and how it works
• How data is moved and processed on modern data infrastructure, including cloud platforms
• Common tools and products used by data engineers to build pipelines
• How pipelines support analytics and reporting needs
• Considerations for pipeline maintenance, testing, and alerting

Summary

Chapter 1: Introduction to Data Pipelines

* Definition and components of data pipelines
* Benefits and challenges of data pipelines
* Real-world example: Building a data pipeline to analyze customer behavior data

Chapter 2: Data Sources and Formats

* Types of data sources (e.g., databases, APIs, files)
* Data formats (e.g., JSON, CSV, XML)
* Best practices for data ingestion
* Real-world example: Extracting data from a relational database using Python's SQLAlchemy

Chapter 3: Data Transformation

* Techniques for data cleansing, transformation, and enrichment
* Joining, filtering, and aggregating data
* Handling missing values and data quality issues
* Real-world example: Normalizing timestamps and converting currency formats

Chapter 4: Data Integration

* Approaches to combining data from multiple sources
* ETL (Extract, Transform, Load) vs. ELT (Extract, Load, Transform)
* Data warehouses and data lakes
* Real-world example: Merging customer transaction data with product metadata

Chapter 5: Data Storage

* Types of data storage systems (e.g., relational databases, NoSQL databases, cloud storage)
* Partitioning and indexing for performance
* Storage optimization techniques
* Real-world example: Choosing between MySQL and MongoDB for storing customer data

Chapter 6: Data Analysis and Visualization

* Techniques for data analysis and exploration
* Visualization tools and libraries
* Data storytelling and insights extraction
* Real-world example: Using Tableau to create an interactive dashboard for sales analysis

Chapter 7: Data Security and Governance

* Data access control and authentication
* Encryption and data masking
* Data lineage and auditability
* Compliance and regulatory concerns
* Real-world example: Implementing role-based access control for sensitive customer data

Chapter 8: Data Pipelines Tools and Frameworks

* Introduction to popular data pipeline tools (e.g., Apache Airflow, Apache Spark, Talend)
* Features and benefits of each tool
* Best practices for tool selection and implementation
* Real-world example: Using Airflow to orchestrate a complex data pipeline

Chapter 9: Data Pipelines Metrics and Monitoring

* Metrics for evaluating data pipeline performance (e.g., latency, throughput, reliability)
* Monitoring and troubleshooting techniques
* Log analysis and error handling
* Real-world example: Setting up monitoring alerts for pipeline failures

Chapter 10: Data Pipelines Design and Architecture

* Principles for designing scalable and reliable data pipelines
* Architectural patterns (e.g., stream processing, batch processing, micro-batching)
* Dataflow orchestration and scheduling
* Real-world example: Architecting a data pipeline to handle large volumes of real-time data