Rethinking Data Movement: A First Principles Approach

2024-08-01 · Source: Modern Data 101 · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

The article "Why Data Movement Needs a Rethink" argues that traditional data ingestion methods, characterized by nightly batch jobs and monolithic processing, are no longer viable due to an explosion of data sources, demand for near real-time freshness, and increasing cost pressures. It introduces five principles of modern data movement: ELT over ETL, incremental-first pipelines, API & DB parity, built-in observability, and extensibility without bottlenecks. These principles are embodied in the Data Developer Platform (DDP) Data Movement Engine, which utilizes an Extract, Normalise, Load (ENL) architecture. This engine supports declarative YAML configs, Debezium-powered Change Data Capture (CDC), schema drift handling, idempotent Iceberg-native loads, and integrated operational metrics via Prometheus and REST APIs, achieving high throughput of approximately 45k rows/sec.

Key takeaway

For MLOps Engineers and Data Engineers building real-time data pipelines, adopting the principles of modern data movement is crucial. Focus on incremental-first strategies, robust Change Data Capture (CDC), and built-in observability to manage increasing data complexity and latency demands. Your team should evaluate solutions like the DDP Data Movement Engine that offer declarative configurations and extensible architectures to reduce operational overhead and ensure data trust.

Key insights

Modern data movement prioritizes incremental, observable, and extensible pipelines to address current data complexity, latency, and cost challenges.

Principles

• ELT separates ingestion from transformation.
• Incremental processing is the default for efficiency.
• Observability is non-negotiable for operational discipline.

Method

The DDP Data Movement Engine employs an ENL (Extract, Normalise, Load) architecture, using declarative YAML, Debezium-powered CDC, and idempotent, chunked loads for reliable, efficient data transfer.

In practice

• Implement incremental-first pipelines.
• Utilize Change Data Capture (CDC) for real-time sync.
• Ensure API connectors handle rate limits and pagination.

Topics

• Data Movement
• Change Data Capture
• ELT Architecture
• Data Movement Engine
• Data Observability

Best for: Data Engineer, MLOps Engineer, AI Architect

Related on AIssential

• 5 Practical Tips for Transforming Your Batch Data Pipeline into Real-Time: Upcoming Webinar — Modernizing data pipelines from batch to real-time requires strategic prioritization, incremental adoption, and modern platform utilization.
• Self-Healing Data Observability in Converged Architectures — Self-healing data platforms require an "Anticipate, Remediate, Immunize" (ARI) loop for autonomous data quality.
• The Growing Shift From ETL to ELT in Modern Data Engineering — Modern cloud platforms enable ELT workflows, loading raw data first for flexible, in-platform transformation.
• How Figma Upgraded Data Pipeline from Multi-Day Latency to Real-Time — Incremental synchronization via Change Data Capture significantly improves data freshness and reduces costs for large-scale data pipelines.
• I Thought Data Engineering Was Just Writing Scripts. I Was Wrong. — True data engineering extends beyond scripting to build reliable systems through idempotency, persistence, and scheduling.
• ML Experiment Tracking in H2O Driverless AI | Part 10 — Effective experiment tracking is crucial for data science, enabling parallel runs, reproducibility, real-time monitoring, and comparison.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Modern Data 101.