Data integration architecture for data engineers

From ETL fundamentals to operational analytics. 10 articles on data pipeline architecture patterns, CDC, and when managed sync replaces custom integration code.

Step 1: Fundamentals

ETL data pipeline with extract, transform, and load stages

ETL process steps and when extract-transform-load applies

The foundation of every data integration architecture decision. Understand when ETL fits your workload and when the warehouse dependency is overhead you can skip.

ELT data pipeline with source, load, and transform stages

ELT vs ETL for data engineers: choosing the right extraction pattern

ELT shifted transforms into the warehouse but kept the warehouse. Know the cost and complexity trade-offs so you can recommend the right pattern for each use case.

Hub-and-spoke architecture diagram connecting data sources to destinations

Data pipeline architecture patterns: batch, streaming, and event-driven

Every pipeline you build follows one of three patterns. Match the pattern to the latency requirement instead of defaulting to batch because it is familiar.

Step 2: Building Skills

Record list with changed rows highlighted for incremental sync

Change data capture methods for efficient replication

CDC tracks diffs instead of copying full snapshots. Know when log-based CDC, trigger-based CDC, or API-based polling fits your source system.

Webhook-triggered automated workflow connecting multiple apps

Webhook infrastructure and why custom handlers break

You have probably built a webhook handler that worked in staging and failed in production. Understand the failure modes before you build the next one.

Reverse ETL activating warehouse data to marketing tools

Reverse ETL and the warehouse loop

Reverse ETL closes the warehouse-to-tool gap. But if data started in a SaaS tool, routing it through a warehouse just to reach another tool is a round trip you can skip.

Step 3: Advanced Strategy

Two apps exchanging data via bidirectional sync arrows

SDK vs API for data sync: the build-vs-buy calculation

Every custom integration you maintain costs engineering hours. This article quantifies the trade-off so you can justify replacing hand-rolled sync with a managed platform.

Three disconnected app silos unifying into a single database

Data silos as an architecture failure mode

Silos form when each new tool stores its own copy of customer records without propagating changes. Recognize the architecture patterns that create them before your integration surface grows.

Identity resolution without an identity graph

If your systems share a common key like email or customer ID, matching records is a sync problem, not a data science project. Know when a shared key is enough.

Operational analytics dashboard with real-time metrics

Operationalizing data without a warehouse layer

Operational analytics pushes data into the tools where teams act on it. Direct tool-to-tool sync delivers this without the warehouse compute and SQL models you would otherwise maintain.