Data Engineering

10 min read1 Data Engineer Lead, 2 Data Engineers, 1 Analytics Engineer

Modern Data Platform

Building a scalable data engineering pipeline processing 1B+ events daily with Airflow, dbt, and ClickHouse.

data

Overview

We built a modern data platform that ingests data from 20+ sources, processes billions of events daily, and provides sub-second analytics for business users. The platform enables self-service analytics while maintaining data quality and governance.

Challenges

1Data siloed across multiple systems (ERP, CRM, E-commerce)
2No single source of truth for business metrics
3Slow query performance on large datasets
4Manual ETL processes prone to errors
5Limited visibility into data quality issues

Solutions

Debezium CDC for real-time change data capture from PostgreSQL
Apache Airflow for workflow orchestration
dbt for transformation layer with 97 models and version control
ClickHouse for real-time analytics
Data quality monitoring with Great Expectations
Self-service BI with Metabase

Implementation

Data Ingestion Layer

Set up Debezium CDC for real-time change data capture from PostgreSQL. Built connectors for databases, APIs, and file sources.

Orchestration & ETL

Deployed Airflow with custom operators. Created 100+ DAGs for various data pipelines.

Transformation Layer

Implemented dbt models with testing and documentation. Created standardized metrics definitions.

Analytics Layer

Deployed ClickHouse cluster for OLAP queries. Built materialized views for common aggregations.

Results

1B+

Daily Events

Processed reliably with automatic scaling

<100ms

Query Latency

P99 on complex analytical queries

<5 min

Data Freshness

From source to analytics-ready

99.9%

Pipeline Reliability

With automatic retry and alerting

-40%

Cost Reduction

Compared to cloud data warehouse

Tech Stack

Debezium CDC

Apache Airflow

dbt Core

ClickHouse

PostgreSQL

Python

Great Expectations

Metabase

Docker

Dokploy

Timeline

6 months

Team

1 Data Engineer Lead, 2 Data Engineers, 1 Analytics Engineer

Back to Case Studies

Data Engineering

10 min read1 Data Engineer Lead, 2 Data Engineers, 1 Analytics Engineer

Modern Data Platform

Building a scalable data engineering pipeline processing 1B+ events daily with Airflow, dbt, and ClickHouse.

Modern Data Platform

data

Overview

Challenges

1Data siloed across multiple systems (ERP, CRM, E-commerce)
2No single source of truth for business metrics
3Slow query performance on large datasets
4Manual ETL processes prone to errors
5Limited visibility into data quality issues

Solutions

Debezium CDC for real-time change data capture from PostgreSQL
Apache Airflow for workflow orchestration
dbt for transformation layer with 97 models and version control
ClickHouse for real-time analytics
Data quality monitoring with Great Expectations
Self-service BI with Metabase

Implementation

Data Ingestion Layer

Set up Debezium CDC for real-time change data capture from PostgreSQL. Built connectors for databases, APIs, and file sources.

Orchestration & ETL

Deployed Airflow with custom operators. Created 100+ DAGs for various data pipelines.

Transformation Layer

Implemented dbt models with testing and documentation. Created standardized metrics definitions.

Analytics Layer

Deployed ClickHouse cluster for OLAP queries. Built materialized views for common aggregations.

Results

1B+

Daily Events

Processed reliably with automatic scaling

<100ms

Query Latency

P99 on complex analytical queries

<5 min

Data Freshness

From source to analytics-ready

99.9%

Pipeline Reliability

With automatic retry and alerting

-40%

Cost Reduction

Compared to cloud data warehouse

Tech Stack

Debezium CDC

Apache Airflow

dbt Core

ClickHouse

PostgreSQL

Python

Great Expectations

Metabase

Docker

Dokploy

Timeline

6 months

Team

1 Data Engineer Lead, 2 Data Engineers, 1 Analytics Engineer