How APIs and data integrations work in Data Engineering
Choosing the right method for ingesting data into data lakes and data warehouses.
Introduction
Data engineering relies on efficient and scalable methods for integrating data from various sources into data lakes, data warehouses, and analytical platforms. APIs play a crucial role in automating data ingestion, synchronization, and transformation, but different integration methods serve different use cases.
In this post, we’ll explore how REST APIs, Webhooks, GraphQL, Message Queues, gRPC, and Direct Database Connections help move data across systems in a data engineering ecosystem. We'll also look at real-world examplesof how they are used in data pipelines.
1. REST API: standard for batch data ingestion
How it works?
REST APIs enable structured data ingestion via scheduled batch jobs or on-demand requests. A data pipeline pulls data from a source system using HTTP requests and stores it in a data warehouse or data lake.
Real-World example: importing CRM data into a data warehouse
A company collects customer interactions in Salesforce.
A scheduled data pipeline calls
GET /customersandGET /transactionsevery night.The responses (JSON data) are loaded into Snowflake or BigQuery for further analysis.
Best for structured data ingestion from external sources, but it requires frequent polling unless combined with event-driven mechanisms.
2. Webhooks: real-time (event-driven) data ingestion
How it works?
Webhooks provide instant data updates when an event occurs, eliminating the need for polling. Instead of a batch job requesting new data, the system pushes new records to the pipeline as soon as they appear.
Real-World example: streaming e-commerce orders to a data lake
An online store records orders in Shopify.
Shopify sends a webhook event (“order_created“) every time a customer places an order.
A cloud function (e.g., Google Cloud Function, AWS Lambda) captures the event and writes it to an S3 bucket / GCS (data lake).
Great for real-time ingestion, but requires handling event failures and duplicates properly.
3. GraphQL: flexible data querying for analytical systems
How it works?
GraphQL allows data pipelines to request only the necessary data fields, reducing bandwidth and processing time. Unlike REST, where predefined endpoints return full datasets, GraphQL enables precise queries.
Real-World example: extracting custom metrics from a marketing API
A company tracks campaign performance in some Marketing Ads API.
Instead of pulling all data, a GraphQL query requests only impressions, clicks, and conversions.
This data is then loaded into a data warehouse for reporting.
Useful for pulling structured data efficiently, but requires GraphQL-supporting source systems.
4. Message Queues & Event Streaming: high-volume, asynchronous data ingestion
How it works?
Message queues (RabbitMQ, Amazon SQS) and event streaming platforms (Kafka, Google Pub/Sub) enable scalable, real-time ingestion by buffering messages and events before they reach the data platform.
Real-World example: processing IoT sensor data
A logistics company collects GPS and temperature data from trucks.
The devices publish messages to Kafka every 10 seconds.
A streaming pipeline processes the data and stores it in BigQuery for real-time monitoring.
Ideal for handling high-volume event data, but requires infrastructure to manage message brokers.
5. gRPC: high-performance microservice communication in data pipelines
How it works?
gRPC enables fast, binary-based communication between services, making it useful for microservices exchanging large datasets efficiently.
Real-World example: microservice-based ETL pipeline
A real-time fraud detection system receives transaction data.
A gRPC service sends enriched transaction details from an API to a feature store.
Another service fetches features from the store and scores transactions for fraud detection.
Best for low-latency communication between microservices, but not widely adopted in traditional ETL workflows.
6. Direct Database Connections: querying and syncing Data
How it works?
Instead of using an API, some pipelines directly connect to source databases (PostgreSQL, MySQL, MongoDB) using drivers and libraries (e.g., psycopg2 for PostgreSQL, SQLAlchemy).
Real-World example: syncing operational data into a data warehouse
A company stores user activity logs in PostgreSQL.
A scheduled job in Airflow executes SQL queries to pull the latest logs.
The extracted data is loaded into BigQuery for analytics.
Good for structured internal data sources, but can impact database performance if not handled properly.
Final thoughts
APIs and integration methods play a crucial role in data engineering, enabling efficient data movement across systems. Choosing the right method depends on the data volume, real-time needs, and system architecture.
Batch API calls (REST) are great for pulling structured data on a schedule.
Webhooks provide real-time updates, reducing polling overhead.
Event-driven systems (Kafka, Pub/Sub) allow massive-scale data ingestion.
GraphQL improves query efficiency when dealing with large APIs.
gRPC speeds up service-to-service communication in modern data platforms.
Direct database connections work well for structured, internal data syncs.
Understanding these methods ensures that your data pipelines are scalable, reliable, and performant.