REST API Calls for Data Engineers: A Practical Guide with Examples

REST API Calls for Data Engineers ## Introduction ## What is a REST API (Data Engineer Perspective) ## Core REST HTTP Methods You’ll Use ## Anatomy of a REST API Request ## Components: ## Example 1: Simple GET Request (Fetching Data) ## Use Case ## API Request ## Python Example (requests library) ## Typical JSON Response ## Example 2: Query Parameters (Filtering Data) ## Use Case ## Python Code ## Example 3: POST Request (Complex Queries) ## API Call ## Payload ## Python Example ## Authentication Methods (Very Important) ## 1. API Key Authentication ## 2. Bearer Token (OAuth 2.0) ## 3. Basic Auth (Less Secure) ## Example 4: Pagination (Very Common in APIs) ## API Response with Pagination ## Python Pagination Logic ## Example 5: Handling Rate Limits ## Retry Logic Example ## Example 6: Error Handling (Critical for Pipelines) ## REST API Data Flow in a Data Pipeline ## Best Practices for Data Engineers ## Conclusion As a Data Engineer, you rarely work only with databases. Modern data pipelines frequently ingest data from REST APIs—whether it’s pulling data from SaaS tools (Salesforce, Jira, Google Analytics), internal microservices, or third-party providers. Understanding how REST APIs work and how to interact with them efficiently is a core data engineering skill. REST (Representational State Transfer) APIs allow systems to communicate over HTTP using standard methods. From a data engineer’s standpoint: In data engineering, GET and POST are used 90% of the time. A typical REST API call consists of: Fetch daily sales data from an external system. Pull incremental data to avoid reprocessing historical records. ✅ Best Practice: Always design pipelines to be incremental. Some APIs require POST when filters are complex. 🔐 Data Engineering Tip Always store credentials in: Most APIs limit results per request. ✅ Always handle pagination, or you’ll silently miss data. APIs often limit requests: 📌 Production pipelines should use: Common HTTP Status Codes: ✔ Always design idempotent pipelines ✔ Log request/response metadata ✔ Store raw API responses for reprocessing ✔ Use incremental loads (timestamps, IDs) ✔ Monitor failures and latency ✔ Respect API rate limits REST APIs are a primary data ingestion mechanism for data engineers. Mastering REST calls—authentication, pagination, retries, and error handling—will make your pipelines reliable, scalable, and production-ready. If you understand REST APIs deeply, integrating any new data source becomes significantly easier. If you to connect with me, let’s connect on LinkedIn or drop me a message—I’d love to explore how I can help drive your data success! Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: https://api.example.com/v1/orders?start_date=2025-01-01&limit=100 Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: https://api.example.com/v1/orders?start_date=2025-01-01&limit=100 CODE_BLOCK: https://api.example.com/v1/orders?start_date=2025-01-01&limit=100 CODE_BLOCK: GET https://api.company.com/v1/sales Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: GET https://api.company.com/v1/sales CODE_BLOCK: GET https://api.company.com/v1/sales CODE_BLOCK: import requests url = "https://api.company.com/v1/sales" headers = { "Authorization": "Bearer YOUR_API_TOKEN", "Accept": "application/json" } response = requests.get(url, headers=headers) data = response.json() print(data) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: import requests url = "https://api.company.com/v1/sales" headers = { "Authorization": "Bearer YOUR_API_TOKEN", "Accept": "application/json" } response = requests.get(url, headers=headers) data = response.json() print(data) CODE_BLOCK: import requests url = "https://api.company.com/v1/sales" headers = { "Authorization": "Bearer YOUR_API_TOKEN", "Accept": "application/json" } response = requests.get(url, headers=headers) data = response.json() print(data) CODE_BLOCK: { "sales": [ { "order_id": 101, "amount": 250.50, "currency": "USD", "order_date": "2025-01-10" } ] } Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: { "sales": [ { "order_id": 101, "amount": 250.50, "currency": "USD", "order_date": "2025-01-10" } ] } CODE_BLOCK: { "sales": [ { "order_id": 101, "amount": 250.50, "currency": "USD", "order_date": "2025-01-10" } ] } CODE_BLOCK: GET /v1/sales?start_date=2025-01-01&end_date=2025-01-31 Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: GET /v1/sales?start_date=2025-01-01&end_date=2025-01-31 CODE_BLOCK: GET /v1/sales?start_date=2025-01-01&end_date=2025-01-31 CODE_BLOCK: params = { "start_date": "2025-01-01", "end_date": "2025-01-31" } response = requests.get(url, headers=headers, params=params) sales_data = response.json() Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: params = { "start_date": "2025-01-01", "end_date": "2025-01-31" } response = requests.get(url, headers=headers, params=params) sales_data = response.json() CODE_BLOCK: params = { "start_date": "2025-01-01", "end_date": "2025-01-31" } response = requests.get(url, headers=headers, params=params) sales_data = response.json() CODE_BLOCK: POST /v1/sales/search Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: POST /v1/sales/search CODE_BLOCK: POST /v1/sales/search CODE_BLOCK: { "region": ["US", "EU"], "min_amount": 100, "date_range": { "from": "2025-01-01", "to": "2025-01-31" } } Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: { "region": ["US", "EU"], "min_amount": 100, "date_range": { "from": "2025-01-01", "to": "2025-01-31" } } CODE_BLOCK: { "region": ["US", "EU"], "min_amount": 100, "date_range": { "from": "2025-01-01", "to": "2025-01-31" } } CODE_BLOCK: payload = { "region": ["US", "EU"], "min_amount": 100, "date_range": { "from": "2025-01-01", "to": "2025-01-31" } } response = requests.post(url, headers=headers, json=payload) data = response.json() Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: payload = { "region": ["US", "EU"], "min_amount": 100, "date_range": { "from": "2025-01-01", "to": "2025-01-31" } } response = requests.post(url, headers=headers, json=payload) data = response.json() CODE_BLOCK: payload = { "region": ["US", "EU"], "min_amount": 100, "date_range": { "from": "2025-01-01", "to": "2025-01-31" } } response = requests.post(url, headers=headers, json=payload) data = response.json() CODE_BLOCK: Authorization: ApiKey abc123 Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Authorization: ApiKey abc123 CODE_BLOCK: Authorization: ApiKey abc123 CODE_BLOCK: Authorization: Bearer eyJhbGciOi... Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Authorization: Bearer eyJhbGciOi... CODE_BLOCK: Authorization: Bearer eyJhbGciOi... CODE_BLOCK: requests.get(url, auth=("username", "password")) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: requests.get(url, auth=("username", "password")) CODE_BLOCK: requests.get(url, auth=("username", "password")) CODE_BLOCK: { "data": [...], "page": 1, "total_pages": 10 } Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: { "data": [...], "page": 1, "total_pages": 10 } CODE_BLOCK: { "data": [...], "page": 1, "total_pages": 10 } CODE_BLOCK: all_data = [] page = 1 while True: params = {"page": page, "limit": 100} response = requests.get(url, headers=headers, params=params) result = response.json() all_data.extend(result["data"]) if page >= result["total_pages"]: break page += 1 Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: all_data = [] page = 1 while True: params = {"page": page, "limit": 100} response = requests.get(url, headers=headers, params=params) result = response.json() all_data.extend(result["data"]) if page >= result["total_pages"]: break page += 1 CODE_BLOCK: all_data = [] page = 1 while True: params = {"page": page, "limit": 100} response = requests.get(url, headers=headers, params=params) result = response.json() all_data.extend(result["data"]) if page >= result["total_pages"]: break page += 1 CODE_BLOCK: 429 Too Many Requests Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: 429 Too Many Requests CODE_BLOCK: 429 Too Many Requests CODE_BLOCK: import time response = requests.get(url, headers=headers) if response.status_code == 429: time.sleep(60) response = requests.get(url, headers=headers) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: import time response = requests.get(url, headers=headers) if response.status_code == 429: time.sleep(60) response = requests.get(url, headers=headers) CODE_BLOCK: import time response = requests.get(url, headers=headers) if response.status_code == 429: time.sleep(60) response = requests.get(url, headers=headers) CODE_BLOCK: response = requests.get(url, headers=headers) if response.status_code != 200: raise Exception( f"API failed with status {response.status_code}: {response.text}" ) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: response = requests.get(url, headers=headers) if response.status_code != 200: raise Exception( f"API failed with status {response.status_code}: {response.text}" ) CODE_BLOCK: response = requests.get(url, headers=headers) if response.status_code != 200: raise Exception( f"API failed with status {response.status_code}: {response.text}" ) CODE_BLOCK: REST API ↓ Python / Spark Job ↓ Raw Zone (JSON) ↓ Transformation (Flattening, Cleaning) ↓ Data Warehouse (Snowflake / BigQuery / Redshift) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: REST API ↓ Python / Spark Job ↓ Raw Zone (JSON) ↓ Transformation (Flattening, Cleaning) ↓ Data Warehouse (Snowflake / BigQuery / Redshift) CODE_BLOCK: REST API ↓ Python / Spark Job ↓ Raw Zone (JSON) ↓ Transformation (Flattening, Cleaning) ↓ Data Warehouse (Snowflake / BigQuery / Redshift) - What REST APIs are (briefly, practically) - Common REST methods from a data engineering perspective - Authentication patterns - Pagination, filtering, and rate limiting - Real-world examples using Python - Best practices for production data pipelines - REST APIs are data sources - JSON is the most common data format - APIs are often incremental, paginated, and rate-limited - APIs feed data lakes, warehouses, or streaming systems - Base URL: https://api.example.com - Endpoint: /v1/orders - Query Parameters: start_date, limit - Headers: Authentication, content type - HTTP Method: GET / POST - Transformed - Stored in a data lake or warehouse - Environment variables - Secret managers (AWS Secrets Manager, Azure Key Vault) - Exponential backoff - Retry limits - 200 – Success - 400 – Bad Request - 401 – Unauthorized - 404 – Not Found - 500 – Server Error