Data ingestion is the process of collecting and importing data from various sources into a centralized storage system such as a data lake or data warehouse.
Modern data engineering projects rarely rely on a single data source. Instead, they ingest data from APIs, databases, files, streaming systems, and cloud platforms.
What is Data Ingestion?
Data ingestion means:
- Extracting data
- Moving it to a target system
- Preparing it for processing
It can be:
- Batch ingestion
- Real-time ingestion
- Micro-batch ingestion
Common Data Sources
1. Databases
- MySQL
- PostgreSQL
- SQL Server
- Oracle
Data is extracted using SQL queries or replication tools.
2. APIs
- REST APIs
- Third-party SaaS systems
- Payment gateways
- CRM platforms
Data is fetched using HTTP requests and JSON parsing.
3. Files
- CSV
- Excel
- JSON
- XML
- Log files
Files may come from local systems or cloud storage.
4. Streaming Systems
Real-time data sources such as:
- Apache Kafka
- IoT event streams
- Application logs
Used for event-driven ingestion.
5. Cloud Storage
- Amazon S3
- Google Cloud Storage
- Azure Blob Storage
Often used as landing zones (raw data layer).
Data Ingestion Architecture
Multiple Data Sources
↓
Ingestion Layer
↓
Raw Storage (Data Lake)
↓
Processing Layer
↓
Data Warehouse
Ingestion Patterns
1. Batch Ingestion
- Runs hourly/daily
- Suitable for reports
- Uses scheduled jobs
Example: Daily sales import from database.
2. Real-Time Ingestion
- Continuous event streaming
- Low latency
- Suitable for live dashboards
Example: Live transaction monitoring.
3. Incremental Ingestion
- Loads only new or changed data
- Uses timestamps or IDs
- Reduces processing time
Tools for Multi-Source Ingestion
- Apache NiFi
- Apache Airflow
- Custom Python scripts
- Cloud-native ingestion services
Key Challenges
- Different data formats
- Schema mismatches
- Duplicate records
- Data quality issues
- API rate limits
- Network failures
Best Practices
- Use a raw layer to store original data
- Validate data before processing
- Use schema versioning
- Implement retry mechanisms
- Monitor ingestion logs
- Design idempotent pipelines
Example Scenario
E-commerce Company:
- Orders from MySQL
- Customer data from CRM API
- Website logs from Kafka
- Marketing data from CSV files
All data ingested into cloud storage → processed → loaded into warehouse → dashboard reporting.
Interview Answer (Short Version)
Data ingestion from multiple sources involves collecting data from databases, APIs, files, and streaming platforms and centralizing it into a data lake or warehouse for processing and analytics.
Final Summary
Data Ingestion from Multiple Sources includes:
- Structured and unstructured data
- Batch and real-time processing
- Centralized storage
- Data validation and monitoring
It is the foundation of any scalable data engineering pipeline.