📥 Data Ingestion: How Data Enters a Data Platform
Data ingestion is one of the first steps in almost every data engineering pipeline.
In simple terms:
Data ingestion = collecting data from different sources and moving it into a system where it can be stored and processed.
📌 1. What is Data Ingestion?
Data ingestion is the process of collecting data from various sources and transferring it to a destination such as:
Data Lake, Data Warehouse, Database, Lakehouse, Streaming platform
Example:
CRM ────────┐
API ────────┤
Database ───┼──→ Data Ingestion → Data Lake/Warehouse
Kafka ──────┤
Files ──────┘
🔄 2. Types of Data Ingestion
There are two major types:
📦 Batch Ingestion – Data is collected and transferred in batches at specific intervals.
⚡ Real-Time Ingestion – Data is transferred continuously as it is generated.
📦 3. Batch Ingestion
Batch ingestion processes data periodically.
Example: A company collects all sales transactions during the day and loads them into the warehouse every night.
8 AM ──┐
12 PM ─┤
4 PM ──┤ → Daily Batch → Warehouse
8 PM ──┘
Common Use Cases: Daily reports, Payroll, Monthly financial processing, Historical data migration
Advantages: ✅ Simple architecture, ✅ Easier monitoring, ✅ Cost-effective
Disadvantages: ❌ Data is not immediately available, ❌ Higher latency
⚡ 4. Real-Time Ingestion
Real-time ingestion continuously captures and transfers data as events occur.
Example:
Payment → Event Generated → Kafka → Stream Processor → Analytics System
The data can become available within seconds or milliseconds, depending on the architecture.
Use Cases: Fraud detection, Real-time monitoring, Stock market systems, IoT applications, Live recommendations
📊 Batch vs Real-Time
Batch: Periodic, Higher latency, Simpler, Usually cheaper, Example: Daily reports
Real-Time: Continuous, Low latency, More complex, Can be more expensive, Example: Fraud detection
📌 5. Common Data Sources
Data Engineers may ingest data from:
🗄️ Databases: PostgreSQL, MySQL, Oracle, SQL Server
🌐 APIs: REST APIs, GraphQL APIs
📄 Files: CSV, JSON, XML, Parquet
📡 Streaming Systems: Kafka, Kinesis, Pub/Sub
☁️ Cloud Applications: CRM, ERP, SaaS applications
🛠️ 6. Common Data Ingestion Tools
Batch: Apache Airflow, AWS Glue, Fivetran, Airbyte
Streaming: Apache Kafka, Amazon Kinesis, Google Pub/Sub, Apache Flink
🔄 7. Full Load vs Incremental Load
Full Load: Transfers the entire dataset.
Source → ALL Data → Destination
Useful when: Loading a table for the first time, Dataset is relatively small, Complete refresh is required
Incremental Load: Transfers only new or changed data.
Source → New/Changed Data → Destination
Example: If a table has 100 million records but only 50,000 changed today, an incremental pipeline processes those 50,000.
✅ Faster, ✅ Lower cost, ✅ Better scalability
🔥 8. Change Data Capture (CDC)
CDC is a technique for identifying changes in a source database.
It can capture: INSERT, UPDATE, DELETE