AWS Data Ingestion

Data Ingestion in AWS (Batch + Streaming)
Why Data Ingestion Matters
Before you can analyze anything, you must bring data into your system:
   ● From databases, APIs, logs, apps, IoT devices
   ● Into a data lake (usually S3) or warehouse

There are two main ingestion types:
1. Batch Ingestion (Scheduled / Bulk)
What it is:
   ● Data is collected and processed in chunks (hourly, daily, etc.)
When to use:
   ● Historical data
   ● Periodic reports
   ● Large datasets
Key Service: AWS Glue
What it does:
   ● Extract, Transform, Load (ETL)
   ● Reads from sources → transforms → writes to S3/Redshift
Features:
   ● Serverless
   ● Uses Spark (PySpark jobs)
   ● Built-in data catalog
Use case:
   ● Daily pipeline: RDS → S3 → Redshift
Think: scheduled ETL engine

Legacy/Orchestration: AWS Data Pipeline
What it does:
   ● Moves data between AWS services on a schedule
Note:
   ● Largely replaced by Glue / Step Functions
Think: older scheduling tool

2. Streaming Ingestion (Real-Time)
What it is:
   ● Data is processed continuously as it arrives
When to use:
   ● Real-time analytics
   ● Monitoring systems
   ● Fraud detection

1. Amazon Kinesis
What it does:
   ● Collects and processes real-time data streams
Components:
   ● Kinesis Data Streams → ingestion
   ● Kinesis Firehose → auto-load to S3/Redshift
Use cases:
   ● Log streaming
   ● Clickstream data
   ● IoT data

Think: AWS-native real-time pipeline

2. Amazon MSK
What it is:
   ● Managed Apache Kafka service
Why use it:
   ● If your system already uses Kafka
   ● More control than Kinesis
Use cases:
   ● Large-scale event streaming
   ● Enterprise pipelines
Think: Kafka on AWS without managing servers

Feature		Batch		Streaming
--------------------------------------------
Speed		Delayed		Real-time
Data size	Large chunks	Continuous small events
Tools		Glue		Kinesis / MSK
Use case	Reports, ETL	Live dashboards

Real Data Engineering Flow
Batch:
1. Data from DB → Glue job
2. Store → S3
3. Query → Athena / Redshift
Streaming:
1. App events → Kinesis / MSK
2. Process → Lambda / consumers
3. Store → S3 / Redshift

Summary:
   ● Glue = batch ETL (most important)
   ● Kinesis = real-time streaming (AWS-native)
   ● MSK = Kafka-based streaming
   ● Data Pipeline = older scheduling tool

Rule of thumb:
   ● Use batch for cost efficiency
   ● Use streaming for real-time insights