Data Ingestion: The Front Door to Modern Data Infrastructure

My post content

In today’s fast-paced, data-centric world, organizations generate massive volumes of data from countless sources—e-commerce platforms, IoT devices, customer interactions, and internal systems. But before any of that data can be used for analytics, AI, or business decision-making, it must first be ingested into a modern data platform. This is why Data Ingestion stands as the first pillar in our six-part series on modern data infrastructure.

Data ingestion is the process of collecting and moving data from various sources into a centralized system like a data lake, warehouse, or both. It is the "front door" of your architecture—everything else depends on its accuracy, timeliness, and flexibility.

Five Design Considerations Before Choosing a Service:

Choosing the right ingestion service isn’t about preference — it’s about fit for purpose. Ask these design questions to guide your selection:

1. What is the nature of the data?

• Streaming: orders, sensor events → Kinesis, EventBridge

• File-based: SFTP, Excel drops → Transfer Family, DataSync

• Database changes: MySQL, Oracle → DMS

2. What is the latency requirement?

• Seconds: real-time fraud detection → Kinesis/MSK

• Hours: end-of-day financial loads → Glue, DMS

3. What is the expected volume and frequency?

• Continuous millions of events: Kinesis/MSK

• Hourly batches or daily files: Glue, Transfer Family

4. Do you need to transform the data at ingestion?

• Yes: use Glue, Firehose transform, Lambda

• No: route raw data directly to lake or warehouse

5. What are the source systems?

• Cloud DBs or on-prem databases: DMS

• Third-party APIs or event-driven apps: EventBridge or Lambda

• Legacy systems: Transfer Family

Use this simple framework to align your ingestion architecture:

• Sub-second response - Kinesis Data Streams

• Managed streaming with minimal setup - Kinesis Firehose

• Kafka compatibility - Amazon MSK

• SaaS events or cross-account APIs- EventBridge

• Replicate Oracle/Postgres to cloud - AWS DMS

• Scheduled transformations - AWS Glue

• Legacy FTP servers - Transfer Family

• File migrations from on-prem - DataSync

• Simple webhook/API triggers - AWS Lambda

A robust ingestion architecture isn’t one-size-fits-all — it’s purpose-built. By matching data characteristics and business needs to AWS ingestion services, you can build a flexible, scalable, and cost-effective data foundation.

Modern data success starts with ingestion — ask the right questions, and let AWS services do the heavy lifting.

Let’s build, learn, and reflect — one post at a time.

— Junaith Haja
Harnessing Data and AI for social good, one blog at a time.

#DataIngestion #AWSCloud #ModernDataInfrastructure #DataEngineering #AmazonKinesis #AWSGlue #StreamingData #BatchProcessing #CloudArchitecture #DataAsAProduct