Modern Data Infrastructure- Part 1: The Six Pillars

In today’s data-driven world, infrastructure isn’t just about storage or speed—it’s about trust, scalability, and delivering insights at the speed of business.

Gone are the days when teams dumped everything into a single warehouse and hoped analytics would magically follow. Today, with the rise of AI, decentralized data teams, real-time use cases, and growing regulatory pressure, organizations need more than pipelines—they need data infrastructure that behaves like a product: modular, observable, secure, and purpose-built.

This post kicks off a new blog series on building modern data infrastructure using the AWS-native stack. We’ll break down key architectural pillars, map the relevant tools, and ultimately apply these learnings to a real-world challenge: designing an AI-powered solution to predict and prevent homelessness in Seattle.

Whether you're modernizing legacy systems or designing from scratch, this series will give you the clarity and confidence to build better data systems—with purpose.

1. Data Ingestion

Modern data systems demand flexibility in how data enters—real-time streams, batch uploads, APIs, and databases. A robust ingestion layer ensures timely, consistent, and scalable data flow across all sources. Without it, your infrastructure runs blind.

AWS Tools: Kinesis · DMS · Transfer Family · Lambda · EventBridge

2. Data Storage

A well-architected storage layer balances performance, scalability, and cost. Combining a data lake for raw and archival data with a warehouse for analytical workloads gives you flexibility to evolve. It’s the backbone that supports everything else.

AWS Tools: S3 · Redshift · RDS · DynamoDB · Redshift Spectrum

3. Data Processing

Data doesn’t create value until it’s refined. Processing pipelines transform messy, inconsistent inputs into high-quality, insight-ready assets. It’s where raw data becomes useful for downstream analytics, ML, and decision-making.

AWS Tools: Glue · EMR · Lambda · Step Functions

4. Governance & Security

As data grows, so do risk and responsibility. Governance ensures the right people access the right data at the right time—while protecting sensitive information, enforcing compliance, and building trust with stakeholders.

AWS Tools: Lake Formation · IAM · KMS · Macie · CloudTrail

5. Data Delivery & Consumption

A data product is only valuable when it’s used. Delivery bridges data engineering with end users—whether they’re querying SQL, building dashboards, running ML models, or consuming APIs. It enables action from insight.

AWS Tools: Athena · Redshift · QuickSight · SageMaker · API Gateway

6. Observability & Orchestration

Data infrastructure must be reliable, predictable, and transparent. Observability ensures pipelines are fresh, complete, and healthy—while orchestration keeps workflows moving, alerts teams on failures, and safeguards SLAs.

AWS Tools: CloudWatch · MWAA (Airflow) · EventBridge · DataBrew

We will learn in detail about each of these pillars in the upcoming articles.

Here is a cheat sheet below, if you find this post useful, please subscribe to our weekly newsletter Signal over Noise

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.

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