Chapter 57: AWS Data Analytics

AWS Data Analytics.

This is one of those topics where many students and freshers get lost because AWS has ~20+ services that can be used for analytics in one way or another. But the good news is: in real life (especially in India 2026), 90 % of companies use only 5–7 services for their analytics stack.

So we won’t do a firehose of 25 names. We’re going to build the story logically — like I’m your favorite teacher explaining it over a second cup of filter coffee — so you actually understand what each piece does, why people choose it, and what a typical Hyderabad startup or mid-size company actually builds in 2026.

Ready? Let’s go step by step.

1. What do we even mean by “AWS Data Analytics” in 2026?

AWS Data Analytics = all the tools and services AWS provides to collect, store, process, transform, analyze, visualize, and act on large amounts of data — usually for business intelligence, reporting, dashboards, machine learning input, fraud detection, customer 360, operations monitoring, etc.

The most important mental model right now (2026):

You have data coming from many places:

• User clicks & behavior → mobile/web/app
• Transactions → payment gateway, orders
• Logs → servers, containers, Lambda
• IoT → sensors, delivery bikes
• Third-party → Google Analytics, CRM (Salesforce/Zoho), ad platforms

You want to answer questions like:

• What is our average order value by city this month?
• Which restaurants have the highest cancellation rate in Hyderabad?
• Which marketing campaign gave the best ROI in Telangana?
• How many users churned last month and why?

AWS Data Analytics is the complete pipeline that lets you move data from “raw chaos” → “clean & queryable” → “visual dashboard” → “automated decision / alert”.

2. The Typical Modern Analytics Pipeline on AWS (2026 – Hyderabad Reality)

Most companies follow roughly this flow:

1. Collect / Ingest data from many sources
2. Store it cheaply & durably (usually in S3)
3. Transform / Clean / Enrich it (ETL or ELT)
4. Query / Analyze it quickly (SQL or Spark)
5. Visualize & Report (dashboards, alerts)
6. Act / Feed downstream (ML, alerts, operational systems)

Now let’s see the most popular services that actually get used in 2026 for each step.

3. Real Hyderabad Example – Full Analytics Stack (2026 Typical)

Your startup “TeluguBites” — restaurant discovery + food ordering app:

Data sources:

• Mobile app events → Kinesis Data Firehose → S3 raw bucket
• Order transactions → from Aurora PostgreSQL → DMS → S3 (or direct Glue connector)
• Payment gateway webhooks → API Gateway → Lambda → S3 / DynamoDB
• Customer support calls → Transcribe → S3

Pipeline they built (very common pattern):

1. Ingest → Kinesis Firehose streams all app events & logs → lands in S3 “raw-zone” bucket
2. Store raw → S3 bucket with partitioning (year/month/day/hour)
3. Transform → AWS Glue job (Spark) runs every hour
   • Crawls raw data
   • Cleans (remove duplicates, fix formats)
   • Enriches (add city from pin-code)
   • Writes clean Parquet files to S3 “processed-zone”
4. Query / Analyze
   • Analysts use Amazon Athena → run SQL directly on S3 processed data Example:
     SQL

     PHP

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     SELECT city, AVG(order_amount) AS avg_order FROM processed_orders WHERE order_date >= CURRENT_DATE - INTERVAL '30' DAY GROUP BY city ORDER BY avg_order DESC;

   • For very complex / frequent reports → load same data into Redshift Serverless → faster joins & aggregations
5. Visualize → Amazon QuickSight dashboards
   • “Top 10 restaurants by revenue in Hyderabad last 30 days”
   • Auto-refresh every 4 hours
   • ML insights: “Anomaly detected: sudden drop in orders in Secunderabad”
6. Act → QuickSight alert → SNS → Slack/email when avg order value drops >15 % in any city

Monthly cost estimate (moderate–high traffic):

• S3 storage (5–15 TB) → ₹3,000–10,000
• Glue ETL jobs → ₹3,000–12,000
• Athena queries → ₹1,000–6,000
• Redshift Serverless (occasional heavy use) → ₹5,000–15,000
• QuickSight (10 users) → ₹3,000–8,000
• Total analytics bill → ₹15,000–50,000/month (very reasonable)

4. Quick Decision Tree – Which Analytics Service Should You Pick? (2026 Hyderabad View)

1. Do you want to run SQL on data already in S3 (no moving data)? → Amazon Athena (serverless, pay-per-query)
2. Do you need very fast, complex joins & aggregations on large data (BI dashboards)? → Redshift Serverless (easiest) or Redshift provisioned (if predictable high load)
3. Do you need ETL / data cleaning / transformation (Spark jobs)? → AWS Glue (serverless Spark + visual ETL)
4. Do you want no-code data prep (business analyst cleans data)? → Glue DataBrew
5. Do you want real-time streaming analytics (live order volume by city)? → Kinesis Data Analytics or OpenSearch

Summary Table – AWS Data Analytics Quick Map (Feb 2026 – India Focus)

Teacher’s final note (real talk – Hyderabad 2026):

Most growing startups do NOT start with Redshift.

They start with:

1. S3 (land all raw data)
2. Glue (clean & transform)
3. Athena (first SQL queries – zero cluster cost)
4. QuickSight (first dashboards)
5. Redshift Serverless (only when Athena gets slow or queries become very complex)

This path keeps costs low until you actually have large-scale analytics needs.

Got it? This is your first clear map of AWS Data Analytics — no 25-service list, just the things that actually matter right now.

Next lesson?

• Deep dive: Build a complete pipeline — Kinesis Firehose → S3 → Glue → Athena → QuickSight dashboard
• Step-by-step: Load CSV from S3 into Redshift Serverless & create first report
• Or full cost comparison: Athena vs Redshift Serverless vs Snowflake for 10 TB data?

Tell me — next whiteboard ready! 🚀📊