Chapter 45: AWS Databases
AWS Databases
When someone asks “what is AWS Databases?”, they usually mean:
“AWS has so many database services — RDS, DynamoDB, Aurora, DocumentDB, Neptune, ElastiCache, Redshift, Timestream, Keyspaces… how do I even understand which one to pick, and what each one is actually for?”
So today we’re going to do a clear, structured, practical overview — like a real classroom session — with everyday analogies, real Hyderabad startup examples, when to choose what, rough 2026 pricing, and the decision tree most Indian teams actually use.
No fluff. Just the truth.
1. Why Does AWS Offer So Many Database Services? (The Honest Answer)
AWS does not believe in “one database fits all”.
Different applications have completely different needs:
- Transaction speed vs analytics speed
- Structured (rows & columns) vs unstructured (JSON documents)
- Millions of small reads/writes vs few huge scans
- Strong consistency vs eventual consistency
- Relational joins vs flexible schema
- In-memory speed vs disk persistence
- Time-series data vs graph relationships
So instead of forcing everyone into one engine (like old-school Oracle or MySQL monopoly), AWS gives you specialized engines — all fully managed.
2. The Most Important AWS Database Services in 2026 (The Ones You Actually Use)
Here’s the realistic list that 95% of Hyderabad startups, mid-size companies, product teams, fintechs, edtech & gaming companies actually choose from:
| Rank | Service | Engine / Type | Best For (One-Liner) | Durability / Availability | Approx Monthly Cost (moderate, ap-south-2) | Typical Hyderabad Use Case (2026) |
|---|---|---|---|---|---|---|
| 1 | Amazon RDS | Relational (managed) | Traditional SQL databases — MySQL, PostgreSQL, SQL Server, Oracle, MariaDB | Multi-AZ = 99.99%+ | ₹3,000–15,000 | Web apps, e-commerce orders, user accounts, CMS |
| 2 | Amazon Aurora | Relational (cloud-native) | MySQL/PostgreSQL but 3–5× faster & more resilient | Multi-AZ = 99.99%+ | ₹5,000–25,000 | High-scale transactional apps, fintech, SaaS |
| 3 | Amazon DynamoDB | NoSQL key-value & document | High-scale, low-latency, fully serverless | 99.999% | ₹1,000–10,000 (on-demand) | Session store, shopping cart, user preferences, real-time apps |
| 4 | Amazon ElastiCache | In-memory cache (Redis / Memcached) | Super-fast caching & session store | 99.99% | ₹2,000–12,000 | Caching API responses, leaderboards, Redis queues |
| 5 | Amazon DocumentDB | MongoDB-compatible | MongoDB workloads without managing Mongo | Multi-AZ = 99.99% | ₹4,000–18,000 | Content management, catalogs, user-generated content |
| 6 | Amazon Redshift | Data warehouse (OLAP) | Analytics, reporting, BI dashboards | Multi-AZ possible | ₹10,000–50,000+ | Business intelligence, sales reports, user analytics |
| 7 | Amazon Timestream | Time-series database | IoT, DevOps metrics, application monitoring | 99.99% | ₹2,000–15,000 | Server metrics, IoT sensor data, clickstream |
| 8 | Amazon Keyspaces | Cassandra-compatible | Wide-column NoSQL (Cassandra workloads) | 99.99% | ₹3,000–20,000 | Messaging systems, product catalogs, time-series at scale |
| 9 | Amazon Neptune | Graph database | Social graphs, recommendation engines, fraud detection | Multi-AZ = 99.99% | ₹8,000–30,000+ | Friend recommendations, knowledge graphs |
3. Quick Decision Tree – Which Database Should You Pick? (2026 Reality)
Ask these questions in order — most Hyderabad teams end up here:
- Do you need SQL / relational tables / joins / ACID transactions?
- Yes → go to RDS or Aurora
- No → continue
- Do you need massive scale + single-digit millisecond latency + serverless?
- Yes → DynamoDB (most popular NoSQL choice in India 2026)
- No → continue
- Do you need in-memory speed (caching, sessions, leaderboards)?
- Yes → ElastiCache (Redis most common)
- No → continue
- Do you need analytics / reporting / large scans / aggregations?
- Yes → Redshift (classic warehouse) or Athena (serverless on S3)
- No → continue
- Do you have existing MongoDB / Cassandra / graph code you want to lift & shift?
- MongoDB → DocumentDB
- Cassandra → Keyspaces
- Graph (recommendations, social) → Neptune
- Do you have time-series data (metrics, IoT, logs over time)?
- Yes → Timestream (cheapest & fastest for this)
4. Real Hyderabad Example – Full Application Stack (2026 Typical)
Your food discovery & delivery startup in Madhapur:
| Component | Database Choice | Why This One? (2026 reasoning) | Approx Monthly Cost |
|---|---|---|---|
| User profiles, orders, restaurants | Aurora PostgreSQL (Multi-AZ) | ACID transactions, relational joins, strong consistency | ₹8,000–18,000 |
| Session store, trending restaurants | ElastiCache Redis | Sub-millisecond reads, pub/sub for real-time updates | ₹3,000–8,000 |
| User preferences, cart items (flexible schema) | DynamoDB (on-demand) | Automatic scaling, single-digit ms latency, pay-per-request | ₹2,000–7,000 |
| Daily / weekly business reports, user analytics | Redshift Serverless | Fast aggregations on large data, pay only when querying | ₹5,000–15,000 |
| Application logs, metrics over time | Timestream | Cheap time-series ingestion & querying | ₹1,000–4,000 |
| Old order receipts (PDFs), compliance data | S3 + Glacier | Very cheap long-term storage | ₹500–1,500 |
Total database/storage bill (moderate traffic): ~₹20,000–55,000/month → Very reasonable for a scaling app serving thousands of daily orders
5. Quick Hands-On Feel – Create Your First Aurora & DynamoDB
- Aurora PostgreSQL (relational)
- RDS console → Create database → Aurora → PostgreSQL
- Choose serverless v2 → min 0.5 → max 8 ACUs
- Wait 5–10 min → get endpoint → connect with pgAdmin / DBeaver
- DynamoDB (NoSQL)
- DynamoDB console → Create table
- Table name: “Users”
- Partition key: userId (String)
- Create → add item → query it → see single-digit ms response
Summary Table – AWS Databases Quick Decision Guide (2026 – India Focus)
| Your Requirement | First Choice | Second Choice | Why First Choice Wins in Hyderabad 2026 |
|---|---|---|---|
| Traditional SQL app (e-commerce, CMS, SaaS) | Aurora PostgreSQL | RDS PostgreSQL | 3–5× faster than RDS, auto-scales |
| High-scale key-value / document (carts, sessions) | DynamoDB | DocumentDB (if Mongo needed) | Serverless, infinite scale, low latency |
| Super-fast caching / real-time | ElastiCache Redis | DynamoDB Accelerator (DAX) | Sub-ms reads, pub/sub |
| Analytics / BI / reporting | Redshift Serverless | Athena (on S3) | Pay-per-query, no cluster management |
| Time-series (metrics, IoT, logs) | Timestream | OpenSearch | Cheapest & fastest for time-based data |
Teacher’s final note: AWS Databases are never “one database to rule them all” — they are specialized tools. In 2026, almost every successful Hyderabad startup uses 2–4 different database services in the same application — usually Aurora or RDS + DynamoDB + ElastiCache + S3 for files.
Once you stop trying to force every problem into one engine, your architecture becomes faster, cheaper, more scalable, and much easier to hire developers for.
Got it? This is the “which database should I actually use?” master lesson.
Next?
- Deep comparison: Aurora PostgreSQL vs RDS PostgreSQL (why most teams switch to Aurora)
- Step-by-step: Build a real app with Aurora + DynamoDB + ElastiCache?
- Or how to estimate database costs for your traffic?
Tell me — next whiteboard ready! 🚀🗄️
