Course: System Design — Zero to Hero
Level: Beginner → Intermediate
Phase: Database Fundamentals
Status: Complete
In this chapter, we build the database foundation required for system design.
You will learn:
The goal is not merely to learn SQL syntax.
The goal is to understand how databases behave as system-design components.
Imagine an application that stores users.
Without persistent storage, the application might keep data in memory:
users = [
{ id: 1, name: "Anubhav" },
{ id: 2, name: "Rahul" }
]This works temporarily.
But what happens when:
In-memory data is not enough.
We need a persistent storage system.
That is where databases come in.
A database is a system for storing, organizing, retrieving, and modifying data.
At a high level:
Application
|
v
Database
|
+---- Store data
+---- Retrieve data
+---- Update data
+---- Delete data
+---- Protect data integrity
+---- Manage concurrent accessA database provides much more than storage.
It also provides mechanisms for:
A beginner may ask:
Why not simply save everything into files?
You can.
For a small application:
users.json
products.json
orders.jsonmight work.
But large applications need more sophisticated capabilities.
Suppose we have:
users.jsoncontaining:
[
{
"id": 1,
"name": "Anubhav",
"email": "anubhav@example.com"
}
]Searching for a user may require reading a large portion of the file.
Now imagine:
100 million usersThe problem becomes much harder.
A database can maintain indexes that allow efficient lookup.
It can also coordinate concurrent reads and writes.
A production database helps solve several problems.
Data survives application restarts.
Application restart
|
v
Database still contains dataInstead of scanning every record, indexes can make targeted queries much faster.
Many users can access the system simultaneously.
User A ----\
User B -----\
User C ------> Database
User D -----/
User E ----/The database manages concurrent operations according to its consistency and transaction model.
Databases can enforce rules such as:
email must be unique
user_id cannot be NULL
order must reference an existing userA database can group multiple operations into one logical unit.
Example:
Transfer ₹1,000
Debit Account A
Credit Account BWe generally don't want:
Debit succeeds
Credit failsA transaction can make the operations succeed or fail together according to the database's transaction semantics.
Databases provide mechanisms for recovering from failures.
Examples include:
A relational database organizes data into tables.
For example:
Users
+----+----------+---------------------+
| id | name | email |
+----+----------+---------------------+
| 1 | Anubhav | anubhav@example.com |
| 2 | Rahul | rahul@example.com |
| 3 | Priya | priya@example.com |
+----+----------+---------------------+A table contains:
A row represents one record.
Example:
1 | Anubhav | anubhav@example.comThis represents one user.
You can think of a row as:
One instance of an entity.
For example:
User #1
User #2
User #3A column represents an attribute.
For example:
id
name
email
created_atEach user has values for those attributes.
Example:
id = 1
name = Anubhav
email = anubhav@example.comA table is a structured collection of records.
Example:
users
orders
products
payments
reviewsEach table normally represents an entity or a useful relationship.
A schema describes the structure and rules of stored data.
For example:
users
id INTEGER
name VARCHAR
email VARCHAR
created_at TIMESTAMPThe schema defines things such as:
Conceptually:
Database
|
+-- users
| +-- id
| +-- name
| +-- email
|
+-- orders
| +-- id
| +-- user_id
| +-- amount
|
+-- productsA primary key uniquely identifies a row.
Example:
users
id | name
---+---------
1 | Anubhav
2 | Rahul
3 | PriyaHere:
idis the primary key.
The important property is uniqueness.
id = 1should identify exactly one user.
Suppose an application receives:
GET /users/123The database needs an efficient way to locate user 123.
A primary-key lookup is usually an important access pattern.
Primary keys also become references from other tables.
Example:
users
-----
id = 123Then:
orders
------
id = 9001
user_id = 123The order belongs to user 123.
Common identifiers include:
1
2
3
4Advantages:
Potential concerns in distributed systems:
Example:
550e8400-e29b-41d4-a716-446655440000Advantages:
Potential concerns:
Applications may use identifiers such as:
order_01J...
user_...
payment_...The important system-design question is not:
Which ID is universally best?
Instead ask:
What are the application's generation, ordering, indexing, storage, and distribution requirements?
A foreign key represents a relationship between records.
Example:
users
id
---
1
2
3And:
orders
id | user_id
---+--------
10 | 1
11 | 1
12 | 2orders.user_id references users.id.
Conceptually:
User
|
+---- Order
|
+---- OrderThere are three common relationship types.
One record corresponds to one record.
Example:
User ---- ProfileA user may have one profile.
One record can have many related records.
Example:
User
|
+---- Order
|
+---- Order
|
+---- OrderThis is extremely common.
Many records relate to many other records.
Example:
Students <----> CoursesA student can enroll in many courses.
A course can contain many students.
Usually, a junction table is introduced:
students
courses
student_coursesExample:
student_courses
student_id | course_id
-----------+----------
1 | 101
1 | 102
2 | 101Constraints help protect data integrity.
Common constraints include:
Ensures a unique row identifier.
Example:
email UNIQUEPrevents duplicate values.
Requires a value.
name NOT NULLEnforces a condition.
Example:
price >= 0Maintains a relationship between tables.
CRUD represents the four basic data operations.
C = Create
R = Read
U = Update
D = DeleteExample:
INSERT INTO users (name, email)
VALUES ('Anubhav', 'anubhav@example.com');SELECT * FROM users;UPDATE users
SET name = 'Anubhav Chaudhary'
WHERE id = 1;DELETE FROM users
WHERE id = 1;These operations form the foundation of application data access.
A database becomes powerful when we can retrieve exactly what we need.
Example:
SELECT id, name
FROM users
WHERE email = 'anubhav@example.com';The database may use an index to avoid scanning every row.
This brings us to one of the most important system-design concepts:
An index is an additional data structure that helps a database find rows efficiently.
Think of a book.
Without an index:
Read every page
until you find the topicWith an index:
Topic
|
v
Page numberA database index works with the same broad idea.
Suppose:
users = 100 million rowsWe run:
SELECT *
FROM users
WHERE email = 'anubhav@example.com';Without a suitable index, the database may need to examine many rows.
Conceptually:
Row 1 check
Row 2 check
Row 3 check
...
Row 100M checkThis is a scan.
Suppose we create:
CREATE INDEX idx_users_email
ON users(email);The database now has an additional structure organized for lookup.
Conceptually:
email
|
+---- anubhav@example.com --> row location
+---- rahul@example.com --> row location
+---- priya@example.com --> row locationThe exact implementation depends on the database and index type.
Indexes are not free.
They can improve reads but increase costs elsewhere.
More indexes
|
+--> faster eligible reads
|
+--> more storage
|
+--> additional write/update work
|
+--> more memory/cache pressureTherefore:
Do not create an index for every column.
Design indexes around real query patterns.
Suppose the application frequently runs:
SELECT *
FROM orders
WHERE user_id = 123
AND created_at >= '2026-01-01';A composite index may be useful:
(user_id, created_at)The order of columns matters.
Indexes should be designed around access patterns rather than simply listing every field.
An index is often more useful when the indexed condition narrows the result set substantially.
Suppose:
genderhas only:
Male
Femalewhile:
emailis almost unique.
An email lookup generally has much higher selectivity than a gender lookup.
This does not mean a low-cardinality column can never be indexed.
The actual usefulness depends on:
A common beginner mistake is:
"Let's add indexes to important columns."
A better approach is:
System design is workload-driven.
A transaction groups operations into a logical unit of work.
Consider money transfer:
Account A = ₹10,000
Account B = ₹5,000Transfer:
₹1,000Desired final state:
A = ₹9,000
B = ₹6,000We do not want:
A = ₹9,000
B = ₹5,000because money disappeared.
Nor:
A = ₹10,000
B = ₹6,000because money appeared.
A transaction helps coordinate the operations.
Conceptually:
BEGIN
Debit A
Credit B
COMMITIf something fails:
ROLLBACKACID is one of the most important database concepts for system design interviews.
ACID stands for:
A = Atomicity
C = Consistency
I = Isolation
D = DurabilityAtomicity means a transaction is treated as one logical unit.
Conceptually:
Transaction
Operation A
Operation B
Operation C
All succeed
OR
Transaction fails/rolls backIt prevents an incomplete transaction from being treated as successfully completed.
Consistency means transactions move the database between valid states according to its defined constraints and rules.
For example:
balance >= 0if such a constraint/business rule is enforced.
A transaction should not leave the database violating applicable integrity constraints.
Important:
Database consistency in ACID is not the same concept as distributed-system consistency discussed later in this course.
This distinction is an important interview point.
Multiple transactions may execute concurrently.
Example:
Transaction A
Transaction B
Transaction CIsolation controls how concurrent transactions interact and what intermediate states they can observe.
Different isolation levels provide different guarantees.
Common SQL isolation levels include:
Read Uncommitted
Read Committed
Repeatable Read
SerializableExact behavior varies by database engine.
After a transaction is successfully committed, its effects are intended to survive failures according to the database's durability guarantees.
Conceptually:
COMMIT
|
v
Database failure
|
v
Committed data remains recoverableDurability commonly involves mechanisms such as:
Replication alone should not automatically be equated with durability.
A useful interview memory trick:
Atomicity -> All or nothing
Consistency -> Valid state
Isolation -> Concurrent transactions don't improperly interfere
Durability -> Committed data survives failures according to guaranteesConcurrent transactions can produce anomalies depending on the isolation model.
Important examples include:
Transaction B reads data written by transaction A before A commits.
A writes X
B reads X
A rolls backB observed data that did not ultimately commit.
A transaction reads the same row twice and gets different committed values because another transaction changed it between reads.
A transaction repeats a query and sees a different set of matching rows because another transaction inserted or deleted rows.
Not every operation needs a large transaction.
Consider:
Create userversus:
Place order
Reserve inventory
Create payment record
Update order stateThe second workflow has stronger coordination requirements.
System design requires asking:
Which operations must succeed together?
That question helps determine transaction boundaries.
Normalization organizes relational data to reduce unnecessary duplication and update anomalies.
Suppose we store:
orders
order_id
customer_name
customer_email
product_name
product_priceIf a customer has 1,000 orders, their information may be repeated many times.
We might separate:
customers
orders
productsand connect them with identifiers.
Normalization can reduce:
For example, if a customer changes their email:
Without normalization:
Update 1,000 order recordsWith normalization:
Update customer record onceNormalization is not automatically always better.
Highly normalized schemas may require more joins.
For read-heavy systems, joins can become expensive or inconvenient depending on workload and architecture.
This leads to:
Denormalization intentionally duplicates some data to improve access patterns.
Example:
Instead of:
orders
|
+--> customerswe might store:
orders
order_id
customer_id
customer_name
customer_emailThis duplicates customer data.
Why?
Perhaps the application frequently needs:
Order + customer display informationand wants to avoid an additional lookup.
| Concept | Normalization | Denormalization |
|---|---|---|
| Duplication | Lower | Higher |
| Consistency management | Often simpler | More complex |
| Reads | May require joins | Can be faster/simpler |
| Writes | Less duplicated data | May require multiple updates |
| Storage | Usually lower | Usually higher |
| Typical use | Transactional models | Read-heavy/access-pattern optimized models |
The important system-design lesson:
Model the data around correctness and access patterns.
SQL databases are relational databases that generally organize data into tables and support SQL for querying.
Examples include:
A typical model:
Users
Orders
Products
Paymentswith relationships between them.
Relational databases are especially useful when you need:
Example:
E-commerce
Customer
|
+---- Orders
|
+---- Order Items
|
+---- ProductsRelational modeling naturally represents these relationships.
NoSQL is a broad family of non-relational database technologies.
Common categories include:
Document
Key-Value
Wide-Column
GraphExamples:
MongoDB -> Document
Redis -> Key-Value
Cassandra -> Wide-Column
Neo4j -> GraphThese systems have different data models and operational characteristics.
"NoSQL" does not mean:
No structure.
It generally means the system is not based on the traditional relational model.
A document database stores records as documents.
Example:
{
"id": 101,
"name": "Anubhav",
"email": "anubhav@example.com",
"skills": [
"JavaScript",
"Node.js",
"System Design"
]
}This can be useful when an entity naturally maps to a document.
The model is approximately:
key -> valueExample:
user:123 -> {...}
session:abc -> {...}They are often useful for:
Redis is a common example.
Wide-column databases are designed around distributed storage and query patterns involving large datasets.
A common example is Cassandra.
They can be useful for workloads requiring:
The schema and query model should be designed around access patterns.
Graph databases represent:
Nodes
Edges
PropertiesExample:
Anubhav
|
| follows
v
Rahul
|
| follows
v
PriyaThey can be useful for relationship-heavy queries such as:
Avoid the beginner rule:
SQL is old and NoSQL is modern.
That is incorrect.
The correct question is:
Which data model and operational characteristics fit the workload?
Consider:
| Requirement | Often a natural fit |
|---|---|
| Complex relationships | Relational |
| Strong transactional workflows | Relational |
| Flexible document-shaped data | Document |
| Extremely fast key lookup/cache | Key-value |
| Very large distributed write workload with known access patterns | Wide-column |
| Relationship traversal | Graph |
These are general tendencies, not absolute rules.
Suppose you are designing a payment system.
Important requirements might include:
Correctness
Transactions
Auditability
Consistency
DurabilityA relational database may be a natural candidate.
Now consider a session store:
session_id -> session_dataA key-value system may be a natural candidate.
Now consider social relationships:
User A follows User B
User B follows User C
User C follows User DA graph-oriented approach may be useful for certain traversal-heavy workloads.
System design depends heavily on the read/write ratio.
Example:
100,000 reads/sec
10,000 writes/secPossible techniques:
Example:
10,000 reads/sec
100,000 writes/secPossible techniques:
The exact solution depends on workload characteristics.
Applications generally do not want to establish a brand-new database connection for every request.
That would create overhead.
Instead, applications often use a connection pool.
Application
|
v
Connection Pool
| | | |
v v v v
Database connectionsRequests borrow connections from the pool and return them when finished.
Suppose:
10,000 application requests/secbut the database can only handle a much smaller number of concurrent connections efficiently.
If every request creates a connection:
Connection storm
|
v
Database overloadA bounded pool can provide backpressure.
Example:
Pool size = 100Requests beyond the available connections may wait, time out, or be rejected according to application behavior.
An important distinction:
Connection poolcontrols how the application uses database connections.
It does not magically increase database capacity.
If the database is already overloaded:
Increase pool from 100
to
1,000may make the problem worse.
More concurrency can create more database contention.
Databases can become bottlenecks because of:
Complex queries consume CPU.
Insufficient memory can increase disk I/O.
Large scans or write-heavy workloads can stress storage.
Concurrent transactions may wait for one another.
Too many active connections can overload the database.
Queries may perform expensive scans.
Examples:
SELECT *when only a few fields are needed.
Or queries that unintentionally scan huge datasets.
A disproportionate amount of traffic targets the same record or partition.
Application servers are frequently easy to scale horizontally:
Server 1
Server 2
Server 3
Server 4The database is harder because state must be coordinated.
You cannot always solve database scaling by simply adding another identical server.
This is why system design eventually requires:
We will study these in later chapters.
Replication means maintaining copies of data across multiple database nodes.
Conceptually:
+--> Replica 1
Primary -----+
+--> Replica 2Potential benefits:
But replication introduces questions:
These are later system-design topics.
Partitioning divides data into smaller pieces.
For example:
Users 0–99M
|
+---- Partition A
+---- Partition B
+---- Partition CPartitioning can improve scalability and manageability.
When partitioning is distributed across database nodes, it is commonly called sharding.
We will study this deeply later.
This is a common interview question.
Defines the primary identity of a row.
Provides an access path that can make queries faster.
A primary key is generally backed by an index in relational database systems, but:
Not every index is a primary key.
Example:
Primary key:
id
Additional indexes:
email
created_at
status
(user_id, created_at)Another common confusion.
Helps the database locate data efficiently.
Query
|
v
Database index
|
v
Stored row/dataStores frequently used results/data closer to the application.
Application
|
v
Cache
|
+--> hit -> return
|
+--> miss -> DatabaseAn index and cache solve different problems.
They are often used together.
Suppose:
User profileis requested 100,000 times.
The database can serve it.
But if the same data is repeatedly requested, caching can reduce database load.
Without cache:
100,000 requests
|
v
Database
|
v
High loadWith cache:
100,000 requests
|
v
Cache
|
+---- Most requests -> cache
|
+---- Misses -> databaseThis connects directly to the previous CDN/caching chapter.
A cache is generally not a replacement for the system of record.
For example:
Database -> source of truth
Cache -> performance layerIf cached data disappears:
Cache failure
|
v
Fetch from databaseThis is one reason cache architecture should define:
For many transactional applications:
Application
|
+--> Cache
|
+--> DatabaseThe database may be the authoritative source.
For example:
Product price = ₹999The cache might contain:
₹999but the database is generally the durable record.
This distinction becomes critical when designing consistency behavior.
A common read architecture:
Client
|
v
Application
|
v
Cache
|
+---- Hit ----> Response
|
+---- Miss
|
v
Database
|
v
Cache
|
v
ResponseThis is called a cache-aside pattern when the application explicitly loads data into the cache after a miss.
A simplified write flow:
Client
|
v
Application
|
v
Database
|
v
Cache update/invalidationThe exact order matters.
If:
Database updated
Cache not invalidatedthe application may serve stale data.
If:
Cache updated
Database write failsthe cache could temporarily contain data that was never committed.
Therefore, cache/database consistency requires deliberate design.
A single database instance creates a single failure domain.
Application
|
v
Database
X
failurePotential result:
Application unavailableProduction architectures may use:
Primary
|
+---- Replica
|
+---- Replicaalong with failover mechanisms.
Availability design introduces trade-offs around:
This is an important distinction.
Maintains additional copies, often for availability or read scaling.
Creates recoverable historical copies for restoration.
If bad data is accidentally written:
Primary
|
+--> Replicathe bad change may replicate.
A backup may allow recovery to an earlier point.
Therefore:
Replication is not a substitute for backups.
Consider:
Application
|
v
DatabasePossible failures include:
Database process crashes
Disk fails
Network partition
CPU saturation
Memory exhaustion
Connection exhaustion
Long-running query
Lock contention
Replication lag
Corrupted dataA robust system considers failure explicitly.
Never assume a database query will always return quickly.
A query can become slow because of:
Applications should use appropriate:
Connection timeout
Query timeout
Request timeoutWithout timeouts:
Slow database
|
v
Requests wait
|
v
Connection pool fills
|
v
More requests wait
|
v
Application becomes unhealthyThis is a cascading failure pattern.
Suppose the database is overloaded.
Application receives:
timeoutand immediately retries:
request
retry
retry
retry
retryNow database load increases further.
This can produce a retry storm.
Use:
We will revisit this in distributed systems.
Imagine:
product_id = 123is extremely popular.
Millions of requests target:
product:123or a single database row.
Even if the database is large, one record or partition can become a bottleneck.
This is called a hot key/hot partition pattern.
Potential solutions include:
The correct solution depends on the workload.
Do not start with:
What tables should I create?
Start with:
What does the application need to do?
For an e-commerce application:
Find product by ID
Search products
Find orders by user
Find order by ID
Update inventory
Create payment
List recent ordersThen design:
around those access patterns.
A simplified relational model:
Users
-----
id
name
email
Products
--------
id
name
price
inventory
Orders
------
id
user_id
status
created_at
OrderItems
----------
id
order_id
product_id
quantity
priceRelationships:
User
|
+---- Orders
|
+---- OrderItems
|
+---- ProductPossible access patterns:
Find user by email
Find orders by user
Find order by ID
Find order items by order
Find product by IDPossible indexes:
users(email)
orders(user_id, created_at)
order_items(order_id)The exact index design should be validated against actual queries and database behavior.
Suppose:
short_code -> long_urlA table could look like:
urls
id
short_code
long_url
created_at
user_idCritical query:
Find URL by short_codeTherefore:
short_codeneeds an efficient lookup path, usually with a uniqueness constraint/index.
Read workload may be much larger than write workload.
That suggests possible future techniques:
Database
|
+--> Cache
|
+--> Read replicasThis connects database design to system scaling.
When choosing a database, ask:
Relational?
Document?
Key-value?
Wide-column?
Graph?Point lookup?
Range query?
Join?
Aggregation?
Graph traversal?Strong?
Eventual?
Application-defined?Single-row?
Multi-row?
Multi-table?
Cross-service?Read-heavy?
Write-heavy?
Mixed?10K rows?
100M rows?
10B rows?Milliseconds?
Seconds?
Batch?Single region?
Multi-zone?
Multi-region?Backups
Monitoring
Failover
Migration
Capacity managementNot necessarily.
Performance depends on:
No.
Indexes consume storage and add work to writes.
No.
Replication and backup solve different problems.
Sometimes.
But database scaling can involve:
Vertical scaling
Caching
Read replicas
Partitioning
Sharding
Query optimization
Denormalization
Storage optimizationNot exactly.
The word "consistency" appears in multiple contexts.
Always explain which consistency model you mean.
No.
Too many connections can overwhelm the database.
Usually not for durable transactional state.
Not necessarily.
For small tables or queries returning a large percentage of rows, a sequential scan can sometimes be more appropriate.
The database optimizer decides based on statistics and cost estimates.
Remember this architecture:
+----------------+
| Application |
+-------+--------+
|
v
+----------------+
| Connection |
| Pool |
+-------+--------+
|
v
+----------------+
| Database |
+----------------+
| | |
v v v
Tables Indexes Transactions
|
v
StorageAt scale:
Application
|
+-------+-------+
| |
Cache Connection Pool
|
v
+---------------+
| Primary DB |
+-------+-------+
|
+----------+----------+
| |
v v
Replica 1 Replica 2Later:
Application
|
v
Router
|
+----------+----------+
| | |
Shard 1 Shard 2 Shard 3This is the progression we will build throughout the course.
Suppose we are designing:
Food delivery applicationRequirements:
Millions of users
High read traffic
Frequent order writes
Payment transactions
Restaurant inventory
Location updatesWe might use different storage technologies for different workloads.
Conceptually:
Users / Orders / Payments
|
v
Relational DB
Hot product/restaurant data
|
v
Cache
Location / high-frequency event data
|
v
Specialized system
Analytics
|
v
Data warehouse/lakeThe key principle:
One system does not have to store every kind of data.
A large architecture can use multiple specialized storage systems.
A database rarely exists alone.
A realistic architecture might be:
Client
|
v
CDN
|
v
Load Balancer
|
v
Application Servers
|
+---------> Cache
|
+---------> Database
|
+---------> Message Queue
|
v
Worker ServicesThe database is one component in the complete system.
A system for persistent storage, retrieval, modification, and management of data.
A column or set of columns that uniquely identifies a row.
A field that references a key in another table to represent a relationship.
An auxiliary data structure that can make eligible queries more efficient.
A logical unit of database work with defined transactional guarantees.
Atomicity
Consistency
Isolation
DurabilityBecause indexes consume storage and can increase write/update cost and maintenance overhead.
Compare based on:
To reuse a bounded set of database connections and avoid creating a new connection for every request.
Structuring relational data to reduce unnecessary duplication and anomalies.
Because all application servers may converge on the same database for shared state.
10 App Servers
|
v
One Database
|
v
Potential bottleneckPotential techniques:
Caching
Read replicas
Indexes
Query optimization
Denormalization
CDN for suitable data
Partitioning where appropriatePotential techniques:
Batching
Asynchronous processing
Partitioning
Sharding
Write-optimized data modeling
Reducing unnecessary indexes
Queue-based bufferingBecause more concurrent work can increase:
Potential chain:
Slow query
|
v
Request waits
|
v
Connection remains occupied
|
v
Pool fills
|
v
New requests wait
|
v
Timeouts
|
v
Retry storm
|
v
System instabilityThis is a classic distributed-system failure pattern.
Design the database model for a blogging platform.
Requirements:
Users can create posts.
Posts can have comments.
Users can like posts.
Posts can have multiple tags.
Users can follow other users.Create:
users
posts
comments
likes
tags
post_tags
followsThen identify:
Design a simplified e-commerce database.
Users can:
Register
Browse products
Create orders
Add products to orders
Pay for orders
View order historyCreate these entities:
users
products
orders
order_items
paymentsDesign:
Primary keys
Foreign keys
Constraints
IndexesThen answer:
Find product by ID.
Find all orders for a user.
Find a specific order.
Find all items in an order.
Find recent orders for a user.
For each query, ask:
What index would support this access pattern?
Before considering a database design production-ready, ask:
You now have the foundation needed to reason about databases in system design.
Key concepts:
Database
|
+-- Tables
| +-- Rows
| +-- Columns
|
+-- Keys
| +-- Primary
| +-- Foreign
|
+-- Constraints
|
+-- Indexes
|
+-- Transactions
| +-- ACID
|
+-- Data Models
+-- Relational
+-- Document
+-- Key-Value
+-- Wide-Column
+-- GraphAt system-design scale:
Database
|
+-- Query optimization
+-- Indexing
+-- Connection pooling
+-- Caching
+-- Replication
+-- Partitioning
+-- Sharding
+-- Backups
+-- Recovery
+-- AvailabilityThe most important mindset is:
Do not choose a database because it is popular. Choose the data model and architecture based on workload, access patterns, consistency, transaction, availability, and scaling requirements.
Before moving forward, you should be able to explain:
Next we move from database fundamentals into SQL databases in depth.
We will learn:
SQL database architecture
↓
Tables and schemas
↓
SELECT / INSERT / UPDATE / DELETE
↓
WHERE / ORDER BY / GROUP BY
↓
JOINs
↓
Aggregations
↓
Subqueries
↓
Transactions
↓
Isolation levels
↓
Query execution
↓
EXPLAIN / query plans
↓
Indexes in SQL databases
↓
Constraints
↓
Database design
↓
Production SQL patternsWe will also start connecting SQL concepts directly to system-design performance and scalability decisions.
Mark it as complete to track your mastery.