India's Most Complete SQL & Database Program — 2025

SQL & Database Design Mastery.

SELECT * FROM career WHERE skill = 'SQL' AND future = 'bright';

INFONEXUS IT Institute's SQL & Database course covers MySQL, PostgreSQL, Advanced Queries, Window Functions, Query Optimization, NoSQL, BigQuery, Snowflake, and Cloud Databases — from Basic to Professional in one industry-aligned program.

View Curriculum ↓
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Batch Starting Soon
16 Seats Remaining
BigQuery + Snowflake Included
Cloud DB Deployment Covered
Why Choose INFONEXUS

The Complete SQL & Database
Stack — All in One Program

INFONEXUS IT Institute is the only training center in Indore covering the full spectrum — from SQL fundamentals through cloud databases, data warehousing, NoSQL, and real-time analytics.

🗄️

MySQL & PostgreSQL Deep Dive

Go beyond basic SELECTs — master complex JOINs, window functions, CTEs, partitioning, and stored procedures in both MySQL 8.0 and PostgreSQL 16 as used in production environments.

⚡

Query Optimization & Execution Plans

Learn to read EXPLAIN ANALYZE output, understand query planners, design optimal indexes (composite, partial, covering), and reduce query times from seconds to milliseconds.

🌩️

BigQuery, Snowflake & Cloud DBs

Work with modern cloud data platforms — BigQuery for Google Cloud analytics, Snowflake's multi-cluster warehouse, AWS RDS, and Azure SQL — the tools that data engineers use daily in 2025.

🍃

NoSQL — MongoDB & Redis

Understand when to use relational vs non-relational databases. Build document stores with MongoDB aggregation pipelines and implement Redis for caching, sessions, and pub/sub messaging.

📊

dbt, Data Modeling & Warehousing

Transform raw data into analytics-ready models using dbt (data build tool), implement star and snowflake schema data warehouse designs, and connect to Power BI and Tableau for visualization.

🎯

Interview Prep & Portfolio Projects

SQL interview problems from FAANG, product companies, and analytics firms. 10 real-world database projects including e-commerce analytics, healthcare reporting, and social media data modeling.

SQL database training at INFONEXUS IT Institute Indore — MySQL PostgreSQL BigQuery course
5 Mo.// full_program
Program Overview

From First SELECT to
Cloud Database
Architect

INFONEXUS IT Institute's SQL program is structured as three progressive stages — each unlocking new capabilities and adding real database projects to your portfolio for job applications.

MySQL 8PostgreSQL 16MongoDBRedisBigQuerySnowflakeAWS RDSdbtPower BIAirflow
Course Curriculum

18 Modules. 3 Levels.
Structured Like a Production Database.

Every module maps directly to real data engineering and database developer job requirements — covering the tools and techniques hiring managers look for in 2025.

SQL and database design course INFONEXUS IT Institute — MySQL PostgreSQL BigQuery Snowflake training Indore

SQL & Database Design — Full Program

// 5 months · 3 levels · 18 modules · 10 live projects · cloud database deployment

🟢 basic_level — SQL Fundamentals, MySQL & Database Design
⏱ 6 Weeks
🗄️ 3 Portfolio Projects
🎓 Zero Experience Needed
📜 Foundation Certificate
01

Database Fundamentals & SQL Introduction

  • What is a database? RDBMS vs flat files — why structured data matters in business
  • Installing MySQL 8.0 and MySQL Workbench — navigating the IDE, running first queries
  • Database concepts: tables, rows, columns, primary keys, foreign keys, schemas
  • Data types in MySQL: INT, VARCHAR, TEXT, DATE, DATETIME, DECIMAL, BOOLEAN, ENUM
  • CREATE DATABASE, CREATE TABLE, DROP, TRUNCATE — DDL (Data Definition Language)
  • INSERT INTO, UPDATE, DELETE — DML (Data Manipulation Language) with safe practices
02

SELECT Queries — Filtering, Sorting & Operators

  • SELECT fundamentals: column selection, aliases (AS), DISTINCT, calculated columns
  • WHERE clause: comparison operators, logical AND/OR/NOT, operator precedence
  • Special operators: BETWEEN, IN, NOT IN, LIKE with wildcards (%, _), IS NULL, IS NOT NULL
  • ORDER BY: single column, multi-column sorting, ASC/DESC — sort NULL handling
  • LIMIT and OFFSET: pagination queries for large datasets
  • String functions: CONCAT, UPPER, LOWER, TRIM, SUBSTRING, LENGTH, REPLACE, REGEXP
03

Aggregate Functions, GROUP BY & HAVING

  • Aggregate functions: COUNT, SUM, AVG, MIN, MAX — NULL handling in aggregations
  • GROUP BY: single and multi-column grouping, understanding group-level data
  • HAVING clause: filtering groups after aggregation (vs WHERE for row-level filtering)
  • Date and time functions: NOW, CURDATE, DATE_FORMAT, DATEDIFF, TIMESTAMPDIFF
  • Math functions: ROUND, CEIL, FLOOR, ABS, MOD, POWER — analytical calculations
  • Conditional expressions: IF, IFNULL, NULLIF, COALESCE — handling business logic in SQL
04

JOINs — Combining Tables Effectively

  • INNER JOIN: matching rows from two tables — understanding the join predicate
  • LEFT JOIN & RIGHT JOIN: including all rows from one side, NULLs for non-matches
  • FULL OUTER JOIN: all rows from both tables — simulating in MySQL
  • CROSS JOIN: Cartesian product — use cases (calendar generation, permutations)
  • SELF JOIN: joining a table to itself for hierarchical data (employee-manager, tree)
  • Multi-table JOINs: joining 3+ tables with aliasing strategy, avoid ambiguous columns
05

Database Design & Normalization

  • Entity-Relationship (ER) modeling: entities, attributes, relationships, cardinality
  • Converting ER diagrams to physical table schemas — forward engineering in MySQL Workbench
  • Normalization theory: 1NF (atomic values), 2NF (no partial dependency), 3NF (no transitive)
  • BCNF: eliminating all dependency anomalies — practical real-world normalization
  • Relationships: one-to-one, one-to-many, many-to-many with junction tables
  • Project 1: Design and build a complete University Management System database from ER to queries
06

Subqueries, Views & Basic Constraints

  • Subqueries in SELECT, WHERE, FROM, and HAVING — correlated vs non-correlated
  • EXISTS and NOT EXISTS: efficient subquery alternatives to IN for large datasets
  • Views: CREATE VIEW, updatable views, using views for data security and abstraction
  • Integrity constraints: NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY with ON DELETE/UPDATE
  • CHECK constraints: column-level and table-level data validation rules
  • Project 2: E-Commerce Product Catalog Database — full schema + 50 analytical queries

🟢 Basic Outcome: Students design and build two complete production database schemas — University Management and E-Commerce Catalog — with complex joins, subqueries, views, and normalization. Deployment-ready on MySQL with full documentation.

🔵 advanced_level — PostgreSQL, Window Functions & Performance Tuning
⏱ 9 Weeks
🗄️ 4 Production Projects
🎓 Prerequisite: Basic Level
📜 Advanced Certificate
07

PostgreSQL — Advanced Relational Database

  • PostgreSQL 16 installation and configuration: psql CLI, pgAdmin 4, connection pooling
  • PostgreSQL-specific data types: JSONB, ARRAY, UUID, CIDR/INET, HSTORE, custom types
  • JSONB queries: ->, ->>, jsonb_each, jsonb_agg — querying semi-structured data in PostgreSQL
  • Full-text search in PostgreSQL: tsvector, tsquery, GIN indexes for search performance
  • Table partitioning: range, list, hash partitioning for large table management
  • PostgreSQL extensions: pgcrypto, pg_stat_statements, TimescaleDB for time-series data
08

Window Functions & Advanced Analytics

  • Window function fundamentals: OVER clause, PARTITION BY, ORDER BY within window
  • Ranking functions: ROW_NUMBER, RANK, DENSE_RANK, NTILE — real-world leaderboard queries
  • Lead/Lag analysis: LEAD, LAG for comparing current vs previous/next row values
  • Frame specification: ROWS BETWEEN, RANGE BETWEEN — rolling averages, running totals
  • Cumulative distribution: CUME_DIST, PERCENT_RANK — percentile analysis in datasets
  • FIRST_VALUE, LAST_VALUE, NTH_VALUE — time-series first/last-seen pattern queries
09

CTEs, Recursive Queries & Advanced Subqueries

  • Common Table Expressions (CTEs): WITH clause — improving readability and reusability
  • Multiple CTEs: chaining CTEs for complex multi-step analytical queries
  • Recursive CTEs: traversing hierarchical data — org charts, bill of materials, graph paths
  • LATERAL joins in PostgreSQL: row-by-row subquery processing — advanced use cases
  • Subquery factoring and query rewriting: converting nested subqueries to efficient joins
  • Project 3: HR Analytics Dashboard — window functions + CTEs over 1M-row employee dataset
10

Indexing Strategies & Query Optimization

  • How query optimizers work: cost-based planning, statistics, query plan selection
  • EXPLAIN and EXPLAIN ANALYZE: reading execution plans, understanding cost estimates
  • Index types: B-Tree, Hash, GIN (full-text), GiST (geometric), BRIN — choosing correctly
  • Composite indexes: column order strategy, index selectivity, covering index design
  • Partial indexes: indexing subsets of rows — reducing index size and improving performance
  • Query rewriting: avoiding sequential scans, function index usage, statistics updates
11

Stored Procedures, Functions, Triggers & Events

  • Stored procedures in MySQL and PostgreSQL: IN, OUT, INOUT parameters, flow control
  • User-defined functions (UDFs): deterministic vs non-deterministic, returning scalars and tables
  • Error handling: DECLARE HANDLER, SIGNAL, SQLEXCEPTION — robust procedure design
  • Triggers: BEFORE/AFTER INSERT/UPDATE/DELETE — audit logging, data validation triggers
  • Scheduled events in MySQL: CREATE EVENT for automated data cleanup, report generation
  • Project 4: Banking Transaction System — procedures + triggers + audit log + balance rules
12

Transactions, ACID Properties & Concurrency

  • ACID properties: Atomicity, Consistency, Isolation, Durability — real-world implications
  • Transaction control: BEGIN/START TRANSACTION, COMMIT, ROLLBACK, SAVEPOINT
  • Isolation levels: READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, SERIALIZABLE
  • Concurrency problems: dirty reads, phantom reads, lost updates — isolation level selection
  • Row-level vs table-level locking: SELECT FOR UPDATE, deadlock detection and prevention
  • MVCC (Multi-Version Concurrency Control) in PostgreSQL — how PostgreSQL handles concurrent writes

🔵 Advanced Outcome: Students deliver four production-grade database projects — HR Analytics with window functions, a Banking System with transactions and triggers, and optimized query suites with documented EXPLAIN plans — targeting Data Analyst and Database Developer roles.

🟣 professional_level — NoSQL, Cloud Databases, dbt & Data Warehousing
⏱ 8 Weeks
🗄️ 3 Capstone Projects
🎓 Prerequisite: Advanced Level
📜 Industry Cert + LOR
13

MongoDB — Document Database & Aggregation Pipeline

  • MongoDB architecture: documents, collections, databases — BSON types and schema flexibility
  • CRUD operations: insertOne/insertMany, find with projection, updateOne, deleteOne
  • Query operators: $eq, $gt, $in, $and, $or, $regex, $elemMatch, $all — array querying
  • Aggregation pipeline: $match, $group, $sort, $lookup (JOIN), $unwind, $project, $bucket
  • MongoDB indexes: single field, compound, text, TTL (time-to-live) for expiring data
  • When to use MongoDB vs PostgreSQL: schema design patterns, embedded vs referenced documents
14

Redis — In-Memory Data Store & Caching Patterns

  • Redis data structures: String, Hash, List, Set, SortedSet, HyperLogLog, Streams
  • Caching patterns with Redis: cache-aside, write-through, write-behind — TTL strategy
  • Redis Pub/Sub: publisher-subscriber messaging for real-time data pipelines
  • Redis Streams: event sourcing, consumer groups — streaming analytics use cases
  • Session management with Redis: user sessions, rate limiting with sliding window counters
  • Redis Sentinel and Cluster: high availability and horizontal scaling for production
15

BigQuery — Cloud-Scale SQL Analytics

  • BigQuery architecture: serverless, columnar storage, slot-based compute — cost model
  • BigQuery SQL: standard SQL + BigQuery extensions, STRUCT, ARRAY, UNNEST, WITH ROLLUP
  • Partitioned and clustered tables: optimizing queries on billion-row datasets for cost
  • BigQuery ML: training linear regression, logistic regression, k-means models with SQL
  • Scheduled queries, materialized views, and BI Engine for sub-second dashboard queries
  • Project 5: Google Analytics e-commerce data analysis — BigQuery + Looker Studio dashboard
16

Snowflake & Cloud Database Architecture

  • Snowflake architecture: virtual warehouses, storage separation, cloud-agnostic design
  • Snowflake-specific SQL: semi-structured data (VARIANT), FLATTEN, PARSE_JSON, LATERAL
  • Time travel and fail-safe: querying historical data, undoing accidental changes
  • AWS RDS: MySQL/PostgreSQL on RDS — Multi-AZ deployment, read replicas, parameter groups
  • Azure SQL & Cloud DB comparisons: choosing the right cloud database for workloads
  • Database cost optimization: query result caching, warehouse auto-suspend, data retention
17

dbt, Data Warehousing & Modern Data Stack

  • Data warehouse concepts: star schema, snowflake schema, slowly changing dimensions (SCD)
  • dbt (data build tool): models, materializations (table, view, incremental), seeds, sources
  • dbt tests: schema tests (unique, not_null, accepted_values), custom test development
  • dbt documentation: model descriptions, column docs, auto-generated DAG lineage graph
  • Modern data stack integration: Fivetran → Snowflake → dbt → Power BI / Looker pipeline
  • Apache Airflow basics: DAGs, operators, scheduling dbt runs in data engineering pipelines
18

SQL Interview Mastery, Portfolio & Career Launch

  • 150+ SQL interview problems: HackerRank, LeetCode SQL, StrataScratch — sorted by company
  • Common SQL interview patterns: running totals, Nth highest, consecutive data, gaps-and-islands
  • System design for databases: sharding strategies, replication, caching layer design
  • Project 6 Capstone: Full data pipeline — raw data → PostgreSQL → dbt → BigQuery → Power BI
  • Portfolio: GitHub SQL repositories, query documentation, data visualization portfolio
  • INFONEXUS placement: resume for SQL/data roles, mock technical interviews, 200+ company connects

🟣 Professional Outcome: Students build a complete modern data stack — PostgreSQL → dbt → BigQuery → Power BI — plus MongoDB, Redis, and Snowflake experience, 150+ solved SQL interview problems, and a production-grade capstone pipeline targeting Data Engineer and Database Architect roles at ₹10–35 LPA.

Career Outcomes

Roles You'll Query Your Way Into

SQL is the #1 required skill in data and engineering job listings. INFONEXUS graduates are placed at analytics firms, tech companies, fintech startups, and MNCs.

🗄️
SQL Developer
₹4–10 LPA
📊
Data Analyst
₹5–12 LPA
🏗️
Data Engineer
₹10–22 LPA
🛡️
Database Admin
₹8–18 LPA
📈
BI Developer
₹8–16 LPA
🏛️
Data Architect
₹18–35 LPA
Our Faculty

Trained by Database Engineers Who Ship Real Systems

Every INFONEXUS SQL trainer is an active data professional with verified production database experience at top tech companies and analytics firms.

Nandita Rao — Data Analytics & BI Trainer INFONEXUS

Nandita Rao

// Analytics & Power BI Expert

4+ Yrs
Alumni Success

1800+ Data Professionals.
All Querying Their Dream Jobs.

Verified placement outcomes from INFONEXUS SQL graduates at top analytics firms, product companies, and data engineering teams.

★★★★★
"

The window functions module at INFONEXUS finally made RANK, LEAD, and LAG click for me. I used them in a HackerRank assessment for my Amazon interview — the exact same patterns from class. The query optimization section is the most thorough I've found anywhere — EXPLAIN ANALYZE became my daily tool.

Adarsh INFONEXUS alumni
Adarsh Mishra
// Data Analyst
₹5 LPA
★★★★★
"

INFONEXUS's dbt + BigQuery + Snowflake Professional level is what separates this from every other SQL course I've seen. Most courses stop at MySQL joins. INFONEXUS takes you all the way to modern data stack deployment. Built my first real Airflow + dbt + BigQuery pipeline in Week 2 of Professional level.

Pooja INFONEXUS alumni
Pooja Sharma
// Data Engineer
₹4.5 LPA
★★★★★
"

I was a finance professional who wanted to switch to data analytics. INFONEXUS's Basic level made SQL accessible, and by Advanced level I was writing CTEs and window functions confidently. Got placed as a Business Intelligence Developer at a fintech firm 3 months after completing the program. The SQL interview preparation is exceptional.

Harshit INFONEXUS alumni
Harshit Gupta
// BI Developer
₹6 LPA
FAQ

Questions About the
SQL Course

Honest answers to everything prospective students ask before joining INFONEXUS IT Institute's SQL & Database Design program.

No prior programming or database experience is required. The Basic level starts from scratch — what databases are, how to install MySQL, and writing first SELECT statements. Students from finance, commerce, engineering, science, and non-technical backgrounds all successfully complete the program. Basic comfort with a computer is the only prerequisite.
INFONEXUS covers five database categories: Relational SQL (MySQL 8.0, PostgreSQL 16), Cloud Analytical Databases (Google BigQuery, Snowflake), Cloud Managed DBs (AWS RDS, Azure SQL), Document NoSQL (MongoDB), and In-Memory/Cache (Redis). The course also covers dbt for data transformation, Airflow for orchestration, and Power BI for visualization — giving students the complete modern data stack.
dbt (data build tool) is the industry standard for transforming raw data in data warehouses. Every modern data engineering team uses dbt to write SQL models that are version-controlled, tested, and documented. INFONEXUS includes dbt because data engineer job descriptions in 2025 almost universally list dbt as a required skill. The Professional level covers full dbt workflows including models, tests, documentation, and Airflow scheduling.
Yes. Module 18 is dedicated to SQL interview preparation — covering 150+ problems from HackerRank, LeetCode SQL, and StrataScratch organized by company (Amazon, Google, Uber, Flipkart, Razorpay). Common interview patterns covered include: running totals with window functions, Nth highest salary, consecutive data (gaps-and-islands), and pivot queries. INFONEXUS also provides mock technical interviews with feedback and company-specific preparation guidance.
Yes. INFONEXUS offers live online classes via Zoom and offline classes at the Indore campus. All sessions are recorded with 12-month playback access. Weekday batches (Mon-Fri, 7-9 PM) and weekend batches (Sat-Sun, 10 AM-2 PM) are available to accommodate working professionals. Students get cloud access to PostgreSQL and BigQuery sandboxes, MySQL Workbench setups, and a MongoDB Atlas free-tier account for hands-on practice throughout the course.
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That Actually Scales.

Join 1800+ INFONEXUS database professionals working at data analytics firms, product companies, and cloud data teams. Your free demo class costs zero.

✓ MySQL + PostgreSQL ✓ BigQuery + Snowflake ✓ dbt + Data Warehousing ✓ 10 Live Projects → ₹10 LPA Max Package
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