Oracle SQL & PL/SQL

SQL databases are relational and structured, making them ideal for applications that require well-defined relationships, data consistency, and reliable transactions. NoSQL databases , on the other hand, are non-relational and offer greater flexibility, making them well-suited for handling large volumes of rapidly changing, semi-structured, or unstructured data. In simple terms: Choose SQL when data relationships, accuracy, and transactional integrity are critical. Choose NoSQL when you need flexibility, high scalability, and fast performance for large or evolving datasets.  📌 When to Use SQL Transactional systems : Banking, e-commerce checkout, payroll. Complex queries & reporting : Business intelligence, analytics dashboards. Stable schema : Customer records, inventory management. Strong consistency required : Financial transactions, compliance-heavy industries. 📌 When to Use NoSQL High scalability needs : Social media feeds, IoT sensor data, gaming leaderboards. Unstru...

1> WHERE Clause Filters rows before grouping. Works with non-aggregate conditions (like simple comparisons on columns). Eg : SELECT employee, bonus FROM emp_bonus WHERE bonus > 5000; 2>  HAVING Clause Filters groups after aggregation. Works with aggregate functions (SUM, AVG, MAX, MIN, COUNT). Eg : SELECT employee, SUM(bonus) FROM emp_bonus GROUP BY employee HAVING SUM(bonus) > 5000; 💡 In Short :  WHERE = "Which rows should I include in the group?" HAVING = "Which groups should I keep after aggregation?"

SQL (Structured Query Language) is essential for a Business Analyst because it bridges the gap between business understanding and data-driven decision-making and it allows you to access, analyze and validate business data without depending entirely on technology teams. Here are few main reasons : 💡 Key Reasons  💡 Data Access: Business analysts often need data from databases. SQL lets BAs directly query databases to extract relevant information without waiting for developers. Requirement Validation: Helps verify whether system data aligns with business requirements or not. Trend & Insight Analysis: Enables quick checks on KPIs, customer behavior, and operational metrics. SQL functions like SUM(), COUNT(), AVG(), and GROUP BY help summarize data Reporting & Dashboards: SQL powers BI tools (like Power BI, Tableau) by providing clean, structured datasets. Root Cause Analysis: When issues arise, SQL helps trace data inconsistencies or process bottlenecks. SQL Topics Busine...

𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 📈 → Sits between business needs and technical teams → Asks: "What does the business need to perform better?" → Focuses on processes, strategy, and decision-making → Core skill: translating problems into solutions 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 📊 →Turns raw data into insights → Asks: "What does the data tell us?" → Focuses on cleaning, analyzing, and visualizing data → Core skill: translating numbers into meaning Both roles ultimately drive the same outcome — better decisions. The difference is the starting point: one begins with the business problem, the other begins with the data.

  Step Stage Key Activities / Deliverables Focus / Outcome 1 Requirement Comes Client raises request/business need Initiation 2 Requirement Gathering Meetings, interviews, workshops Collect requirements 3 Requirement Analysis Identify gaps, risks, dependencies Clarify scope 4 Requirement Prioritization MoSCoW method, business value Rank importance 5 BRD (Business Requirement Document) Objectives, scope, risks, rules What business wants 6 FRD (Functional Requirement Document) Workflows, wireframes, validation rules How system works 7 Review Stakeholder & team feedback Refine documents 8 Client Sign-off Client approval Requirements finalized 9 Sprint Planning Select user stories, estimate effort Plan sprint 10 Development Build application, BA clarifies Working product 11 Testing (QA) Functional, regression, smoke, integration Quality assurance 12 UAT (User Acceptance Testing) Client validates solution Business approval 13 Go Live Deploy to production System available 14 Hypercar...

1. What is denormalization? A. Removing tables B. Combining tables to improve performance C. Deleting duplicate data D. Creating indexes only Ans : B 2. What is the main advantage of denormalization? A. Less storage B. Better normalization C. No redundancy D. Faster query performance ANS : D 3. What is a disadvantage of denormalization? A. Increased redundancy B. Better reporting C. Reduced JOINs D. Faster queries Ans : A 4. Which systems commonly use denormalization? A. Banking transaction systems B. Reporting and dashboard systems C. Authentication systems D. Compiler systems Ans : B 5. Which statement is TRUE? A. Normalization improves performance only B. Denormalization reduces redundancy C. Normalization reduces redundancy D. Denormalization removes duplicate data Ans : C 6. What is the purpose of constraints in SQL? A. Increase redundancy B. Maintain data integrity C. Speed up internet D. Delete tables Ans : B 7. Which constraint prevents NULL values? A. DEFAULT B. UNIQUE C. NOT ...