Oracle SQL & PL/SQL

  In Oracle Database, DBMS_SCHEDULER is a powerful built-in PL/SQL package used to schedule, automate, execute, and monitor database tasks along with manage background task inside the Oracle Database. It is especially useful for DBAs who need to automate routine maintenance, data processing, backups, reporting, and other recurring activities. 1. What is DBMS_SCHEDULER and it's use. DBMS_SCHEDULER is an Oracle-supplied PL/SQL package that allows database users and administrators to define and manage scheduled jobs. For example, suppose a DBA needs to run a table cleanup procedure every day at 2:00 AM. Instead of manually executing the procedure, the DBA can create a scheduler job that runs automatically. Automating data loads, transformations and clean up from staging tables into core datawarehouse tables.  Data purging activities Report scheduling

  DBA_OBJECTS is an Oracle data dictionary view that lists every object in the entire database — tables, views, indexes, sequences, procedures, packages, triggers, synonyms, etc. — across all schemas and all these were managed by DBA. Core Oracle database objects Object                                       Purpose      1> Table                               Stores application data in rows and columns 2> Index                             Improves query performance by providing faster access paths 3> View                               Virtual table based on a SQL query 4> Materialized View   Stores the re...

  A database schema is like a blueprint that defines how data is organized and stored in a database . It describes the structure of tables, columns, data types, relationships, constraints, and indexes, providing a clear framework for how different pieces of data are stored and connected. 🔑 Key Points Schema = Structure → Defines tables, columns, and rules. Ensures consistency → Data must follow the schema (e.g., a column defined as INT cannot store text). Relationships → Specifies how tables connect (foreign keys, primary keys). Constraints → Rules like NOT NULL , UNIQUE , CHECK .

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...