Microsoft Excel as a Database PDF

**USING MICROSOFT EXCEL AS A DATABASE** **EXCEL AS A FLAT-FILE DATABASE:** Excel functions as a flat-file database: - It stores data in a **single table or sheet** without complex relational structures like SQL databases - Flat-file databases are useful for managing **small datasets** wh...

**USING MICROSOFT EXCEL AS A DATABASE** **EXCEL AS A FLAT-FILE DATABASE:** Excel functions as a flat-file database: - It stores data in a **single table or sheet** without complex relational structures like SQL databases - Flat-file databases are useful for managing **small datasets** where complex relationships and constraints aren´t required. - It´s a useful tool for **smaller applications or quick analyses** - [Data Storage in Excel:] each worksheet in Excel can represent a "table" with rows as records and columns as fields - [Advantages and limitations:] while Excel can manage tables and perform simple lookups, it lacks the **integrity constraints** (like enforcing referential integrity) and **scalability** of an RDBMS - [Appropriate use cases:] just used for rapid prototyping, data explorations and small to medium datasets **CREATING AND USING RELATIONSHIPS IN EXCEL:** Excel´s Data Model allows for the creation of relationships between tables, enabling users to query and analyze data from multiple tables within the same workbook. - **Data Model Basics:** is a built-in feature allowing multiple tables to exist in a single workbook with defined relationships, making it possible to analyze related data. - **Setting up relationships:** allow to establish relationships between tables using fields that serve as foreign keys - **Relationship types:** Excel only supports one-to-one and one-to-many relationships. \*Many-to-many relationships require more complex workarounds, like bridge tables. - **Limitations of relationships in Excel:** Excel doesn´t enforce referential integrity in relationships, making manual attention to data consistency necessary. **DATA INTEGRITY IN EXCEL:** Data integrity is critical in any database to ensure that the data is accurate, consistent and reliable. Although Excel doesn´t enforce integrity constraints, best practices and certain tools within Excel can help maintain data quality. - **Unique IDs and Data consistency:** be sure to include unique IDs for each record to avoid duplicates and the use of consistent data types within each column to prevent errors. - **Validation rules:** use Excel´s Data Validation feature to restrict data input (enforcing date formats or restricting input to specific lists) - **Error checking tools:** use [Conditional Formatting] to highlight potential errors (duplicates or blank cells) and [Text to Columns] to standardize data formats (dates or currency) **NORMALIZATION WITH EXCEL LIMITS:** **Database normalization** minimizes redundancy and improves data integrity by organizing data into separate tables. In Excel, basic normalization techniques can still be implemented, helping to make the data cleaner and easier to manage. - **Basic Normal Forms:** 1. 1NF: separating atomic values in different fields 2. 2NF: separating entities in different tabs (Splitting Data into Tables) and using PK and FK 3. 3NF: avoiding transitive dependencies - **Relational Table Design:** designing tables around entities and their relationships to minimize redundancy and ensure each piece of information only appears once. **EXCEL FUNCTIONS AS SQL EQUIVALENTS:** Excel includes lookup functions like VLOOKUP, INDEX, MATCH and, more recently, which can simulate basic JOIN operations in SQL by pulling data from other tables. - **VLOOKUP (Vertical Lookup):** it can retrieve data from a specified range based on a matching key. It has limitations needing sorted data or being limited to exact matches. - **INDEX and MATCH Combination:** INDEX and MATCH together offer more flexibility and can retrieve data from left-to-right, unlike VLOOKUP - **Limitations:** Unlike SQL joins, these functions don´t dynamically update as new data is added or existing data is changed; they require careful handling and manual updates if the data structure changes. **PIVOT TABLE:** Advanced tool for calculating, summarizing and analyzing data that allows you to see comparisons, patterns and trends in them in a simple way.