Master Data Modeling with Erwin Data Modeler tool!

Understand data modeling as a design activity that meets business requirements through requirement gathering and analysis, and how multiple correct models can express entities, attributes, and relationships for the database.

Explore the entity relationship (ER) model, a high-level, graphical representation of entities, attributes, and relationships, emphasizing business processes and eventual database implementation and future coverage of the dimensional model.

Identify and describe entities as principal data objects in a model, using singular names and rectangles in diagrams. Distinguish independent versus weak, and dependent entities with their relationships.

Learn to create and rename entities in Erwin data modeler’s logical data model, manage properties and attributes, and define surrogate keys, primary keys, and udp.

Explore core data types for data modeling, including string, numeric, boolean, and date-time categories, with notes on fixed and variable length types and key design considerations.

Explore attributes in data modeling, defining identifiers, codes, quantifiers, and text items, and learn how attributes describe entities like customers, students, and teachers with primary keys and descriptors.

Learn to create and configure attributes in Erwin data modeler, define primary keys and not null constraints, apply validation rules and UDP domain values for a customer entity.

Explore identifying and non-identifying relationships, explain one-to-one and one-to-many cardinalities, and learn to handle many-to-many with an associative entity in the physical model.

Explore creating identifying and non identifying relationships between customer and account—one-to-many and recursive—using role names, migrated attributes, and resolve many-to-many with an associative entity in the physical model.

Learn about primary, surrogate, candidate, alternate, structured (composite), and foreign keys in data models, their properties, and how they link tables through relationships.

Create and manage primary, alternate, and inversion keys in Erwin Data Modeler for customer and account entities, define a concatenated primary key, and map foreign keys in a one-to-many relationship.

Explore subtypes and super types through generalization and specialization, using a party supertype for customer, employee, and vendor subtypes; learn discriminator attributes and when to create mutually exclusive or overlapping subtypes.

Create a four-entity data model in Erwin, define party as the supertype with customer and employee subtypes, set subcategory semantics inclusive or exclusive, and use party type code as discriminator.

Create a simple data model using Erwin, grouping data into customer, product, calendar, and transaction entities; define attributes, keys, and relationships; explore data quality and normalization through practice.

Explore common data modeling notations, including information engineering and IDEF1X, and learn how entities, relationships, and attributes are represented in the Erwin Data Modeler context.

Master Erwin data modeler naming standards, defining entities and attributes with primes and modifiers, using a glossary, and verifying compliance with prefixes, capitalization, and name hardening.

Define domains in Erwin to standardize common attributes, then drag and drop them into entities. Configure prefixes and constraints to streamline identifiers, amounts, names, codes, and dates across models.

Learn to create and reuse model templates in Erwin Data Modeler to speed data modeling and enforce naming standards and domain values, then bind or unbind templates in new models.

Practice creating a relational data model for IMDB by identifying at least five entities, gathering information, and organizing data to compare approaches in a guided exercise.

Explore conceptual data modeling as the first stage of database design, a theory-led, technology-agnostic diagramming method used by business analysts and data modelers to define entities, relationships, and rules.

Explore building a conceptual data model in the Erwin data modeler for the IMDB database, using a high-level logical model with entities like title, movie, serial, genre, platform, and reviews.

Translate a conceptual data model into a logical, database-agnostic design by defining entities, attributes, and relationships with primary and foreign keys, resolving many-to-many with associative entities.

Transform a conceptual IMDB data model into a logical model using Erwin Data Modeler, resolving many-to-many relations with associative entities and defining keys and attributes.

Master data modeling through normalization theory to remove redundancy and incompleteness. Learn first through third normal forms and BCNF, and why fifth normal form is not used.

Explore physical data modeling in Erwin, focusing on target database effects on loading and retrieval, with indexing, partitioning, clustering, compression, and views to boost performance.

Explore physical data modeling with Erwin, configure target databases like Oracle and Teradata, define primary keys and indexes, apply slowly changing dimensions, and create views and materialized views.

Generate ddl from your data model through forward engineering in Erwin, selecting create table, views, and materialized views, preview results, then save or directly deploy to the database.

Learn to set up a Snowflake ODBC connection for Erwin data modeler by downloading drivers, configuring data source details, and testing the connection for a successful integration.

Learn to reverse engineer a database into physical and logical data models using Erwin Data Modeler, from files or direct database connections, with glossary and key relationships.

Master the complete compare and alter scripts workflow in Erwin data modeler. Deploy changes to new schemas in Snowflake, use reverse engineering, and generate targeted alter scripts.

Explore dimensional modeling and data warehousing, contrast OLTP and online analytical processing, and learn to define the fact table, its grain, and dimensions with hierarchies to enable reporting.

Explore dimension and fact table types in Erwin data modeler, including static, slowly changing type one and type two, rapidly changing, conformed and junk dimensions, plus additive, semi-additive, non-additive facts.

Explore dimensional modeling with Erwin: define facts and dimensions, build snowflake or star schemas, and adjust dimensional properties, role types, and update types to manage history and performance.

Explore data warehousing concepts, architecture, extraction, loading and transformation, and elements centralizing data from multiple sources for historical analysis and business intelligence, enabling analytics and decision making.

Compare Kimball and Inmon data warehousing approaches, covering data marts, ETL, star and snowflake schemas, conformed dimensions, and bottom-up versus top-down design tradeoffs.

Map data warehouse sources from OLTP models into a unified data movement using Erwin, import other models, and resolve differences with the compare wizard to build accurate data mappings.

Compare relational and dimensional data models, highlighting parent and child versus dimensions and facts, normalization, table counts, aggregate tables, and front-end tool suitability.

Explore denormalization and normalization concepts, including when to reintroduce redundancy, standard techniques like repeating groups, repeating data elements, and summarization, and approaches such as rolling up, rolling down, and identity.

Denormalize in Erwin by linking a normalized attribute to a related entity to create a migrated attribute for better performance, with changes propagating via two-way synchronization in the physical model.

Explore top-down and bottom-up data modeling approaches, their goals, and common pitfalls. Build from general concepts to detailed entities like customer and employee, or start from interfaces and reports.

Create a dimensional data model for Amazon using the Erwin Data Modeler tool, guided by attached guidelines and reporting-focused business requirements.

Create a dimensional data model for Amazon ecommerce by identifying entities such as user, product, brand, seller, and order, defining relationships, and planning star schema for reporting.

Develop a star schema from the Amazon data model by rolling down products, categories, and addresses, adding start and end dates for order states and a profit metric.

Learn to annotate and customize a data model in Erwin Data Modeler, including naming the Amazon normalized data model, using the diagram window, and adjusting object properties.

Learn erwin transformation options, including horizontal and vertical partitioning, to optimize data models by distributing attributes while preserving primary keys.

Create and manage subject areas in Erwin Data Modeler to group related entities, preserving relationships while moving entities between subject areas using the subject area editor and move options.

Explore the erwin diagram menu to arrange and view model objects, and create entities such as US student and teacher with attributes like student ID and student name.

Link conceptual, logical, and physical data models with design layers, then add a model source, load the IMDB logical data model, and connect the sources via the model source editor.

Advance your data modeling with Erwin macros, using the macro toolbox and naming options to automate entity and attribute naming, with 190 plus predefined commands for naming and triggers.

Master the advanced report designer in Erwin data modeler to generate entity and attribute reports from the data model, view ER diagrams, and export to HTML, PDF, or CSV.

Create source-to-target mappings in erwin using the report designer, mapping data movement sources to a DW model, include transformation notes and comments, and export the mapping report to HTML.

Master the bulk editor in Erwin Data Modeler to update multiple table definitions efficiently using CSV imports, notepad++ cleanups, and importing model updates from CSV.

Use the bulk editor in Erwin to update column definitions for all columns, then transform data with Excel tools, text-to-columns, VLOOKUP, and concatenate, and import the CSV to apply changes.

Use the advanced query tool in Erwin to create a customer table, load data from a CSV into Snowflake, and run filtered queries to verify results.

Save the current data model diagram in Erwin using the diagram picture tool, name it, select subject areas, save to a folder, and verify the saved picture opens as expected.

Understand why data modeling matters for flexible loading, future-proof design, and reduced anomalies through normalization; emphasize documentation, data quality, and a single version of truth for enterprise analysis.