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Achieve quick and straightforward data modeling results by employing top-quality tools. They allow for the easy creation or modification of new models or alteration to existing ones as you design a redesign project.
They also support implementation by offering industry standards and guidelines to guide their use by your team members.
As modern environments tend to be highly complex, Oracle data modeling tools are becoming essential.
Since not all agencies offer equal functionality, you must consider all factors before making an informed choice, such as understanding the current system, objectives, and existing concerns before selecting one.
What Exactly Is Data Modeling?
Data modeling involves creating, defining, and improving business processes by identifying requirements for datasets and their relationships to each other.
This shows how databases manage information, such as types and associations, and how data is organized for storage and use.
An extensive data model is integral in developing an accurate database. Eliminating redundancies and errors ensures all requirements for data are documented regarding security, governance, usage, and other related considerations.
There are generally three different types of data models:
- Model concept
- Models are available in physical form.
- Model of a Logical Model
Building a house (database) provides an apt analogy for data modeling. Discussing building specifications, such as several rooms and exterior design, is crucial for business requirements.
A data modeler creates an architectural blueprint based on this information - this blueprint helps visualize its construction by engineers, database architects, and developers.
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Why Does Data Modeling Matter?
Data modeling is the cornerstone for building databases and optimizing performance, so its understanding should be paramount for those involved with data analysis--from database architects, data analysts, and business analysts down the chain of command.
Here are a few use-case examples:
Data Warehousing And Its Impact
Data warehouses are organized systems storing information gathered from various sources in similar/related formats.
When mapping warehouse structures and designs, their requirements must be considered and utilized when creating datasets, as this allows for data mining and analysis.
Using AI/ML And Big Data To Enable Other Technologies
Data modeling is pivotal in system design processes and forms the cornerstone for further stages, including business rule creation and tool/program creation.
Businesses can fully utilize modern technologies such as AI/ML and IoT for maximum benefit.
Collaboration Among Business Stakeholders
Data modeling helps IT teams collaborate easily with non-technical staff and construct data structures useful for analysis.
All stakeholders gain an overview of how data is utilized within business operations.
Enhance Query Performance
Querying data involves extracting it from a database. While this task can be completed quickly for smaller datasets, more substantial ones could pose more significant challenges, potentially overloading servers with too much data for optimal query performance to occur.
Utilizing data modeling allows analysts to extract only relevant information for query performance improvement more efficiently, thus saving time in data entry processes.
What Qualities Should a Data Modeling Tool Have?
Data modeling is enormously complex for small companies, especially when managing on-premises systems, cloud data stores, and SaaS third-party tools.
But don't fret: with the appropriate agency in hand, you won't be left doing all the heavy lifting yourself - instead, it will handle most of the hard work for you!
Below are a few features you should keep in mind when selecting a data modeling tool:
- Align with Your Business Goals and Use Cases. Your chosen tool must meet both database design teams and app designers with all the tools they require to work effectively together.
- Collaboration of IT users and business users. User interface and visualization features are pivotal in making data modeling tools in oracle accessible to non-technical users.
- NoSQL databases require analysis tools capable of working across various databases and platforms. Therefore, if your organization relies upon non-relational databases to manage unstructured data, then an analysis tool that supports such platforms will be essential.
- Data modeling standards that come standard include notation and best practices for database design. Look for tools that automatically create a schema, manually saving you time and effort.
- Compatibility between design tools. Some design tools will supply data models in proprietary formats; this doesn't make the tool any less effective; instead, it just changes how it should be utilized.
Compatibility between design tools. Some design tools will supply data models in proprietary formats; this doesn't make the tool any less effective; instead, it just changes how it should be utilized.
What Exactly Is The Oracle Analytics Cloud (OAC)?
Oracle Analytics Cloud, Oracle's cloud analytics platform, can be utilized by individuals, groups, or enterprises.
OAC boasts many capabilities ranging from data preparation and visualization, machine learning and natural language processing (NLP/NLG), and machine learning to mobile capabilities.OAC stands out by going beyond essential data preparation or visualization services to cover Machine Learning models without needing coding knowledge for model construction.
OAC provides organizations with a great alternative to Oracle Analytics Server (OAS), eliminating concerns over updates or hosting arrangements as Oracle handles everything for you - plus, OAC has automated lifecycle capabilities!
Migration to OAC depends on which version of Oracle Business Intelligence Enterprise Edition you're currently running (11g or 12c); exporting files directly into OAC should suffice, and you can start working immediately! Many organizations continue to connect Power BI/Tableau to OAC using or even after migrating over to OAC; this may be because many users prefer sticking with Power BI/Tableau over OAC.
OAC pricing is calculated based on the monthly user count or OCPUs/hour. OAC offers customizable plans that suit both enterprise and professional editions of OAC to enable organizations to choose which method is the most done for them based on pricing information and plan details.
Here's more pricing info regarding each plan option:
Let's dive in, discussing five best practices in OAC data modeling.
OAC: Five Best Tips in Data Modeling
The five best Tips in OAC for data modeling are:
- Use star schema
- Separate logical tables for fact/dimension table
- Rename logical column names with presentation-friendly labels
- Only the columns required in the logical layers should be retained
- Use a consistent naming convention
Now let's look at them all in more detail.
1. Make Use Of The Star Schema.
OAC requires its data models for information analysis to utilize a star schema to connect dimension and fact tables.
A star schema might sound complex, but it is straightforward to implement.
Let us review three tables to illustrate the star schema.
- Orders Fact Table
- Customer (Dimension Table)
- Product Dimension Table
Product IDs serve to connect the Customers table with the Orders Table. In addition, Product IDs allow associating Products table information to that in Orders.
Now we will see the relationship among the Customer, Product, and Order tables. You have created an effective data model with fact tables and multiple-dimension tables! Congratulations on making such an outstanding data model.
A star model lacks relationships among its dimensions tables; instead, they're linked through an information table. Data in the OAC layer may be represented with different star schemas.
Avoid associating data between dimension tables of different logical layers; instead, move all facts to one fact table and avoid data duplication between dimension tables of multiple layers.
Star schemas are highly recommended to optimize physical layer performance.
2.Create A New Table For Each Fact And Dimension Table.
Create separate tables for every fact or dimension used in the analysis. Combine or merge tables of more than one dimension when creating fact tables, including integrating.
Filters may also help narrow the data in dimension tables. Separated logical tables can help you quickly identify tables and reduce errors.
3. Use Presentation-Friendly Labels To Rename Logical Column Names.
Renaming columns in your logical tables with more presentable labels should be an industry best practice.
Labeling dashboards and reports are unnecessary. Locating columns within an account becomes even simpler if their labels and names match exactly.
Time saver! A report creator should take advantage of these data modeling techniques.
4.Only Keep The Columns That Are Required In The Logical Layers.
Keep only those columns necessary (and remove those not required) for analysis in any logical table - such as dimensions or facts tables.
Each table may hold significant information; typically, these tables are created for better tracking (from an organizational development or compliance perspective) or tracking purposes.
These data may not apply to your analyses; you can remove unwanted columns while keeping only those relevant. Your logical layer can serve as storage.
Best practices for creating fact tables and dimension tables are identical.
Remember to keep the primary key as you explore deeper logical layers. Although primary keys might not directly benefit your analyses, their columns are integral when linking fact tables with dimension tables.
5.Use A Consistent Naming Convention.
When creating tables, companies must adhere to an industry convention for creating tables. Many firms prefix Dim before dimension table names to mark consistency across their business units.
Two important reasons exist for which you might use additional naming conventions:
security reasons and convenience
First and foremost, an extended absence will help you better comprehend a table. Quick analyses often necessitate making tables quickly; making such quickie tables ad-hoc can be easy to overlook details.
Name your tables carefully when intending to use them for future analyses or scheduling reports. Doing otherwise could prove disastrous.
Second, this will assist your colleagues who use similar assets (reports or datasets, projects or dashboards) as you to remain on the same page and analyze data without disrupting widely shared assets.
Be sure to confer with your team when setting a convention so everyone is on board, or create a document regarding naming conventions for sharing purposes.
Oracle Database Modeling Tools: 6 Essential ERD Tools
1. Visual Paradigm
is an online diagramming software that draws ER diagrams. Compatible with many popular DBMSs such as Oracle, MySQL SQL Server, and Sybase databases.
The ERD for Oracle tool allows users to construct conceptual, logical, and physical models. With its modern user interface and extensive set of notations, including Crow's Foot notations - creating such models has never been simpler!
This tool allows you to enter samples directly into entities while understanding your database structure. Furthermore, its Model Transistor feature saves time by creating physical and logical models from the upper level without starting from scratch.
This ERD tool helps users automatically generate physical database elements in an Oracle database by automatically creating DDL files based on entities selected or the model itself, patch changes made to both entities or physical database elements made during data modeling or physical database maintenance, or visually edit an existing database by importing.
2. Navicat
Is an Oracle ER modeling offline tool that supports many other DBMSs, including MySQL SQL Server MariaDB and multiple OSes such as Windows, MacOS, Linux, etc.
ERD software features Crow's Foot notations, IDEF1X, and UML for rapid data modeling creation at any level - conceptual to physical.
Furthermore, its user-friendly GUI allows you to construct all physical and logical models effortlessly, saving time and effort when working. It even has automatic features to quickly produce physical/analytical models at various heights in seconds!
Navicat reverse engineering allows users to import physical databases and modify them, while its "Export SQL" feature generates SQL scripts for every component in your database.
3. SqlDBM
This online ERD for Oracle can also be found as a tool on SQL Server, MySQL, and Amazon Redshift databases. Designed as an interactive ERD for distributed teams working remotely across devices and locations - with support for other popular databases, Crow's Foot or IDEF1X notations allow you to convert conceptual models to physical ones similar to any ER Diagram solution.
Modelers automatically generate DDL files based on your data model, while reverse engineering makes reverse engineering possible by copy-pasting or uploading SQL DDL files generated from physical databases for use as models - thus simplifying version control as well as creation.
4. Astah Professional Offline
Astah PROFESSIONAL offline is a multi-diagramming software. With this versatile program available for macOS, Windows, and Ubuntu operating systems, you can easily create ER, UML, and data flow diagrams, as well as many others.
Astah Professional provides Crow's Foot notation and IDEF1X notations to assist Oracle ERD tool users with its "Export Excel" feature allowing data model components to be exported as SQL (SQL-92) query modes for processing by SQL-92.
Furthermore, its reverse engineering feature lets users import an existing database.
Astah Professional can be utilized for offline work only as it doesn't support collaboration online as some tools might.
5.Lucidchart
LUCIDCHART, another multi-diagramming tool available online to create ER Diagrams, is supported by Oracle, Salesforce, and other databases.
Lucidchart allows you to easily create an ER chart manually or automatically using imported data, using templates as speed bumps if choosing a manual drawing of an ER diagram.
Lucidchart offers many ways for users to collaborate on data models with others. Lucid scripts can generate SQL scripts to export your models directly into Oracle DBMS or any of the supported DBMSs.
Furthermore, this ERD tool offers various means of sharing models.
6. ERDPlus
ERDPLUS for Oracle is another online ERD tool with all the features and notations necessary to create conceptual to actual ER diagrams while supporting MySQL, IBM DB2, SQL Postgres, and other popular databases. In addition, an automated feature automatically makes physical models based on a logical ER Diagram.
ERDPlus generates SQL scripts to automate the creation of your Oracle database from physical models. Furthermore, this online ERD tool also lets you export diagrams in different formats.
This tool allows users to visualize data without performing reverse engineering operations.
Data Modeling Options In 2023
This section explores various tools for data modeling which may assist your project.
Archi
Archi is an open-source data modeling software architects, and others use for building ArchiMate models and sketches.
Features
- Sketch View. Archi is an innovative sketch tool that lets you brainstorm and design sketches quickly and efficiently.
- Archi is written in Java, so it works on Windows, macOS, and Linux computers and is cross-platform!
- Also, create canvas models to share with others easily using our Canvas Modeling Toolkit!
- Archi's Hints View helps newcomers grasp ArchiMate components, views, and relationships more quickly for an easier learning curve.
- Archi also features an advanced modern data visualization tool that displays model relationships.
Pricing
Archi can be downloaded for free.
Lucidchart
Lucidchart helps create visual representations of business processes, data flows, and team planning.
Features
- Lucidchart runs on Windows, Mac, and Linux systems and enhances collaboration by providing collaborative cursors and shape-specific comments to create systems and processes with colleagues.
- Visualization Lucidchart allows users to generate automatically generated entity relationship diagrams, organization charts, data models, and more.
- Compatible software includes Slack and Microsoft Office, Google Workspace, and Atlassian products.
- Lucidchart can quickly meet your organization's specific requirements by scaling to meet them.
Pricing
Lucidchart offers three versions for any individual or team: Individual ($7.95 monthly subscription), Team (one person at $9 per month subscription fee), or Enterprise (custom quotation).
Data Modeler Navicat
Navicat Data Modeller is an application used for designing database models. It enables the creation of conceptual, logical, and physical data - as well as forward and reverse engineering capabilities, SQL queries creation/export models from different external data single sources/export models/specify data types/model entity relationships/etc.
Features
- Navicat Data Modeler supports multiple database systems. MS SQL Server, as well as other popular options like SQLite, are supported.
- Designer Tool. Types of Models. Navigation supports three distinct data models - conceptual, physical, and logical. Model Conversion allows the conversion of abstract data to its equivalent logical representation.
- Reverse Engineering. Navicat allows users to generate ER diagrams from existing database structures.
- Data models can then be visualized to comprehend better attributes, indexes, and relationships within your model.
- SQL Code Generation. Navicat Data Modeler also lets you generate SQL code based on sections of your model.
- Collaboration. Navicat integrates seamlessly with the cloud so that you can sync model files between teammates remotely. This enables effective collaboration across locations.
Pricing
Navicat comes in two versions: noncommercial and enterprise. The enterprise version charges $22.99 per month. A yearly subscription costs $229.99; you can purchase the perpetual license at $459.
You can select the perpetual license at $249 or the monthly subscription fee of $12.99.
Data Modeler DTM
DTM Data Modeler lets you build logical and physical data models and perform forward engineering and reverse. Designers and administrators of databases can even add indexes and triggers to specific tables to better organize data structures.
Features
- DTM offers an HTML report designer tool for reverse and forward engineering, allowing for in-depth data-model comparisons and better understanding.
- DTM features an easy way to access basic features quickly.
- Furthermore, its conversion tool enables data models created using one database to compatible versions.
Pricing
DTM Data Modeler can be purchased with one payment of $179 for one license, $349 for three appointments, or $499 for five charges.
MagicDraw
MagicDraw can be utilized for reverse engineering, database schema modeling, and systems engineering applications.
Primary users include software developers, system analysts, document writers, and architects.
Features
- MagicDraw offers an intuitive user experience to enable quick startup time. Plus, its Open API allows access to additional functionality.
- Collaborate. MagicDraw's cloud platform makes collaboration easy for designing database models or engaging in other activities.
- Automated report generation Create comprehensive software documentation in RTF or HTML formats automatically with MagicDraw.
Pricing
MagicDraw Professional can be purchased for $899, while MagicDraw Standard average costs just $299.
Oracle SQL Data Modeler
Oracle SQL Developer Data Modeler allows managing and creating multidimensional, logical, and physical data models.
Features
- Collaborative development. Oracle offers an advanced cloud infrastructure designed for collaboration between members of your team to complete various modeling tasks efficiently and successfully.
- Plus, their documentation offers guidance to beginners getting their feet wet with data modeling!
- Oracle Data Modeler allows for both reverse engineering and forward engineering quick analysis of data models to understand their structure, attributes, and relationships thoroughly.
Pricing
Oracle SQL Developer Data Modeler can be freely downloaded.
HeidiSQL
HeidiSQL is a data modeler that allows users to manipulate databases like SQLite PostgreSQL, Microsoft SQL Server, and MySQL MariaDB.
Features
- Exporting data from a database. Table rows may be shipped in PHP arrays, SQL statements, XML HTML, or CSV format.
- With HeidiSQL, you can export these rows as PHP arrays, SQL statements, XML HTML, or CSV for further use. In addition,
- HeidiSQL includes task management capabilities to monitor running processes and support multiple server connections and an SQL Editor, which lets you edit tables, stored routines, and views easily.
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Conclusion:
Data modeling is a crucial element of analysis, offering users numerous benefits, including better collaboration, lower terms of cost, reduced risks, faster return on investment, and increased performance.
Data modeling may take multiple forms, including conceptual, logical, and physical models.
Oracle Analytics Cloud is an enterprise cloud service providing data modeling capabilities. This platform helps create secure databases, which can be utilized in business process analysis.
Use BI Connector to link Oracle Analytics data with modern visualization tools like PowerBI or Tableau and access interactive dashboards featuring drag-and-drop functionality to gather valuable insights.
