Advanced Modeling
Microsoft Fabric & Azure
Transform the way you access, manage, and make data-driven decisions by connecting your data sources and services within a unified platform.
Attain a comprehensive view of your data by implementing a Data Warehouse, Data Lake, or Data Lakehouse, and enhance the analytical capabilities of your entire organization.
Data Warehouse
A Data Warehouse is a structured repository that stores data from various sources in an organized manner, with the goal of providing a single source of truth.
Data is cleaned, transformed, and integrated into a schema optimized for queries and analysis.
Data Lake
A Data Lake is a repository for unstructured or semi-structured data, designed to store vast amounts of raw data in its original format, facilitating accessibility. Data lakes are designed to receive and store all types of data without a predefined schema.
Data Lakehouse
A Data Lakehouse represents an innovative, open data management architecture that seamlessly blends the flexibility, cost-efficiency, and scalability of Data Lakes with the robust data management and ACID transactions of Data Warehouses. This amalgamation enables comprehensive business intelligence (BI) and machine learning (ML) capabilities across all your data.
Data Modeling
Data modeling is the process of designing and structuring how data is organized and related within a database. It involves identifying entities, attributes, and the relationships between them to ensure the integrity, consistency, and efficiency of stored data.
Data modeling provides a logical and visual representation of information, making it easier to understand and access data efficiently.
Azure Solutions
Microsoft Azure
Offers a variety of services that support data modeling in Data Warehouse, Data Lake, or Data Lakehouse environments.
Azure Data Lake Storage
It is a highly scalable and secure storage service that allows you to store structured and unstructured data. Combined with tools like Azure Databricks.
Azure Data Factory
Allows you to orchestrate the necessary Extract, Transform, Load (ETL) processes for data modeling in a Data Lakehouse, providing a comprehensive solution for data management and transformation.
Work Methodology
Analysis of Requirements
Identify and understand business and data requirements.
Conceptual Modeling
Abstract representation of entities and relationships.
Data Normalization
Eliminate redundancies and ensure model efficiency.
Logical Modeling
Transform the conceptual model into a well-defined logical structure.
Physical Implementation
Create the physical structure of the database.
Testing and Validation
Verify functionality and compliance with requirements.
Maintenance and Evolution
Update and enhance the model based on changes and new features.
Data Analysis and Predictive Modeling
Utilize advanced techniques and algorithms to analyze data, discover patterns, make forecasts, and build predictive models that drive informed decision-making.
We resolve your inquiries.
Architecture Comparison
The primary difference between a Data Lake, a Data Warehouse, and a Data Lakehouse lies in their approach to data management and storage. A Data Warehouse stores structured data in a predefined schema, a Data Lake stores raw data in its original format, and a Data Lakehouse is a hybrid approach that combines the capabilities of both.
Source: www.databricks.com
Data Lakehouse con Fabric & Power BI
End-to-End Data Lakehouse Architecture with Microsoft Fabric for data ingestion and transformation from multiple sources. It employs native connectors and intuitive data transformation. Data is stored in Delta Lake format for efficient access and manipulation. Power BI enables data visualization and querying, while Warehouse provides connectivity and data querying in Lakehouse tables.
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