How does data warehousing architecture affect data management?

Data modeling involves designing the structure of your data to ensure it's organized, efficient and supports the business's needs. In data warehousing, it's crucial because the architecture must accommodate different data models, like relational or multidimensional, to manage and access data effectively. What I've seen most data scientists do day to day is to regularly assess and update data models to meet evolving business needs, this would give you and idea or how your data should be modelled.

A data warehouse is one type of data management system only that is designed to enable and support business intelligence (BI) activities, especially analytics. Data warehouses are solely intended to perform queries and analysis and often contain large amounts of historical data. In addition , a cloud-based DWH architecture leverages cloud computing resources to store, manage, and analyze data for business intelligence and analytics. The DWH should provide a single system of record for an organization. System quality includes three measures – system flexibility, system scalability, and system integration. DWH should be flexible. DWH eco system improves the speed and efficiency of accessing different data sets.

Data integration refers to the process of combining data from various sources into a unified view. In data warehousing, it's central, as the architecture should seamlessly integrate data from multiple systems, ensuring consistency and accuracy. As business intelligence developers, we mostly monitor ETL processes, ensuring data is collected and integrated from source systems as planned. Sometimes the aim of this is to provide clean and valuable data for our stakeholders.

Data warehousing architecture significantly impacts data storage. It determines how and where data is stored within the warehouse. Choices here influence factors like data retrieval speed and scalability. If we talk about data storage, this mostly speaks to database engineers, rather than data scientists. Most of the time DB engineers optimize data storage, making decisions on data partitioning and indexing for performance.

Data access in the context of data warehousing involves how users or applications interact with the stored data. The architecture must provide efficient and secure means for querying and retrieving data. Data analysts mostly manage user access controls and query performance, addressing any bottlenecks.

Data warehousing architecture is the design and structure of a data warehouse, which is a centralized repository of integrated data from multiple sources that supports analytical and reporting applications Data quality and consistency: Data warehousing architecture determines how data is extracted, transformed, and loaded (ETL) from the source systems to the data warehouse. Data accessibility and usability: Data warehousing architecture determines how data is organized, stored, and accessed in the data warehouse. The data warehouse can use different schemas, such as star, snowflake, or galaxy, to model the data according to the business requirements and user queries.

Data Governance Tools/Methodologies are paramount to success of a Good Data Quality Strategy. This ensures Highest Level of Quality for data via likes of Data Stewards and other stakeholders.

The DW architecture can make or break a DW project, specially in the data management or data governance aspect. I have seen numerous architectures that are good only in slides. How many times does data need to be copied before landing in the DW. Each copy represents a risk that can cause future problems. Security best practices and policies will force architects to rethink the way DW are designed to make them leaner and be more manageable. On the other hand, as the DW become an essential customer facing tool, the DW must be led out of its glass cage. Governance Policies will have to evolve to support new business imperatives.

The architecture defines how data is structured, stored, and accessed in the data warehouse. It significantly affects data management by influencing data flow, performance, and scalability. Proper architecture ensures efficient data management, while a poor one can lead to data silos and inefficiencies.

At this age of AI , BI role is diminishing. DW architecture was primarily to cater BI . But still rightfully done EDW provides greater value with single version of truth of data to evaluate the ML models and AI driven Apps. Modern EDW need to be setup with lean Super type and less surrogate keys for all real time load and analytics.This could make quick loads times with less compromised on data quality. Algorithms need to be integrated as part of data loads to establish data lineage and data quality.

Data warehousing architecture critically shapes data management. It affects data modeling by defining data organization, integration from varied sources, and storage with performance implications. Access is structured for user retrieval, and processing power is key for efficient analysis. Data quality hinges on the system's ability to ensure integrity. Other considerations include security, governance, compliance, disaster recovery, and cost-efficiency. Alignment with business objectives is vital for effective data management.