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Data Analytical Architecture

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Data Analytical Architecture - An OverviewData Analytical Architecture - An OverviewData Analytical Architecture: An Overview Analytics architecture refers to the comprehensive framework that encompasses the systems, protocols, and technologies used to collect, store, and analyze data within an organization. It plays a pivotal role in transforming raw data into meaningful insights, thereby driving informed decision-making and strategic planning. The architecture is designed to ensure the efficient flow of data from various sources to end-users, enabling them to access and inte Components of the Data Analytical ArchitectureComponents of the Data Analytical ArchitecturePasted image 20240512124835.png The diagram you provided illustrates a typical Data Analytical Architecture. Let's break down and explain each component and the flow of data within this architecture: Components of the Data Analytical Architecture 1. Different Data Sources: * This represents the various origins of data that can include databases, sensors, social media, and more. * These sources provide raw data which needs to be collected and processed. 1. Department Warehouse: * A

Data Analytical Architecture Explained

Data Loading and Warehouse Management:

  • Data loaded into the data warehouse must be well-understood, structured, and normalised with appropriate data types.
  • Centralisation of data provides security and backup facilities. It also allows for significant preprocessing and checkpoints before storing data.
  • Control over Enterprise Data Management (EDM) may extend to local systems like departmental warehouses and data marts created by business users for flexible analysis needs.

Departmental Warehouses and Data Marts:

  • Departmental warehouses and local data marts cater to specific business needs, allowing for more flexible analysis.
  • Users may conduct deeper analysis within these local systems, providing additional insights beyond the centralized data warehouse.

Data Utilization Across the Enterprise:

  • Once data resides in the data warehouse, it is accessed by various applications across the enterprise for Business Intelligence (BI) and reporting purposes.
  • These applications are vital for operational processes, receiving critical data feeds from data warehouses and repositories.
  • Analysts then access provisioned data for downstream analytics, often using tools that perform in-memory analytics on desktops, analyzing samples of data rather than the entire dataset.

In Simple Terms: Imagine your data warehouse as a massive storage facility for all your organization's data. Before storing data, it's like going through a security check to ensure everything is in order. Within this warehouse, different departments might have their smaller storage areas (warehouses) for specific needs.

Once the data is in the warehouse, different teams and applications across your organization can access it for various purposes like reporting and business intelligence. Finally, analysts use specialized tools to dig deeper into the data, like detectives investigating clues, but they often work with smaller samples rather than the entire dataset.

This architecture ensures that data is managed efficiently, accessible to those who need it, and analyzed effectively to drive informed decision-making across the organization.

MM - Data Analytical ArchitectureMM - Data Analytical ArchitectureCreating a mind map for Data Analytical Architecture involves identifying key concepts and their relationships. Here are the keywords and short sentences for each major component and their sub-components: Central Node Data Analytical Architecture** Branches 1. Different Data Sources Keywords**: Diverse origins, raw data, databases, sensors, social media. Short Sentences**: Various origins of data. Raw data from multiple sources. 2. Department Warehouse Keywords**: Department-specific, sto