6 components of an enterprise data warehouse

In the current information-based commercial environment, data-driven businesses increasingly rely on complex information management systems. This is necessary in today’s information-based commercial environment. These systems exploit their extensive databases. The Enterprise Data Warehouse (EDW) is the hub of the data ecosystem. It is a central repository built to accommodate and analyze large amounts of structured and unstructured data. In this blog, we look at the EDW architecture and its six core components. We explore how they impact organizational insights and decision-making processes.

The core components of an enterprise data warehouse

Data sources

According to a survey by IDG, 84% of organizations consider data from multiple sources as critical to their business strategy.

An enterprise data warehouse is fed by numerous types of data sources. These sources are diverse, ranging from internal to external databases. Examples include transactional systems, CRM, ERP, cloud applications, and social media. Ultimately, consolidating information from these sources creates a single view. This single view covers the organization’s operations, customers, and market dynamics.

Ingestion layer

According to MarketsandMarkets, the data integration market is expected to grow from $6.44 billion in 2020 to $12.24 billion by 2025, at a CAGR of 13.7%.

The Ingestion Layer acts like a gateway for raw data into the EDW environment. This component is responsible for raw data extraction from various sources. Subsequent transformation into a standardized form occurs here. The data is prepared before loading onto the staging area for further action. Moreover, advanced techniques and integration tools streamline this process. This leads to efficient real-time ingestion, enabling timely decision-making.

Staging area

Research by Forrester indicates that data preparation tasks consume up to 80% of data scientists’ time, highlighting the importance of efficient staging processes.

After ingestion into the EDW system, all materials undergo refinement and preparation in the Staging Area. This intermediate storage refines raw data through comprehensive cleansing, standardization, and enrichment. The result is data more useful for analytical purposes. Finally, data integrity and consistency are ensured. This involves applying cleansing algorithms, deduplication techniques, and validation routines before the information advances to the storage layer.

Storage layer

According to a study by IBM, 63% of organizations plan to increase investment in storage technologies to accommodate growing data volumes.

The Storage Layer is the heart of the enterprise data warehouse system. It provides scalable and efficient storage for structured and unstructured data assets. Robust database technologies support this layer. Examples include relational databases, columnar stores, or distributed file systems. This makes the layer relevant for optimizing data retrieval and query performance. Moreover, methods like indexing, compression, and partitioning enhance resource utilization and storage efficiency.

Metadata module

Gartner predicts that by 2023, 90% of data and analytics innovation will require incorporating metadata management, governance, and sharing.

The Metadata Module is central to the EDW architecture. It serves as a repository for comprehensive details about organizational information assets. This includes attributes, structures, and relationships. For example, metadata catalogs capture vital attributes, lineage, access control definitions, and classifications. This allows users to effectively locate and use similar objects. Ultimately, this mechanism guarantees quality, compliance, and traceability throughout the entire lifecycle. It also enforces metadata-driven governance and lineage tracking.

Presentation layer

Research by McKinsey & Company suggests that organizations that leverage data visualization tools effectively can increase decision-making effectiveness by up to 36%.

The Presentation Layer is the interface that grants users access to insights from the data warehouse components. This layer includes user-friendly dashboards, reporting tools, and ad-hoc query interfaces. It also provides customized data visualizations for various personas. These personas include top management executives, HR directors, and country managers. By providing self-service analytics and personalized reporting options, the Presentation Layer empowers stakeholders. They can explore data, gain actionable insights, and make informed decisions to drive business success.

Enterprise data warehouse versus usual data warehouse

Information management involves two main concepts: the Enterprise Data Warehouse (EDW) versus the traditional Data Warehouse (DW). While both store and manage data, they have significant differences. Therefore, we will look into the attributes of both the EDW and traditional DW. We will highlight their unique features, functionalities, and appropriateness for various organizational needs.

1. Scope and scale

The EDW is designed to serve all corners of an organization. It helps departments and units with diverse information requirements. It pulls together information on operations, clients, and market dynamics from several sources. Consequently, this makes the data appear as one single entity. The EDW’s scalability allows it to handle the vast quantities of structured and unstructured data modern businesses need.

In contrast, a classic DW may focus only on specific departments within a company. Thus, it has a narrower scope than the EDW. For example, a DW may be implemented for financial reporting, sales analysis, or supply chain monitoring. However, a traditional warehouse may lack the scalability to support overall analytical requirements effectively. This remains true even if it handles large amounts of data.

2. Data integration and agility

The EDW strongly emphasizes robust data integration capabilities. This facilitates seamless ETL (Extraction, Transformation, and Loading) processes for obtaining data from diverse sources. Using complex integration tools ensures faster data flow. This facilitates real-time updates that maintain information uniformity across the company. Therefore, this agility allows organizations to respond quickly to changes. They can easily integrate new analytics tools and datasets into their business context.

Traditional warehouses also support data integration, but their process is often more formal and procedural than the EDW. Consequently, making adjustments or adding fresh details requires considerable manual intervention. This slows down development schedules. It also limits operational flexibility under dynamic business scenarios.

3. Scalability and performance

Scalability is a key feature of the EDW design. It enables firms to adjust storage and processing resources based on data growth and resource demand. Cloud-based solutions allow organizations to scale resources up or down depending on workloads. This ultimately leads to almost infinite scalability. Furthermore, high-performance processing engines and distributed computing architectures ensure smooth query execution. This executes complex analytics and real-time insights efficiently.

Nevertheless, traditional DWs often face scaling difficulties. This happens when information volume or user access increases without purchasing additional equipment. Scaling hardware infrastructure to support increased workloads requires significant capital investment. Moreover, this comes with related operational challenges. Inadequate facilities often result in performance degradation. Repeated requests occupy resources, causing slowdowns. This frustrates users, especially those operating from resource-constrained environments or outdated design patterns.

4. Governance and compliance

Governance and compliance are integral parts of the EDW ecosystem. They are embedded within metadata management and data governance frameworks. These frameworks guarantee information quality, lineage, and security. Furthermore, centralized governance structures enforce access control, data privacy policies, and regulatory standards at the enterprise level. This helps mitigate risks associated with data breaches or non-compliance.

Traditional data warehouses may incorporate governance and compliance measures. However, these processes might be less comprehensive or centrally located than those of an EDW. Decentralized governance can pose issues, such as tracking lineage, ensuring data integrity, and monitoring regulatory compliance. This is because silos can challenge effective metadata management capacities.

Enterprise data warehouse architecture

Organizations need the enterprise data warehouse database server to make sense of their large amount of information. This server is an essential piece of technology in their decision-making process. Therefore, the architecture is crucial for organizing, processing, and analyzing information. It supports informed decision-making based on real-time business analytics. We should examine the principles that define the most effective architecture for building EDWs.

Centralized repository

The central repository is at the core of any EDW’s architectural design. It serves as a single source for all organizational data. It consolidates information from various sources using one standard format. These sources include operational systems, external feeds, and third-party providers. By providing a central storage point, it aids fast retrieval without redundancy. Thus, it ensures the same meaning across all users.

Data integration layer

This part acts as an interface for raw materials flowing into the actual EDW. It includes ETL processes that extract, transform, and load data from many source systems. Thus, the right quality must be ensured by cleaning, enriching, and harmonizing new datasets often. Advanced integration features support agile decision-making and operational efficiency. These features allow for either real-time or batch data ingestions.

Data warehousing engine

The data warehousing engine supports the EDW’s architecture. It provides a solid storage medium and a query toolset utilized in processing. Relational database management systems are a good example of this component. These systems are optimized to handle analytical workloads like SQL Server, Oracle, or Teradata. It allows users to store data efficiently. Moreover, it facilitates loading, indexing, partitioning, and optimizing queries for easy access during reporting and analysis.

Metadata management framework

The Metadata Management Framework is key to understanding, governing, and utilizing information from an EDW. It provides general knowledge about the metadata used to describe data, including definitions and lineage. Through this framework, users can identify information and trace it back to its sources. Furthermore, they can understand its impact on other areas and control regulatory compliance. This ensures decisions are informed using reliable assets.

Business intelligence (BI) layer

The Business Intelligence (BI) layer sits atop the EDW architecture. It provides intuitive interfaces and tools for data visualization, reporting, and analytics. BI platforms offer dashboards, ad-hoc query tools, OLAP cubes, and predictive analytics capabilities. These are tailored to the needs of different user personas across the organization. Consequently, this layer empowers users to explore data, uncover insights, and derive actionable intelligence. This intelligence supports strategic decision-making and drives business outcomes.

Scalability and flexibility

An EDW is designed based on two key principles: scalability and flexibility. The architecture should accommodate increased data storage and processing requirements. This is achieved by scaling horizontally or vertically as data volumes and user needs change. Furthermore, the principle of modular design facilitates the integration of new information sources, analytical tools, and technologies. This maintains flexibility in EDW operations.

How can brickclay help?

Brickclay guides organizations through the complexities of successful Enterprise Data Warehouse (EDW) implementation and optimization. This enables clients to gain maximum benefit from their data resources and drive business success. Here are some ways Brickclay helps businesses develop their data potential:

Customized implementation and scaling

• Brickclay offers customized EDW solutions. These solutions are designed to meet the unique needs and objectives of each organization. Whether building a new EDW or enhancing existing infrastructure, our team works closely with clients. We develop customized solutions that align with their business goals.
• Brickclay provides comprehensive end-to-end implementation services for EDW projects. This spans from strategy and planning to deployment and maintenance. We leverage industry best practices and cutting-edge technologies. This ensures seamless integration of data sources, optimal performance, and scalability of the EDW environment.
• As data volumes grow and user requirements evolve, scalability and performance optimization become critical for EDW environments. Brickclay helps organizations scale their EDW infrastructure horizontally or vertically. We also optimize query performance and fine-tune system configurations. This effectively meets growing demands.

Data quality and governance

• Brickclay specializes in data integration and ETL (Extract, Transform, Load) processes. This enables organizations to consolidate data from disparate sources into their EDW. By streamlining data ingestion, cleansing, and transformation, Brickclay ensures data quality and integrity. This lays a solid foundation for accurate and reliable analytics.
• Effective metadata management is essential for ensuring data quality, lineage, and compliance within the EDW. Brickclay assists organizations in implementing robust metadata management frameworks and governance processes. This enables clients to track data lineage, enforce data standards, and adhere to regulatory requirements.

Advanced analytics and reporting

• Brickclay empowers organizations to derive actionable insights from their data. This is done through advanced analytics and reporting capabilities. We leverage state-of-the-art Business Intelligence (BI) tools and techniques. This enables users to explore data visually and generate interactive reports. They gain deeper insights into key business metrics and trends.

Ready to unlock the power of your data? Contact Brickclay today. We offer tailored enterprise data warehouse solutions that drive informed decision-making and business success.