Types of Data Warehouse

Host-Based (MVS) Data Warehouses

MVS (Multiple Virtual Storage) is a mainframe operating system developed by IBM. Host-based data warehouses running on MVS systems are designed to handle large-scale data processing & analytics workloads. These data warehouses leverage the robust architecture & high-performance capabilities of mainframe systems to deliver reliable & efficient data warehousing solutions.

MVS-based data warehouses has many advantages, like:

1. Scalability: MVS systems can scale vertically by adding more processing power, memory, & storage to a single machine. This allows organizations to handle increasing data volumes & complex queries without compromising performance.
 

2. Reliability: MVS systems are known for their stability & reliability. They provide features like fault tolerance, data integrity, & disaster recovery, ensuring that the data warehouse remains available & accessible even in the face of hardware or software failures.
 

3. Security: MVS-based data warehouses offer advanced security features, including access control, data encryption, & auditing capabilities. These mechanisms help protect sensitive data & maintain compliance with industry regulations.
 

4. Integration: MVS systems can seamlessly integrate with other mainframe technologies & tools, such as IBM DB2, CICS (Customer Information Control System), & IMS (Information Management System). This enables organizations to leverage their existing mainframe investments & build comprehensive data warehousing solutions.
 

Note: However, it's important to remember that MVS-based data warehouses may require specialized skills & knowledge to manage & maintain effectively. Organizations need to have a team of experienced mainframe professionals who are well-versed in MVS systems & data warehousing practices.

Host-Based (UNIX) Data Warehouses

UNIX is a popular operating system known for its stability, scalability, & performance. Host-based data warehouses running on UNIX systems offer a robust platform for storing & analyzing large volumes of data. These data warehouses leverage the power & flexibility of UNIX servers to deliver high-performance data warehousing solutions.

Important features of host-based (UNIX) data warehouses are:

1. Scalability: UNIX systems can scale horizontally by adding more servers to a cluster, allowing organizations to distribute the data processing workload across multiple machines. This enables the data warehouse to handle increasing data volumes & user queries efficiently.
 

2. Performance: UNIX servers are designed to handle demanding workloads & provide fast query response times. They utilize advanced technologies like parallel processing, data partitioning, & query optimization to ensure optimal performance, even when dealing with complex queries & large datasets.
 

3. Flexibility: UNIX systems offer a wide range of tools & utilities for data management, ETL (Extract, Transform, Load) processes, & analytics. This flexibility allows organizations to customize their data warehousing solutions based on their specific requirements & integrate with various data sources & applications.
 

4. Cost-effectiveness: Compared to mainframe systems, UNIX servers are generally more cost-effective, especially for small to medium-sized organizations. They provide a balance between performance & affordability, making them a viable option for many businesses.
 

5. Skill availability: UNIX systems are widely used in the IT industry, & there is a large pool of professionals with UNIX skills. This makes it easier for organizations to find & hire talent to manage & maintain their UNIX-based data warehouses.
 

Note: It's important to keep in mind that UNIX-based data warehouses may require regular maintenance, patching, & updates to ensure optimal performance & security. Organizations need to have a skilled IT team or rely on managed services to handle the ongoing management & support of their UNIX-based data warehousing infrastructure.

LAN-Based Workgroup Data Warehouses

LAN-based workgroup data warehouses are designed to serve the needs of specific departments or workgroups within an organization. These data warehouses are typically implemented on local area networks (LANs) & provide a dedicated environment for storing & analyzing data relevant to a particular business unit or function.

Let’s see some of the key characteristics of LAN-based workgroup data warehouses:

1. Departmental focus: Workgroup data warehouses are tailored to meet the specific requirements of a department or business unit. They contain data that is relevant to the workgroup's activities, such as sales, marketing, finance, or human resources. This focused approach allows the workgroup to have quick access to the data they need for analysis & decision-making.
 

2. Smaller scale: Compared to enterprise-wide data warehouses, workgroup data warehouses are smaller in scale. They deal with a subset of the organization's data & serve a limited number of users within the department. This smaller scale makes them easier to implement, manage, & maintain.
 

3. Faster deployment: LAN-based workgroup data warehouses can be deployed relatively quickly since they have a narrower scope & fewer data sources to integrate. This faster deployment enables workgroups to start utilizing the data warehouse sooner & derive value from their data analytics initiatives.
 

4. Customization: Workgroup data warehouses can be customized to meet the specific needs & requirements of the department. They can be designed with the workgroup's data model, business rules, & reporting requirements in mind. This customization ensures that the data warehouse aligns closely with the workgroup's goals & objectives.
 

5. Improved performance: Since workgroup data warehouses serve a smaller user base & handle a limited dataset, they can often provide better performance compared to larger, enterprise-wide data warehouses. Queries & reports can be executed faster, enabling users to access the information they need in a timely manner.
 

Note: Just like every other type, It's important to keep in mind that LAN-based workgroup data warehouses may have some limitations. They may not be suitable for cross-departmental analysis or enterprise-wide reporting. Additionally, managing multiple workgroup data warehouses across an organization can lead to data silos & inconsistencies if not properly coordinated.

Host-Based Single Stage (LAN) Data Warehouses

Host-based single-stage data warehouses, also known as LAN-based single-stage data warehouses, are designed to consolidate data from various sources into a centralized repository within a local area network (LAN) environment. These data warehouses provide a single point of access to integrated & cleansed data for analysis & reporting purposes.

The main features of host-based single-stage data warehouses are:

1. Data integration: Single-stage data warehouses gather data from multiple source systems, such as transactional databases, operational systems, & external data sources. The data is extracted, transformed, & loaded (ETL) into the data warehouse, ensuring data consistency & quality.
 

2. Centralized data repository: The data warehouse serves as a central repository for all the integrated data. It provides a single version of the truth, eliminating data silos & inconsistencies across different systems. Users can access the data warehouse to obtain reliable & up-to-date information for their analysis & reporting needs.
 

3. LAN-based architecture: Host-based single-stage data warehouses are implemented on a local area network (LAN) infrastructure. This allows users within the organization to access the data warehouse using the existing network setup, without the need for complex network configurations or remote access.
 

4. Simplified data modeling: Single-stage data warehouses often employ a simplified data modeling approach, such as a dimensional model or a star schema. These models are designed to facilitate easy understanding & querying of the data, enabling users to quickly retrieve the information they need.
 

5. Improved query performance: By consolidating data into a single stage, host-based single-stage data warehouses can provide faster query performance compared to accessing data from multiple source systems. The data warehouse is optimized for querying & reporting, with indexing & aggregation techniques applied to speed up data retrieval.

Note: Host-based single-stage data warehouses may have some limitations. They may not be suitable for handling extremely large datasets or supporting real-time data processing.Moreover, as the data volume grows, the scalability of single-stage data warehouses may become a challenge, requiring additional hardware & processing power.

Limitations

Data warehouses offer many benefits,if we use them but just like every other tool, they also have some limitations that organizations should be aware of. 

Let’s see some of the limitations: 
 

1. High implementation costs: Building & maintaining a data warehouse can be expensive. It requires significant investments in hardware, software, & skilled personnel. Organizations need to allocate sufficient budget & resources to ensure the successful implementation & ongoing operation of their data warehousing infrastructure.
 

2. Complex data integration: Integrating data from multiple sources into a data warehouse can be a complex & time-consuming process. Data from different systems may have inconsistent formats, structures, & quality, requiring extensive data cleansing & transformation efforts. Ensuring data consistency & accuracy across the data warehouse can be challenging.
 

3. Data latency: Data warehouses typically rely on batch processing to load data from source systems. This means that there may be a delay between the time data is generated in the source systems & when it becomes available in the data warehouse. Real-time or near-real-time data access may not be possible in all cases, which can impact decision-making in time-sensitive situations.
 

4. Scalability challenges: As data volumes grow, the scalability of data warehouses can become a concern. Traditional data warehouses may struggle to handle the increasing data size & complexity, leading to performance degradation. Scaling the data warehouse infrastructure to accommodate the growing data needs can be costly & require significant hardware & software upgrades.
 

5. Limited flexibility: Data warehouses are designed to support predefined queries & reporting requirements. They may not be as flexible as other data storage solutions when it comes to handling ad-hoc queries or unstructured data. Modifying the data warehouse schema to accommodate new data sources or changing business requirements can be a complex & time-consuming process.
 

6. Skill requirements: Managing & maintaining a data warehouse requires specialized skills & knowledge. Organizations need to have a team of experienced data warehouse professionals, including data architects, ETL developers, & database administrators. Finding & retaining talent with the necessary skills can be challenging & expensive.

Multi-Stage Data Warehouses

Multi-stage data warehouses are designed to address some of the limitations of single-stage data warehouses. They introduce additional stages or layers between the data sources & the final data warehouse to improve data processing, storage, & analysis capabilities. 

Let’s discuss the main characteristics of multi-stage data warehouses:

1. Staging area: The first stage in a multi-stage data warehouse is the staging area. It serves as a temporary storage location for data extracted from various source systems. The staging area allows for data validation, cleansing, & transformation before the data is loaded into the main data warehouse. This ensures data quality & consistency.

2. Data integration: Multi-stage data warehouses facilitate data integration from multiple sources. The staging area acts as a central point for consolidating & harmonizing data from different systems. Data integration techniques, such as ETL (Extract, Transform, Load) processes, are applied to combine & transform the data into a consistent format.
 

3. Data mart layer: After the staging area, the data is loaded into the data mart layer. Data marts are subject-specific or department-specific subsets of the data warehouse. They are designed to serve the analytical needs of specific business units or functional areas. Data marts provide faster query performance & easier data access for end-users.
 

4. Aggregation & summarization: Multi-stage data warehouses often include an aggregation & summarization layer. This layer pre-calculates & stores summarized data at various levels of granularity. Aggregated data helps improve query performance by reducing the amount of data that needs to be processed at runtime. It enables faster retrieval of high-level summary information.
 

5. Data archiving: Multi-stage data warehouses may incorporate a data archiving stage. This stage is used to store historical or infrequently accessed data. Archiving older data helps manage the size of the main data warehouse & improves query performance by keeping the active dataset smaller. Archived data can still be accessed when needed for historical analysis or regulatory compliance.
 

6. Improved scalability: By distributing data processing & storage across multiple stages, multi-stage data warehouses offer better scalability compared to single-stage architectures. Each stage can be scaled independently based on the specific requirements, allowing for more efficient resource utilization & performance optimization.

Stationary Data Warehouses

Stationary data warehouses are designed to store & manage data that remains relatively stable over time. They are suitable for situations where the data is not frequently updated or modified. 

The  main characteristics of stationary data warehouses are : 
 

1. Historical data storage: Stationary data warehouses are primarily used to store historical data that is no longer actively updated. This data is typically used for long-term analysis, trend identification, & decision-making purposes. The data in a stationary data warehouse represents a snapshot of the data at a specific point in time.
 

2. Infrequent data updates: Unlike operational systems or real-time data warehouses, stationary data warehouses do not require frequent data updates. The data is loaded into the warehouse periodically, such as daily, weekly, or monthly, depending on the business requirements. Once the data is loaded, it remains unchanged unless there is a need for historical data corrections or updates.
 

3. Read-only access: Stationary data warehouses are designed for read-only access. Users can query & retrieve data from the warehouse for analysis & reporting purposes, but they cannot modify or update the data directly. This ensures data integrity & consistency, as the data remains unchanged once it is loaded into the warehouse.
 

4. Optimized for query performance: Since stationary data warehouses are primarily used for querying & analysis, they are optimized for fast query performance. Indexing techniques, data partitioning, & pre-aggregation strategies are applied to improve query response times. The data is often structured in a way that facilitates efficient data retrieval & analysis.
 

5. Long-term data retention: Stationary data warehouses are designed to retain data for extended periods, often several years or even decades. They provide a historical repository that can be used for trend analysis, regulatory compliance, & long-term decision-making. The data in a stationary warehouse is rarely purged or deleted unless there are specific data retention policies in place.
 

6. Lower maintenance requirements: Compared to real-time or frequently updated data warehouses, stationary data warehouses have lower maintenance requirements. Since the data is not constantly changing, there is less need for frequent ETL processes, data validation, & updates. This reduces the operational overhead & simplifies the management of the data warehouse.

Distributed Data Warehouses

Distributed data warehouses are designed to store & manage data across multiple physical locations or servers. They provide a scalable & flexible approach to data warehousing, allowing organizations to handle large volumes of data & support geographically dispersed users. 

The main characteristics of distributed data warehouses are:

1. Data distribution: In a distributed data warehouse, data is spread across multiple servers or nodes. Each node holds a portion of the overall data, & the data is distributed based on various factors such as geographical location, data type, or business unit. This distribution allows for parallel processing & improved performance by leveraging the resources of multiple servers.
 

2. Parallel processing: Distributed data warehouses enable parallel processing of queries & data loading operations. Queries can be executed simultaneously across multiple nodes, allowing for faster query response times. Similarly, data loading processes can be distributed across nodes, reducing the time required to load large volumes of data into the warehouse.
 

3. Scalability: Distributed data warehouses offer excellent scalability options. As data volumes grow, additional nodes can be added to the distributed architecture to handle the increased data storage & processing requirements. This horizontal scalability allows organizations to accommodate growing data needs without significant performance degradation.
 

4. High availability: Distributed data warehouses provide high availability by distributing data & processing across multiple nodes. If one node fails or becomes unavailable, the other nodes can continue to serve user queries & maintain data accessibility. This ensures that the data warehouse remains operational even in the event of hardware or network failures.
 

5. Data localization: Distributed data warehouses allow for data localization, where data can be stored closer to the users or applications that frequently access it. This can improve query performance by reducing network latency & data transfer costs. Data localization is particularly beneficial for organizations with geographically dispersed users or data centers.
 

6. Complex data integration: Distributed data warehouses can handle complex data integration scenarios. Data from various sources can be distributed across different nodes based on predefined criteria. This enables efficient data integration & processing, as each node can focus on a specific subset of data or processing tasks.
 

7. Data governance & security: Distributed data warehouses require robust data governance & security measures. Consistency & integrity of data across nodes must be maintained through appropriate data synchronization & replication techniques. Access control, data encryption, & other security mechanisms need to be implemented to ensure data protection & compliance with regulatory requirements.

Characteristics of Local Data Warehouses

Local data warehouses, also known as departmental or small-scale data warehouses, are designed to serve the specific needs of individual departments or business units within an organization. 

The main characteristics of local data warehouses are:

1. Departmental focus: Local data warehouses are tailored to meet the unique requirements of a particular department or business unit. They contain data that is relevant to the department's operations, such as sales, marketing, finance, or human resources. This focused approach allows the department to have quick access to the data they need for analysis & decision-making.
 

2. Limited data scope: Compared to enterprise-wide data warehouses, local data warehouses have a limited data scope. They only include data that is specific to the department's functions & objectives. This narrower data scope makes it easier to manage & maintain the data warehouse, as there are fewer data sources & less data volume to handle.
 

3. Faster implementation: Local data warehouses can be implemented relatively quickly compared to larger, enterprise-wide data warehouses. Since they have a smaller data scope & fewer stakeholders involved, the development & deployment process can be streamlined. This allows departments to start utilizing the data warehouse sooner & derive value from their data analytics initiatives.
 

4. Customized data models: Local data warehouses often employ customized data models that are specific to the department's needs. The data model is designed to reflect the department's business processes, metrics, & reporting requirements. This customization ensures that the data warehouse aligns closely with the department's goals & information needs.
 

5. Improved data access & performance: Local data warehouses provide faster data access & query performance compared to accessing data from a centralized, enterprise-wide data warehouse. Since the data volume is smaller & the data model is optimized for the department's needs, queries can be executed more efficiently, enabling users to retrieve the information they need quickly.
 

6. Data autonomy: Local data warehouses give departments a level of data autonomy. They have control over the data contained within their data warehouse & can make decisions regarding data management, data quality, & data governance specific to their department. This autonomy allows departments to be more agile & responsive to their data-related needs.
 

7. Integration with departmental tools: Local data warehouses can be easily integrated with department-specific tools & applications. This integration allows users to access & analyze data using the tools they are familiar with, such as business intelligence platforms, reporting tools, or data visualization software. Seamless integration enhances user adoption & productivity.

Disadvantages

1. Data silos: Local data warehouses can lead to the creation of data silos within an organization. Each department may have its own data warehouse, leading to a fragmented view of the organization's data. This can result in inconsistencies, duplication of data, & difficulty in obtaining a holistic view of the business.
 

2. Limited data sharing: Local data warehouses may hinder data sharing & collaboration across departments. If each department has its own data warehouse, it becomes challenging to share data & insights with other departments. This lack of data sharing can lead to missed opportunities for cross-functional analysis & decision-making.
 

3. Inconsistent data definitions: When multiple local data warehouses exist, there is a risk of inconsistent data definitions & metrics across departments. Each department may have its own way of defining & calculating key performance indicators (KPIs), leading to confusion & discrepancies when comparing data from different sources.
 

4. Duplication of efforts: Local data warehouses can result in duplication of efforts in terms of data integration, data cleansing, & data management. Each department may have its own processes for extracting, transforming, & loading data into their data warehouse. This duplication of efforts can be inefficient & lead to increased maintenance costs.
 

5. Limited scalability: Local data warehouses may have limited scalability compared to enterprise-wide data warehouses. As data volumes grow & the need for cross-departmental analysis increases, local data warehouses may struggle to accommodate the expanding data requirements. Scaling local data warehouses can be complex & costly.
 

6. Difficulty in maintaining consistency: Ensuring data consistency across multiple local data warehouses can be challenging. Changes to data sources, data structures, or business rules may need to be replicated across all the local data warehouses, requiring coordination & effort. Inconsistencies can arise if updates are not applied uniformly across all the data warehouses.
 

7. Lack of enterprise-wide reporting: Local data warehouses may not support enterprise-wide reporting & analysis. Generating reports that span multiple departments or require data from different local data warehouses can be complex & time-consuming. This limitation can hinder the ability to make informed decisions at an enterprise level.

Frequently Asked Questions

What is the main difference between a data warehouse & a database?

A data warehouse is designed for data analysis & reporting, while a database is used for transaction processing & real-time data management.

How do data warehouses handle data from multiple sources?

Data warehouses use ETL (Extract, Transform, Load) processes to integrate & consolidate data from various sources into a centralized repository.

Can data warehouses support real-time data processing?

Traditional data warehouses are designed for batch processing, but modern data warehousing solutions can support near-real-time data processing using techniques like change data capture (CDC) & streaming data integration.

Conclusion

In this article, we explained the different types of data warehouses, like host-based, LAN-based, multi-stage, stationary, distributed, & local data warehouses. Each type has its own characteristics, advantages, & limitations. These different architectures helps organizations choose the most suitable data warehousing solution based on their specific requirements, data volumes, scalability needs, & business objectives. 

You can also check out our other blogs on Code360.