What is a distributed data warehouse (DWH)?

Key takeaways:

1. A single centralized data warehouse (DWH) faces limitations like scalability challenges, performance bottlenecks, and high maintenance costs, making it less suitable for large-scale or real-time data processing.

2. Distributed data warehouses (DWHs) offer enhanced scalability, real-time analytics, and fault tolerance by distributing data across multiple nodes.

3. There are two main types of distributed DWHs: geographically distributed, which allows both local and global data access, and technologically distributed, which focuses on cost-effectiveness and scalability.

4. Each type has its own set of advantages and challenges, such as data synchronization issues or complex query optimization. Choosing the right distributed DWH platform depends on an organization’s specific needs, including scalability, cost, and data handling requirements.

Many organizations build and maintain a centralized data warehouse. The data present in that single warehouse is integrated across the corporation, usually with an integrated view at the headquarters; this supports operating on a centralized business model. It is also advisable to use it when the data volume is relatively small, because there may be no practical benefit in dividing it into separate data stores. A single centralized data warehouse simplifies data management and reduces complexity. However, there are situations where distributed data warehouses are beneficial.

A single centralized data warehouse (DWH) faces several limitations, including scalability challenges, performance bottlenecks, high maintenance costs, a single point of failure, data latency, and limited flexibility in handling modern data needs. As data volumes grow and real-time processing demands increase, these limitations make centralized systems less viable for many organizations.

This has led to the need for distributed data warehouses, which offer enhanced scalability, improved performance, fault tolerance, real-time analytics, cost efficiency, and flexibility by distributing data and processing across multiple nodes or servers. Distributed DWHs leverage cloud-based or multi-node architectures, ensuring faster query response times, higher availability, and the ability to integrate diverse data sources.

Understanding local and global DWHs

A local warehouse contains data specific to a particular department or project within an organization and caters to the data needs of that particular entity.

A global warehouse is a centralized repository that holds all the data and provides a comprehensive view of data across the entire organization. It supports cross-functional analysis and decision-making, while ensuring data consistency.

Characteristics

Let’s explore some key features of a distributed data warehouse.

1. Data storage across multiple locations: A distributed DWH stores data across various physical locations, improving reliability and availability by avoiding a central point of failure.

2. Concurrent accessibility: Multiple users can access and modify data simultaneously without conflicts, ensuring data consistency and integrity.

3. Synchronization of data: Changes made at one site are reflected across all other sites, ensuring users always have access to the most up-to-date information.

4. Scalability: A distributed DWH can easily expand by adding more sites as data volume or user numbers increase.

5. Transparency: Users can interact with a distributed DWH as if it were a single, integrated system, without needing to understand the complexities of the underlying distributed architecture.

Before diving into the details of the types of distributed data warehouses, let’s cover some essential concepts to better understand the topic.

Types of distributed DWH

There are two types of distributed DWHs: Geographically distributed and Technologically distributed.

Geographically distributed

Geographically distributed DWHs are suitable for corporations with a global presence, especially when the business’ operations are based in different geographical locations or encompass varying product lines. This approach involves the use of both local and global data warehouses. This approach is beneficial when access to both local and global data is required and if a significant chunk of processing occurs at the local level.

• Advantages:

  • Local and global accessibility: Geographically distributed DWHs are ideal for multinational corporations, because they allow both local branches and headquarters to access data efficiently. This ensures that decision-makers at various levels can obtain the information they need.

  • Local autonomy: Branches of an organization with a geographically distributed setup often require autonomy in data management and processing to enable them to customize data handling to their specific needs and the local regulations.

• Disadvantages:

  • Data synchronization: Maintaining data consistency across the local and global data warehouses can be challenging, because updating and combining data with different formats may lead to discrepancies or errors.

  • Complexity: The management of multiple data instances is required to ensure that they align with the global standards. This can increase complexity, as well as the risk of data quality issues.

Technologically distributed

Technologically distributed DWHs are required when dealing with extensive datasets distributed across multiple processors. It involves logically having one data warehouse, but physically storing data across multiple interconnected data stores/data warehouses.

• Advantages:

  • Cost-effectiveness: Technologically distributed DWHs offer cost advantages, because they eliminate the need for a large centralized hardware infrastructure. Organizations can scale by adding servers as needed.

  • Scalability: Adding more servers to the network allows organizations to handle the increasing data volumes and analytical workloads effectively.

• Disadvantages:

  • Network data communication: As the data warehouse expands, efficient network communication and data transmission becomes essential. Querying across multiple nodes or servers may lead to data transportation challenges and may severely impact the query performance.

  • Complex query optimization: Managing complex queries that involve data distributed across multiple servers becomes challenging. Optimizing queries to retrieve relevant data efficiently requires implementation of query optimization techniques.

The choice between these approaches depends on the specific needs and priorities of an organization.

How to choose

When selecting a distributed data warehouse platform, it is important to assess the specific needs of the business, including the budget constraints and scalability requirements. There are also some popular technologies to choose from such as Amazon Redshift, Google BigQuery, and Snowflake.

Another consideration is the platform’s ability to handle the required data volume and query performance needs, while ensuring ease of integration with the existing tools. Evaluation of the platform’s user-friendliness and availability of community and vendor support is also important. You should choose a platform that aligns with your long-term business goals and technology strategy.

Conclusion

Distributed data warehouses provide a robust solution for organizations looking to overcome the limitations of centralized DWHs, offering flexibility, scalability, and performance improvements. With options like geographically and technologically distributed DWHs, businesses can tailor their data architecture to suit global operations or large-scale data processing. The choice of platform should be aligned with the organization’s long-term goals, ensuring scalability and ease of integration with existing systems.