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Introduction

A data warehouse is a data store used to store and analyze large amounts of data from multiple sources. It is designed to support business intelligence and decision-making processes.

Key Characteristics

Structured data
Complex queries
Transactions
Joins

CAP Theorem

General CAP Theorem Handling

A data warehouse is designed to handle the CAP theorem by prioritizing consistency and availability over partition tolerance.

Guarantee of Consistency

A data warehouse provides strict consistency, which means that updates to the database are immediately visible to all nodes in the system.

Guarantee of Availability

A data warehouse achieves high availability by replicating data across multiple nodes in the system. If one node fails, another node can take over without any downtime.

Guarantee of Partition Tolerance

A data warehouse is not designed to handle partition tolerance. If a network partition occurs, the system may become unavailable.

Usage

Best Usage

The best data warehouse usage is for applications that require complex data analysis and reporting, such as business intelligence, financial analysis, and customer analytics.

Neutral Usage

A data warehouse can also be used for applications that require structured data storage and retrieval, such as inventory management systems, customer relationship management systems, and e-commerce websites.

Worst Usage

The worst data warehouse usage is for applications requiring real-time data processing or high-write workloads.

System Design Role

A data warehouse is well-suited for distributed systems that require complex data analysis and reporting. It can be used as a primary or secondary data store for backup and archiving.

Data Model

A data warehouse uses a relational data model, where data is organized into tables with defined relationships between them. The advantages of this data model are flexibility and support for complex queries. The disadvantages are limited scalability and difficulty in managing large amounts of data.

Query Language

A data warehouse uses SQL as the query language. The advantages of this query language are support for complex queries and ease of use. The disadvantages are limited scalability and difficulty in managing large amounts of data.

Scalability

How to Make it Performant

Indexing is used to optimize query performance to make a data warehouse performant. This involves creating indexes of the data optimized for the queries that will be performed.

High Traffic Handling

A data warehouse can handle high traffic by replicating data across multiple nodes in the system and using load balancing to distribute queries evenly across the nodes. It is well-suited for high-read workloads but may not be as well-suited for high-write workloads.

How to Scale it

A data warehouse can be scaled horizontally by adding more nodes to the system. Sharding is recommended to distribute data across the nodes evenly.

Usage in Distributed Systems

A data warehouse can be used in a distributed system by replicating data across multiple nodes and using load balancing to distribute queries evenly across the nodes. Specific considerations to keep in mind include data consistency, network latency, and fault tolerance.

Replication

A data warehouse handles replication by replicating data across multiple nodes in the system. Best practices for replication include using a master-slave or master-master replication model, monitoring replication lag, and using automatic failover.

In Practice

Best Practices

When using a data warehouse, best practices include optimizing queries, monitoring performance, using appropriate indexing strategies, and following security best practices.

Common Pitfalls

Common pitfalls to avoid when using a data warehouse include over-indexing, under-indexing, not monitoring performance, not following security best practices, and not optimizing queries.

Examples
Amazon Redshift
Google BigQuery
Microsoft Azure Synapse Analytics