Cloud data warehouse

What is cloud data warehouse?

Cloud data warehouse is a storage repository which stores your organizational data in the cloud rather than on-premise infrastructure. Cloud data warehousing offers flexible, scalable, and cost-effective solutions for data storage, querying, and analytics. Some examples of cloud data warehouses are Amazon Redshift, Google BigQuery, Snowflake, Microsoft Azure Synapse, etc., which are some of the best cloud data warehouses. 

Architecture of cloud data warehouses

Cloud data warehousing architecture comes with seven components, namely distributed data ingestion, storage, compute, querying, data management, security and governance, and integration. 

Data ingestion - Data analysts use ETL pipelines and connectors to ingest data into data warehouses through batch processing or stream data processing.

Storage - the main function of the cloud data warehouse is storing structured data in the cloud layer. The main difference between traditional data storage and cloud data warehouses is that the latter separates storage from infrastructure. Hence, you could handle scaling much efficiently without slowing down the performance.

Another advantage of cloud storage is columnar storage, where data is stored in columns rather than rows, which is effective for fast processing and querying.

Compute - The next layer of cloud data warehouse is the compute layer. It takes care of resource allocation (CPU and memory), which is required when a user is running queries and accessing stored data. Similar to storage, compute could be also scaled up and down, which makes cloud data warehouses more cost-effective.

Querying - Cloud data warehouses support standard SQL or other query languages you need for querying data. This way, you could access any dataset from massive storage much faster. 

Another characteristic of cloud data warehouses is its massively parallel processing capabilities, which signifies distributed query execution across multiple nodes.

Data management - Cloud data warehouses also handle data processing automatically, like indexing, partitioning, clustering, metadata management, data lineage tracking, etc. 

Security and governance - It’s much easier to access data from cloud data warehouses from anywhere. To ensure that it’s secure access, it comes with data encryption, role-based access control, and other authentication factors. Your security and governance team could ensure that you are in compliance with GDPR, HIPAA, and other regulations. 

Integration - To get maximum value from your stored data, you need to connect and integrate data storage with other systems. Cloud data warehouses help you connect seamlessly with business intelligence platforms, AI and ML systems, etc to run advanced AI systems and real-time analytics.

Benefits of cloud data warehousing

The increasing volumes of data across industries make cloud computing and cloud data warehousing the best choice for smooth data management. Here are some benefits of cloud data warehousing for your business.

Scalability: Cloud data warehouses are both flexible and scalable. Whether you want to increase or decrease your storage capacity and processing capabilities, cloud data warehouses can support.

Cost-effective: You could pay only for the resources you use with cloud data warehouses and upscale or downscale whenever required.

High-performance: You can get speed and performance, even while handling complex queries involving large data volumes. 

Better accessibility: Anyone could access data without having to be physically located on the premises. Companies with global locations can have better yet secure access to data.

No maintenance burden: You don’t need extra people or effort to manage cloud resources, perform maintenance and updates. Any backups, security patches, and optimization efforts happen automatically as well.

Types of cloud data warehouse 

Cloud data warehouses can be of three types, depending on the type of cloud.

1. Public cloud data warehouse, which is offered by a managed cloud provider to multiple customers/organizations on pay-as-you-go usage. Also, known as a multi-tenant environment, data stored here will be made available to the public, hence it’s ideal for non-critical data. Examples: Amazon RedShift, Snowflake, etc.

2. Private cloud data warehouse, is a cloud resource dedicated for a single organization and is suitable for storing sensitive and critical data. Examples: IBM Cloud Private for data.

3. Hybrid cloud data warehouse is when an organization uses hybrid resources, combining the scalability and ease-of-access of cloud and security and exclusivity of on-premise resources. Example: Azure Synapse Analytics which allows both on-premise and cloud data storage.

4. Serverless cloud data warehouse is also a type, where the users don’t have to worry about scaling resources and can use compute and storage as required. And the cloud providers fully take care of provisioning, scaling up, and down. 

5. Distributed cloud data warehouse is when the cloud storage is distributed across multiple locations. 

Cloud data warehouses are important for any organization to manage large data volumes effortlessly and build real-time analytics and AI use cases. Check out more about cloud data warehousing, cost, and planning required for cloud data warehousing.