Many business-critical applications, such as databases, ERPs, and marketing apps, have all moved to the cloud with the introduction of modern cloud infrastructure. As a result, the majority of business-critical data is now stored in the cloud. Companies want a data warehouse to store data from all of their cloud-based applications now that all of their company data is in the cloud so Cloud Data Warehouse is becoming essential.
This article seeks to explain what is cloud data warehouse, its benefits and how it is different from the traditional data warehouse. Also, top vendors in the markets.
What is a Cloud Data Warehouse?
A cloud data warehouse is a database that is optimised for analytics, scale, and usability and is supplied as a managed service on the public cloud. Due to enhanced access, scalability, and speed, cloud-based data warehouses allow firms to focus on running their operations rather than managing a server room. They enable business intelligence teams to produce faster and better insights.
Why It Matters
For decades, data warehouses have been a mainstay of enterprise analytics and reporting. However, they were not built to handle today’s massive data expansion or to keep up with end users’ ever-changing requirements.
You are no longer bound by physical data centres with cloud data warehousing, and you can now dynamically build or downsize your data warehouses to meet changing company budgets and requirements. A cloud data warehouse, like a typical data warehouse, saves data from a range of diverse data sources, including IoT, CRM, finance systems, and others.
A cloud-based data warehouse’s data is highly structured and unified, making it suitable to support a wide range of business intelligence and analytics use cases.
Key Benefits of Cloud Data Warehouse
At a fraction of the cost of old systems, modern cloud designs integrate the power of data warehousing, the flexibility of big data platforms, and the elasticity of the cloud. We’ll go over the benefits of a cloud-based data warehouse over a traditional data warehouse in terms of performance, scalability, and cost.
- Faster Insights: A cloud data warehouse has more computing capacity and can perform real-time cloud analytics using data from various sources considerably faster than an on-premises data warehouse, allowing business users to get better insights faster.
- Scalability: A cloud-based data warehouse provides near-infinite capacity and is simple to increase as your storage requirements grow. You won’t have to buy additional hardware to expand your cloud storage, unlike an on-premises data warehouse, and you’ll pay a fraction of the price.
- Overhead: Running an on-premises data warehouse necessitates a specialised server room with expensive technology, as well as experienced staff to manage, manually upgrade, and debug problems. The operational costs of a cloud data warehouse are much lower because it does not require physical gear or dedicated office space.
Data Warehouse vs Cloud Data Warehouse
A traditional data warehouse is a type of on-premise data warehouse that is housed in a company’s office. Companies must purchase their own hardware, such as servers. The installation necessitates the use of human resources as well as a significant amount of time.
Data Warehouse must be managed and updated by a distinct team within the firm. It takes time to scale the Warehouse since additional hardware must be sent to the destination and then installed.
Cloud Data Warehouse is a cloud-based data warehouse system. Companies are not required to own and maintain hardware. Third-party Cloud Data Warehouse Service providers such as Google BigQuery, Snowflake, and others handle all hardware updates, maintenance, and scalability.
Companies can simply combine Cloud Data Warehouses with other SaaS (Software as a Service) platforms and tools for Business Analytics due to the availability of data in the cloud.
Top Cloud Data Warehouse Vendors
There are plenty of popular cloud data warehouse platforms to select from, including Amazon Redshift, Google BigQuery, Microsoft Azure, Snowflake, etc and to select the appropriate one for your business we are going to compare four of the most popular vendors on the basis of characteristics like cost, scalability, architecture, security features, speed, and other criteria-
1. Google BigQuery
BigQuery is a fully managed, serverless data warehouse that expands automatically to meet storage and processing power requirements. BigQuery hides much underlying hardware, database, nodes, and configuration details since Google doesn’t expect you to handle your data warehouse architecture. Its flexibility is ready to use right out of the package. It’s as simple as creating a Google Cloud Platform (GCP) account, loading a table, and running a query to get started. The remainder is taken care of by Google.
BigQuery is a columnar and ANSI SQL database that can process terabytes to petabytes of data at breakneck speed. With BigQuery GIS, you can do spatial analysis using familiar SQL, with BigQuery ML, you can also quickly develop and operationalize machine learning models on large-scale structured or semi-structured data using simple SQL and BigQuery BI Engine can also offer real-time interactive dashboarding.
2. Amazon Redshift
While it is the most mature and feature-rich, its limits are the most similar to those of a standard data warehouse. This makes it the most difficult to manage, and it’s not well suited for newer use cases or workload separation.
The first step in constructing a Redshift data warehouse is to launch an Amazon Redshift cluster, which is a collection of nodes. You upload your data set after you’ve provisioned your cluster and then run data analysis queries. Using standard SQL-based tools and business intelligence applications, Amazon Redshift enables rapid query performance regardless of the size of your data set.
3. Microsoft Azure
Azure Synapse Analytics is a newer analytics service that combines enterprise data warehousing and big data analytics into one package. It allows you to query data utilising serverless on-demand resources or supplied resources. For your business intelligence (BI) and machine learning (ML) needs, Azure Synapse provides a consistent experience for ingesting, preparing, managing, and serving data.
A cloud-native, distributed SQL processing engine is at the heart of Azure Synapse. It’s based on SQL Server and designed to handle the most demanding enterprise data warehousing tasks. Azure SQL Data Warehouse (SQL DW), like other cloud MPP solutions, splits storage and compute, charging for each individually. Azure Synapse abstracts actual machines by representing compute power in the form of data warehouse units and saves relational table data with columnar storage (DWUs). This allows your users to scale computational resources easily and seamlessly as needed.
Snowflake is a fully managed MPP cloud-based data warehouse that runs on Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Unlike the other data warehouses discussed here, Snowflake is the only one that does not have its own cloud. Snowflake offers worldwide data replication thanks to its common and interchangeable code base, which means you can migrate your data to any cloud, in any area, without having to recode your apps or learn new skills.
You can create as many virtual warehouses as you need to parallelize and isolate the performance of specific queries if you’re a Snowflake user. Snowflake achieves high concurrency by separating storage and computing, allowing multiple warehouses to access the same data source at the same time.
To conclude, this article covered everything you need to know about Cloud Data Warehouses. It also went over the benefits and requirements of a Cloud Data Warehouse, and how it is different from the data warehouse. It also includes a list of the best Cloud Data Warehouse Services available today.