Cloud Data Warehouse Comparison: Redshift vs. BigQuery vs. Azure vs. Snowflake for Real-Time Workloads

 

Data helps companies take the guesswork out of decision-making. Teams can use data-driven evidence to decide which products to build, which features to add, and which growth initiatives to pursue. And, such insights-driven businesses grow at an annual rate of over 30%.

But, there’s a difference between being merely data-aware and insights-driven. Discovering insights requires finding a way to analyze data in near real-time, which is where cloud data warehouses play a vital role. As scalable repositories of data, warehouses allow businesses to find insights by storing and analyzing huge amounts of structured and semi-structured data.

And, running a data warehouse is more than a technical initiative. It’s vital to the overall business strategy and can inform an array of future product, marketing, and engineering decisions.

But, choosing a cloud data warehouse provider can be challenging. Users have to evaluate costs, performance, the ability to handle real-time workloads, and other parameters to decide which vendor best fits their needs.

To help with these efforts, we analyze four cloud data warehouses: Amazon Redshift, Google BigQuery, Azure Synapse Analytics, and Snowflake. We cover the pros and cons of each of these options and dive into the factors you’ll need to consider when choosing a cloud data warehouse.

Popular Cloud Data Warehouses

Many of today’s new cloud data warehouses are built using solutions from major vendors such as Amazon Redshift, Google BigQuery, Microsoft Azure Synapse Analytics, and Snowflake.

Major vendors differ in costs or technical details, but they also share some common traits. Their cloud data warehouses are highly reliable. While outages or failures might happen, data replication and other reliability features ensure your data is backed up and can be quickly retrieved.

Amazon, Google, Microsoft, and Snowflake also offer highly scalable cloud data warehouses. Their solutions use massively parallel processing (MPP), a storage structure that handles multiple operations simultaneously, to rapidly scale up or down storage and compute resources. And, data is stored in columnar format to achieve better compression and querying.

Compared to on-premise data warehouses, cloud alternatives are more scalable, faster, go live in minutes, and are always up to date.

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