BigQuery vs Redshift: Comparing Cloud Data Warehouse Solutions 

Data is arguably the digital gold when it comes to running a modern business, given that nearly 60% of companies in the world leverage data analytics to drive processes and optimize costs. Rising trends over the past few years, such as the AWS redshift database have culminated in next-level storage and computation solutions that can help businesses harness the opportunities of big data.

Talking of storage, businesses often turn to a cloud data warehouse solution, which serves as a central repository for all information collected from various sources, both internally and externally. Some of the prevalently used cloud data warehouse solutions include Amazon Redshift and Google BigQuery. But which solution is the best for your scenario? Here is an in-depth look into google BigQuery vs RedShift. Keep reading to stay updated.

What is AWS RedShift?

Redshift is part of Amazon’s cloud architecture service—Amazon Web Services (AWS) which serves as a cloud warehouse solution for businesses that leverage insights from both structured and semi-structured data sets. The primary source code for RedShift was acquired by Amazon from ParAccel, a company that was building ParAccel Analytic Database, based on PostgreSQL. And for that reason, AWS RedShift is technically massively parallel processing (MPP) data warehouse built on a PostgreSQL fork. 

However, before we compare BigQuery vs RedShift, it’s important to note that inasmuch as RedShift has a primary commonality with PostgreSQL, it features a unique column structure that leverages distribution styles and keys instead of support indexes to organize data. Moreover, this cloud warehouse solution is a unique query execution engine that deviates from PostgreSQL. 

A typical AWS RedShift infrastructure features a cluster, which can include one or multiple computer nodes. To work, a user partitions the computer nodes into slices, which are then allocated a part of the node’s disk space or memory. A user might also need a leader node to coordinate extra nodes, as well as external communication, especially if the cluster is provisioned with multiple nodes. 

What is Google Big Query?

BigQuery is an extension of the larger Google Cloud Platform (GCP) infrastructure and serves as a cloud warehouse solution for businesses. Built on top of Dremel technology, this cloud storage is among the pioneering solutions in the market, after Monet DB and C-store. Technically, Dremel serves as a query service for running SQL-like queries for faster, accurate results against large data sets.

Although BigQuery originally kept Dremel’s hybrid SQL language, the solution has since been upgraded to support standard SQL language. GCP BigQuery works alongside other unique systems and technologies to facilitate a typical task execution, including:

• Borg: includes an enterprise cluster management system that assigns resources to Dremel jobs, which are typically computed over Goggle’s REST.
• Colossus: includes a planet-scale storage solution that feeds in data to individual Dremel jobs.
• Juniper: includes an inner data network that facilitates translation and reading of data, as far as Dremel jobs are concerned.
• Capacitor: consists of a columnar storage format for organizing and compressing Dremel job data.

Cloud Data Warehouse Comparison: AWS RedShift vs. Big Query

When comparing BigQuery vs RedShift, it’s important to understand that both are cloud warehouse solutions as a service. However, there is a great difference between redshift and big query, especially when it comes to features, operations, as well as infrastructure. Here is a summarized comparison of cloud data warehouse platform in the two scenarios. 

Performance: RedShift vs. BigQuery

Performance is relative in any type of data warehouse solution, the comparison between gcpbigquery vs aws redshift notwithstanding. Typically, performance depends on schema complexity, the size of the user’s data tables, and the number of incoming simultaneous queries, among other factors. Nonetheless, a user might need greater manual configuration to ensure high availability in RedShift than BigQuery. In matters of speed, BigQuery can outperform RedShift, especially if you are leveraging a single dc2. large node.

Pricing Model: Big Query vs. RedShift AWS Pricing / AWS Redshift Costs

Aws RedShift pricing is popular in the market because it covers both storage and computation costs. There are various client options to choose from, including an in-built AWS Nitro System known as RA3, Dense Storage, or even Dense Compute node types.

Although the cheapest node dc2.large features a 160GB storage capacity cost up to $0.25 per hour, clients are advised to estimate their costs for this cloud warehouse solution using the AWS Redshift Pricing Calculator.

On the other hand, Google big query pricing model is pretty complex when compared to the AWS redshift database solution, given that the storage and query costs are separate. Clients can choose from different pricing models, including streaming inserts vs. queries vs. storage API, active vs. long-term, as well as flat-rate vs. on-demand. Users pay up to $0.020 per GB every month for storage and $5 per TB for query. Here is the GCP Pricing Calculator to help you estimate accurate costs.

Some of the Top Companies That Use AWS RedShift

Amazon RedShift serves more than 1500 users, including the following top companies:

• Amazon
• Coinbase
• Phillips
• Yelp
• Liberty Mutual Insurance

Top Companies That Use Google BigQuery

Google BigQuery has over 453 companies using the service, including:

• Spotify
• The New York Times
• Trustpilot
• Stack
• Mollie

Wrap Up: Pros and Cons of RedShift & BigQuery

When comparing BigQuery vs RedShift, it’s right to say that both cloud warehouse solutions are highly scalable on demand and allows businesses of all services to benefit from real-time data analytics at unmatched price-performance. At the same time, the service providers for both these solutions assume the responsibility of managing the database, as well as the infrastructure, allowing clients to focus on core business needs using familiar, or user-friendly SQL. 

Running queries on ASW RedShift is also easier, as opposed to Google’s BigQuery, thanks to Amazon’s Spectrum concept that borrows heavily from Oracle external tables. Typically. Users can retrieve and query structure, as well as semi-structured data sets from AWS S3, without necessarily loading the data into RedShift. Moreover, AWS RedShift supports standard SQL queries for the management and execution of machine learning models.

And BigQuery vs RedShift prices, how do they compare? Google big query, pricing is complicated, especially when it comes to query operations, while RedShift’s is straightforward, predictable, and enhances concurrent data usage and analytics. Nonetheless, this shortcoming is probably addressed by the higher level of data warehouse setting and performance control that RedShift offers to users. You can leverage free first-month subscriptions to benchmark the two solutions and determine which one is suitable for your business needs and use cases. You can also contact us for designing a cloud data warehouse solutions. 

FAQ on Cloud Data Warehouse Solutions