We've reduced by 10 the cost of our nightly batches by using flex slots.įinally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.īigQuery is still evolving very quickly. 0 cost when the solution is not used, only pay for the query you're running.īut quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. Its on-demand pricing is particularly adapted to small workloads. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.īigQuery can therefore be set up with almost zero cost of human resources. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. These solutions seem to match our goals but they have very different approaches.īigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Our benchmark was conducted over BigQuery and Snowflake. The choice of the most suitable solution is therefore fundamental. "MySQL compatibility " is the top reason why over 11 developers like Amazon RDS for Aurora, while over 27 developers mention "Data Warehousing" as the leading cause for choosing Amazon Redshift.Īccording to the StackShare community, Amazon Redshift has a broader approval, being mentioned in 269 company stacks & 67 developers stacks compared to Amazon RDS for Aurora, which is listed in 121 company stacks and 31 developer stacks.Ĭloud Data-warehouse is the centerpiece of modern Data platform. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing. No Up-Front Costs- You pay only for the resources you provision.Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources. Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. ![]() On the other hand, Amazon Redshift provides the following key features: ![]() Some of the features offered by Amazon RDS for Aurora are: ![]() It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.Īmazon RDS for Aurora and Amazon Redshift are primarily classified as "SQL Database as a Service" and "Big Data as a Service" tools respectively. Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability Amazon Redshift: Fast, fully managed, petabyte-scale data warehouse service. Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon RDS for Aurora vs Amazon Redshift: What are the differences?Īmazon RDS for Aurora: MySQL and PostgreSQL compatible relational database with several times better performance.
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