Redshift is capable of handling thousands of concurrent connection with many concurrent queries, but your warehouse needs to be configured to handle that load beforehand.īigQuery separates storage and compute resources, which is great for maintaining high performance at lower costs. Redshift scales horizontally and has the ability to scale significantly, however, resizing or changing your clusters can take some time. Redshift performs well and can deliver fast queries on huge datasets, however, it does not scale as quickly as its competitors. Automatic scaling makes operating your warehouse without a data administrator much easier. Additionally, compute clusters can automatically spin up and down depending on usage, making Snowflake well positioned to handle high variations in demand.
Snowflake has near unlimited storage capacity and will scale up automatically. By separating storage and compute layers, it is easy for many concurrent users to run queries and work with your data. Snowflake separates compute power and storage so that you can easily scale up storage and compute resources independently. When you are selecting a data warehouse, you will likely want one that can scale, both in terms of performance and storage. There are some slight differences between each of them, but nothing that a quick Google search can't solve. Redshift recently added support for semi-structured data.īigQuery supports both JSON and XML in structured and semi-structured formats.Īll three warehouses use an ANSI compliant SQL syntax. The high level of flexibility makes Snowflake great for users who do not have a predefined, rigid data structure. Snowflake supports the largest variety of data types, including JSON, XML, Avro, and Parquet and can store structured or semi-structured data. Data Types and Query LanguageĮach data warehouse supports different data types. BigQuery easily integrates with Google's machine learning tools, which is a huge advantage if you are using your data for artificial intelligence. Unlike Redshift, BigQuery is serverless, meaning that resources are allocated dynamically without upfront provisioning of hardware. Google BigQuery is the cloud data warehouse component of the Google Cloud Platform (GCP) environment. Redshift is loved by users who are working with huge amounts of data.
Of the warehouses we will discuss today, Redshift has the most customers. Users love Redshift as it integrates with ease into the Amazon Web Services (AWS) ecosystem. Amazon RedshiftĪmazon Redshift is a fully-managed cloud-data warehouse that handles structured data and semi-structured data. Compute pricing is based on a credit system, with each credit translating back to compute seconds. Snowflake bills for compute usage and storage separately. Performance is highly scalable to ensure that your queries stay fast. Snowflake offers fully managed data warehouses with near-unlimited storage. Snowflake is a cloud data warehouse that can handle both structured and semi-structured data. Of the three data warehouses that we are discussing today, Snowflake is growing the fastest. Although no one is immune to outages, all of these data warehouses have high uptime. Additionally, all the solutions that we will discuss today have historically been extremely reliable. Over the course of this blog post, we will talk about the 3 most popular cloud data warehouses and how they differ along multiple dimensions.Īll of the solutions that we will discuss today are cloud-based, which will allow you to scale your warehouses as your data needs grow.
What type of and how much data do you want to store?.When picking your data warehouse provider, there are a few things you might want to consider: Just as a quick refresher, a data warehouse is a data storage system that stores huge amounts of historical data from multiple sources, and acts as the source of truth for data analysis and reporting. Now that you know all about data warehouses, let's explore the different options that are out there so that you can pick the best warehouse for your use case. A few blog posts ago, we spoke about data warehouses and the potential benefits that they can have on your organization.
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