Whether you build software or manage cloud spending, choosing a data warehouse platform with a strong ROI typically comes down to two choices: Snowflake or BigQuery.
It's unbelievable how far these two tools have come in recent years. Holding nearly a third of the market share, they have reduced complex and distributed tasks to simple and profound actions.
If you're looking to start using one of these tools but aren't sure which one is right for your business, continue reading.
We'll go in depth to look at the ways these platforms are built, how they approach pricing and see which of their features can make your life easier.
BigQuery runs on a serverless architecture with scalable storage and compute.
Part of the GCP ecosystem, BigQuery automatically manages the underlying infrastructure, including scaling, replication, and backup.
Reference: https://cloud.google.com/blog/products/data-analytics/new-blog-series-bigquery-explained-overview
Snowflake's architecture separates compute power, data storage, and client services, allowing them to operate independently. This design enables faster performance, and concurrent tasks, and also supports structured and semi-structured data in its storage architecture.
Reference: https://www.snowflake.com/product/architecture/
Snowflake and BigQuery have vastly different ideas on how to charge for their services.
If you're running queries sporadically, the pay-as-you-go model BigQuery imposes makes sense, as you're billed for the volume of data scanned by a query.
There's also a flat rate option you can negotiate if you're seeing more consistent usage patterns.
In Snowflake, you're charged per second of computation time.
This can be a competitive option if you're running small and frequent queries (streaming video, for example). Snowflake also offers considerable storage options at varying rates, making it a versatile choice for enterprises with diverse storage requirements.
Let's take a loo at how much BigQuery charges for its services:
BigQuery's pricing is also influenced by factors such as location, storage type (active or long-term), and the number of virtual CPU slots reserved. Users with larger workloads can opt for a flat-rate plan with a fixed number of slots, while those with more dynamic requirements can choose the on-demand plan, which provides up to 2,000 virtual CPUs.
BigQuery's pricing model provides flexible options for businesses with varying workloads and resource requirements. Make sure you research them to get the most from your cloud ROI.
Snowflake's pricing is consumption-based and revolves around three main components: storage, compute, and cloud services.
Scalability is critical when choosing a cloud data warehousing solution, as it determines how well a platform can meet growing demands while maintaining performance and cost-effectiveness.
BigQuery scales horizontally, which allows it to handle large and complex queries with ease.
It's flat-rate model provides more predictable costs for large and consistent workloads, while the on-demand model is more flexible for dynamic or unpredictable workloads.
Snowflake also scales horizontally by adding more compute resources to the cluster as needed. But it doesn't automatically adjust your resources, which requires more frequent monitoring and management.
This means setting up price alerts that notify you whenever a budget threshold is met or exceeded, optimizing query performance, and having a strong grasp of what teams and features cost you the most in Snowflake.
Both BigQuery and Snowflake offer excellent scaling potential for their respective pricing models. BigQuery is simpler to scale, and Snowflake more flexible.
Both BigQuery and Snowflake offer advanced features and tools to help you stay on top of your cloud spending.
Aside from the native cost management tools, there are various third-party tools and services available to manage cost across both Snowflake and Google BigQuery.
Here's what to look for in these tools and how to choose the right one for your business.
When it comes to performance, both BigQuery and Snowflake are capable of processing large datasets quickly and efficiently.
Snowflake is known for its strong performance in handling complex queries involving large amounts of data. The reason is that Snowflake separates compute and storage, which allows it to scale compute resources independently of the amount of data stored.
On the other hand, BigQuery is optimized for processing large volumes of structured data quickly and efficiently. It uses a columnar storage format and a massively parallel processing (MPP) engine to accelerate query performance. BigQuery also supports real-time analysis with its streaming ingestion feature, which allows users to analyze data as it is generated.
BigQuery gives you plenty of ways to work with queries, including query plan visualization, query execution statistics, and the ability to set query priorities.
BigQuery also has a query cache that stores the results of frequently executed queries, reducing the amount of time it takes to retrieve data. Furthermore, BigQuery provides a functionality called "resharding", which automatically redistributes data among nodes to achieve load balancing and enhance performance.
Meanwhile, Snowflake offers a feature called "auto-clustering," which automatically organizes data based on usage patterns to improve query performance.
Auto-clustering works by creating new clusters based on frequently queried columns, which helps to reduce the amount of data that needs to be scanned. Snowflake also has a query profiling tool that can help users identify performance bottlenecks and optimize their queries.
BigQuery provides several built-in tools and features, such as the Query Editor and the Data Transfer Service, which simplify data migration and transformation processes. BigQuery's documentation is thorough and accessible, with plenty of resources available for users of different skill levels.
Snowflake has built a following thanks to its ease of use and an intuitive web-based UI that lets you make all the changes you need from your browser. Other features, such as SnowSQL and SnowPipe, are great additions for data management and integration.
BigQuery integrates with a wide range of third-party tools and services, including data ingestion tools like Apache Kafka, data warehousing platforms like Looker and Qlik, and data management tools like Apache Beam and Apache Spark.
Reference: https://cloud.google.com/bigquery#lightbox7
If you want to breathe some life into your data, Snowflake works with Power BI and Looker, as well as Tableau, Informatica, and Talend.
Snowflake also has native integrations with cloud services like AWS, Azure, and Google Cloud so you can move data and process it in various cloud environments without much effort.
In brief, both solutions provide robust integrations with well-known tools and platforms and have exhaustive documentation in place.
Snowflake and BigQuery are among the most popular data warehousing solutions.
We've looked at how they differ in cost, architecture, scaling potential, performance, and ease of use.
The right fit for your business will be the one that ticks the most boxes relevant to your use case. Depending on how you run your apps and services, both Snowflake and BigQuery are powerful contenders for data warehousing and processing.
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