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Analytics dashboards are a great tool for your SaaS product users. They need smart insights to make business decisions confidently. And even though at the surface, your customers will only see charts and graphs in your SaaS apps, a good data infrastructure is key to a user-friendly analytics experience.
In this article, we’ll compare two modern cloud data warehouses: Snowflake vs BigQuery. Learn about their key differences, and which one is better for your embedded analytics setup.
Before we dive into Snowflake and Google BigQuery, it’s crucial to understand the difference between databases and data warehouses. A database stores and organizes data in a structured format, with a predefined schema. They focus on processing transactions, which makes them excellent as an operational system.
On the other hand, data warehouses are a specialized type of database designed to handle large volumes of data. Unlike databases, data warehouses don’t follow a fixed schema, but use a schema-on-read approach. They store data in its raw format, so you can explore and analyze it in different ways without having to transform or rearrange it upfront.
Because of this architecture, a data warehouse is tailored for analytical processing, complex querying and scalability. For that reason, they are a popular data infrastructure for business intelligence and data analysis. Often, they will be used on top of your operational databases.
Now that we know the difference, let’s take a closer look at Snowflake and BigQuery.
Snowflake is a cloud-based data platform. It offers a powerful and user-friendly data warehouse that stores, manages, and analyzes large volumes of data in the cloud. You can host it on cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP) or Microsoft Azure.
Snowflake is a great data warehouse for business intelligence thanks to its data storage, data processing, query optimization, and support for SQL-based querying. However, Snowflake can do more than only data warehousing tasks. With capabilities for data integration, real-time analytics and data sharing, it is one of the more versatile data platforms on the market.
Google BigQuery is a fully-managed and serverless cloud data warehouse. As part of the Google suite, BigQuery exclusively runs on Google Cloud Platform. Similarly to Snowflake, BigQuery stores and analyzes massive volumes of data quickly and cost-effectively using SQL-like queries.
BigQuery is ideal for high-performance analytics and data exploration without managing complex infrastructure. With both batch and real-time data processing, it’s a powerful tool for big data analytics, business intelligence, and data-driven decision-making.
Although they seem alike at first, BigQuery and Snowflake have plenty of differences to prefer one over the other. Let’s have a closer look.
Both Snowflake and BigQuery store data in a columnar format. This makes them both excellent choices for analytical queries. Opposed to relational databases that store data in rows, a columnar database only has to read the columns it needs for a specific query, and ignores the rest. As a result, queries are much faster.
Both data warehouse solutions can handle vast amounts of data quickly, thanks to a process called data partitioning. This means they compress and divide a large database into smaller, more manageable parts. But they each do so in a slightly different way.
Snowflake automatically divides data into micro-partitions when the data is loaded. It uses metadata about these micro-partitions to optimize query performance.
BigQuery, on the other hand, automatically divides data based on a specific column. Usually, it will use the timestamp to create specific partitions, in an append-only manner.
If you’re dealing with time-series data or often query data in a specific timeframe, BigQuery is a great fit. If you need to do more complex analytical queries over a wide range of data, Snowflake is the better option. It can skip over any micro-partitions that are irrelevant to your query.
Snowflake uses a multi-cluster, shared data architecture. It uses multiple virtual warehouses - or computing clusters - to handle analytical tasks and queries. Just think of it as a team of colleagues. Let’s say you’re working on a project together. If every team member takes up a few tasks simultaneously, you’ll be done in no time. If one colleague has to do all of the work consecutively, it might take him or her weeks.
Snowflake’s architecture has three layers.
Snowflake completely separates storage and computing power, which makes it extremely flexible and cost-effective. Each virtual warehouse works independently, so they can’t affect each other’s performance. You just scale computing resources up and down depending on your needs. The same goes for storage.
Like Snowflake, Google BigQuery also separates computing and storage. However, its architecture is very different. BigQuery is a fully-managed, serverless data warehouse. With serverless data warehousing, users don’t have to provision or manage resources. Unlike Snowflake, which requires manually scaling resources, BigQuery automatically allocates resources based on query demands.
BigQuery’s architecture is as follows.
If ease of use is important to you, BigQuery is a great choice. You don’t need to manage complex infrastructure, and serverless ETL automatically scales to handle your queries.
If you have predictable workloads, and want more flexibility to scale storage and computing separately, Snowflake is a better fit for you. With this level of control, you can easily save a lot of money on warehousing.
No one likes it when dashboards take ages to load. To keep your product users engaged, you need fast analytics. And that means good query performance. Luckily, Snowflake and Google BigQuery both lead the pack when it comes to performance. Especially for large datasets, they can take a heavier load than operational databases like MongoDB or PostgreSQL. But there are a few key differences in the way they handle performance.
For starters, their different architecture impacts query performance. It's a great idea to look at existing benchmarks that set off data warehouses against each other. However, don't forget that performance really depends on the type of datasets you want to analyze. Besides general benchmarks, it’s always a good idea to run your own benchmarking on your specific use case.
Snowflake is great if you want to run multiple workloads at the same time, because of its concurrency control. With its virtual warehouses, there’s excellent isolation between workloads. It also uses something called clustering keys, which reduces the need for data sorting during certain queries. And finally, its automatic query optimization is great if you want optimal performance without having to fine-tune queries manually.
On the other hand, BigQuery’s caching mechanism is superb and reduces query execution time drastically. Its serverless model scales resources automatically based on the query requirements, which makes it great if you need to handle varying workloads. In terms of query optimization, BigQuery uses techniques to rewrite and optimize queries for better performance. It will select the most efficient approach to process the data.
If you want to use materialized views, you’ll be happy to hear both tools support them. You’ll speed up your workflows considerably by storing precomputed results of complex queries.
If you run most of your business on Google Cloud Platform, BigQuery is the logical choice. It has a tight integration with the Google Cloud ecosystem, making data movement a seamless experience. The advantage of Snowflake, on the other hand, is that it runs on multiple clouds. So if you need a little more flexibility, go for Snowflake.
Both tools have an extensive API that lets you integrate with other tools in your tech stack. For example, you can use their APIs to do data ingestion from data sources like web services, data lakes or cloud storage. Or you can hook it up to your favorite BI tool or embedded analytics software.
Both Snowflake and BigQuery support many different data types, whether it’s structured or semi-structured data. They natively support CSV, JSON, Avro, ORC and Parquet. On top of that, Snowflake also natively supports XML. In BigQuery, you’ll need a workaround.
Both tools offer support for SQL queries. However, BigQuery uses a dialect of SQL, called GoogleSQL (previously Standard SQL). If you are moving to or from BigQuery, you may have to modify your SQL queries slightly.
Snowflake and BigQuery are both cost-effective solutions for data warehousing. You pay for what you use, so you’ll get the most bang for your buck. However, their pricing models have a few key differences.
BigQuery uses a pay-as-you-go model. They charge based on the data volumes processed, and the amount of data stored in tables. Data storage is a separate cost. The good thing is that you don’t have to commit to a long-term contract. With their on-demand pricing per terabyte, you only pay for resources as you use them.
If your workloads are predictable, or if you’ve got a limited budget, their flat-rate pricing is a great alternative. For a monthly flat fee, you’ll get a fixed amount of query and storage resources.
Snowflake uses a per-second billing. Compute costs are charged on a per-second basis, so you pay for the amount of time it takes to process a query. If your queries are short and efficient, you could save a lot of money. Snowflake also separates compute and storage costs, which gives you a lot of flexibility. If you’re running heavy queries, but aren’t scaling your storage, their model is a great cost-cutter. Data storage costs depend on the amount of data and the level of data replication.
If you need to handle concurrent workloads, Snowflake also offers pricing options for concurrency scaling.
Both pricing models have their pros and cons. But which one is better? This is a question only you can answer for yourself. It really depends on your individual workloads and requirements. Depending on your data volume, query frequency and your performance wishes, one of them may be more cost-effective than the other.
Snowflake and BigQuery are both solid data warehouse providers for SaaS companies. If you want to offer engaging reporting analytics in your SaaS platform, you will enjoy their query optimization and performance. Which one is better depends on your use case.
Google BigQuery excels in handling large-scale analytics and data exploration tasks. With its serverless architecture, you can easily scale and process massive datasets with low management overhead. If you’re already on Google Cloud, and you want quick ad-hoc queries at a low cost, Google BigQuery is great for your organization.
If you need to run multiple complex workloads at the same time, Snowflake’s multi-cluster architecture is the best fit for you. It handles diverse analytical needs and data collaboration use cases extremely well.
If you need help figuring out the right data infrastructure for your use case, our team of analytics experts is here to help. Book a free consultation today!
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