Every modern data stack is built around a cloud data warehouse. It is nearly impossible to gain insights from your data without a cloud-based data warehouse. A data warehouse is, at its core, an analytics platform where information from various data sources is stored for analysis.
This information is used to make high-level business decisions and to answer pressing business questions. Today, every company is either using a data warehouse or is in the process of implementing one. Despite the fact that there are several key players in the data warehousing space, this post will only cover BigQuery and Snowflake.
What is Snowflake?
Snowflake is a warehouse solution based on Software-as-a-Service (SaaS) that can be run on any of the popular cloud providers (AWS, Azure, GCP). It was designed specifically for the cloud and includes a few key components that set it apart from other cloud data warehouses. Snowflake was publicly launched in 2014 and has since grown to become a major player in the data warehousing industry, with a market capitalization of $90.35 billion as of October 2021.
Snowflake was created in the cloud and by the cloud. That is, it has no baggage and almost no management or operational overhead. Snowflake handles all backend infrastructure as a native SaaS service, allowing you to focus on what matters most: gaining insights from your data. Snowflake has a ton of scalability, allowing for nearly infinite concurrent queries.
What is BigQuery?
Google BigQuery was one of the first data warehouse solutions available on the market when it was introduced in 2010 as part of Google Cloud Platform. However, at the time, it was primarily regarded as a complex query engine. Google BigQuery has advanced significantly since then, and it is no longer the same solution.
BigQuery, like Snowflake, does not require any infrastructure to be set up or maintained. Instead, you can concentrate on uncovering meaningful insights through standard SQL. Google BigQuery is a completely Google product that does not run on any other cloud provider.
Snowflake is a serverless solution that separates storage and compute and is based on ANSI SQL. Its architecture combines traditional shared-disk and shared-nothing architectures to give you the best of both worlds. It uses a central repository for persisted data to make your data available to all compute nodes on the platform.
Snowflake processes all of your queries using MPP (massively parallel processing). This means that each compute cluster (virtual machine or server) locally stores a portion of your entire data set. Snowflake organizes your data into separate micro partitions for storage, which are then internally optimized and compressed for columnar storage.
In fact, all data loaded into Snowflake is reorganized, optimized, and compressed into a columnar format before being stored in the cloud. Snowflake handles all aspects of data storage automatically, including file size, structure, compression, metadata, statistics, and other data objects that are only accessible via SQL queries and are not directly visible to you.
Snowflake processes data using "virtual warehouses," or clusters of compute resources. Each warehouse is an MPP made up of several nodes. Snowflake's cloud services layer coordinates all Snowflake activities, including user requests, authentication, infrastructure management, metadata management, query parsing and optimization, access control, and so on.
Snowflake and Google BigQuery are very similar in that Google BigQuery is serverless and separates storage from compute. It is based on ANSI SQL as well. But it's architecture is very different. Google infrastructure technologies like Dremel, Colossus, Jupiter, and Borg are used by BigQuery to power a variety of multi-tenant services. Google BigQuery uses Dremel, a sizable multi-tenant compute cluster, to process SQL queries.
By transforming your SQL queries into execution trees, Dremel handles the tedious work. Slots are what BigQuery refers to as tree leaves. They carry out the necessary computation after reading data from storage. The "mixers" of the tree, or its branches, manage all aggregations. One member of your team has access to thousands of slots, allowing them to execute queries as needed.
BigQuery compresses data into a columnar format for Colossus, Google's global storage system, just like Snowflake does. There is no single point of failure thanks to Colossus' management of distributed management, data replication, and recovery. BigQuery transfers data quickly from one location to another using Google's Jupiter network. Borg, Google's forerunner to Kubernetes, handles all hardware resource allocation and orchestration for BigQuery.
Clusters can stop and restart when they are inactive or busy thanks to Snowflake's auto-scaling and auto-suspend features. Snowflake users are unable to resize nodes, but they can easily resize clusters with a single click. Additionally, Snowflake enables you to automatically scale up to 10 warehouses with a 20 DML limit per queue per table.
BigQuery will take care of everything behind the scenes, it will automatically provision additional compute resources as needed. The default cap for BigQuery is 100 concurrent users. You can automatically scale up and down on either platform depending on demand. Additionally, Snowflake lets you isolate workloads from various businesses in various warehouses, enabling various teams to work independently without encountering concurrency problems.
Compliance & Security
Snowflake automatically encrypts data in transit and at rest. It does not, however, provide granular permissions for columns, but it does for schemas, tables, views, procedures, and other objects. BigQuery, on the other hand, offers column-level security as well as permissions on datasets, individual tables, views, and table access controls.
Because BigQuery is a Google offering, you can also use other Google Cloud services that have built-in security and authentication to BigQuery, making integrations much easier. Snowflake does not include any built-in virtual private networking capabilities. However, if Snowflake is hosted on AWS, AWS PrivateLink can help.
BigQuery, on the other hand, allows you to use Google's virtual private cloud. BigQuery and Snowflake are both HIPAA, ISO 27001, PCI DSS, SOC 1 TYPE II, and SOC 2 TYPE II compliant.
Both platforms support structured and semi-structured data (Avro, Parquet, Orc, CSV, JSON), and Snowflake announced support for unstructured data on September 20th, 2021, and has made it available in public preview.
You can manage user roles, permissions, and data security with BigQuery and Snowflake. All performance tuning occurs automatically, and as your data volume grows and queries become more complex, each platform scales in the background to meet your requirements.
Furthermore, because each solution is provided as a SaaS service, all underlying maintenance and infrastructure is handled for you. BigQuery handles everything automatically, and Snowflake allows administrators to scale compute and storage layers independently. This means you can isolate workloads in Snowflake without having to deal with the sizing and permissions issues that come with virtual warehouses.
When it comes to data security, both BigQuery and Snowflake perform admirably. Time Travel and Fail-safe are two features in Snowflake that can help with this. Snowflake Time Travel saves a state of your data before it is updated. Time Travel has a standard retention period of one day (Enterprise customers can specify a period of up to 90 days). Databases, schemas, and tables can all benefit from Time Travel.
Snowflake can recover historical data using Fail-safe. This is a fixed period that begins immediately after the time travel retention period ends. Although you must request that Snowflake initiate the recovery, this feature allows Snowflake to recover any data that may have been damaged or lost due to extreme operational failures.
BigQuery administrators can easily undo changes without having to deal with the hassle of a recovery. BigQuery keeps a seven-day history of all changes to its tables. BigQuery, on the other hand, provides a feature called table snapshots that allows you to keep table data for more than seven days (snapshots are used to preserve the contents of a table at a particular point in time).
Because Snowflake's pricing model is based on each individual warehouse, the cost is heavily influenced by your overall usage. Snowflake offers a variety of warehouse sizes (X-Small, Small, Medium, Large, X-Large, and so on), with prices and server/cluster counts varying greatly. Snowflake Standard Edition costs about 0.0003 credits per second or one credit per hour for an X-Small Snowflake warehouse.
One credit per hour can also be calculated. It is important to note that credit costs vary depending on the business tier within Snowflake. The price per credit for Snowflake Standard Edition is $2. Furthermore, with each increase in warehouse-size, credit consumption within Snowflake doubles (for example, Snowflake's 6X Large warehouses can cost up to 512 credits per hour).
Furthermore, credit costs on Snowflake vary greatly depending on the tier. Snowflake does offer several plans that allow you to purchase credits in advance to cover usage. This is advantageous because Snowflake's pre-purchase capacity plans have lower upfront costs than the on-demand option.
BigQuery, on the other hand, charges based on the number of bytes scanned or read. BigQuery provides both on-demand and flat-rate pricing. On-demand pricing charges you $5 per TB for the number of bytes processed in a given query (the first TB of data processed per month is completely free of charge). You buy slots (virtual CPUs) or dedicated resources to run your queries with BigQuery's flat-rate pricing model. For 100 slots, the monthly cost is around $2,000 (this can be reduced to $1,700 with an annual commitment).
Storage costs are low for both Snowflake and BigQuery. Snowflake charges a monthly fee of $40 per TB for on-demand customers and $23 for upfront customers. BigQuery, on the other hand, charges $20 per TB of active storage and $10 per TB of inactive storage.
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As a native SaaS solution Snowflake was built to work with any of the major cloud providers (AWS, GCP, Azure). BigQuery is a native Google Cloud offering, which means it is only available if you are a Google Cloud Platform user.
Snowflake tends to outperform every data warehouse, including BigQuery, on query times, performance, and execution times right out of the box. Snowflake and BigQuery are probably more similar than dissimilar.
If you run a lot of queries with a lot of idle time, BigQuery will most likely be more efficient and have lower compute costs. Snowflake, on the other hand, is likely to be more cost-effective if you have more predictable and continuous usage.
Snowflake and BigQuery Differences
There are several significant differences between Snowflake and BigQuery. To begin with, scaling in Snowflake is not entirely automatic. It necessitates your participation. BigQuery, on the other hand, handles everything automatically.
Second, Snowflake can be run on any of the major cloud providers, whereas BigQuery can only be run on Google Cloud Platform. Snowflake is a full-service SaaS solution, whereas BigQuery is a PaaS solution. Furthermore, Snowflake has a unique feature called Secure Data Sharing, which allows you to share specific objects in a database with other Snowflake accounts. Because everything happens in Snowflake's unique services layer and metadata store, no data is actually copied or transferred between accounts when using Secure Data Sharing.
BigQuery does not support data sharing. BigQuery, on the other hand, allows you to create authorized views to share query execution results with specific users or groups without giving them access to the underlying tables. BigQuery also has a feature called BigQuery ML that allows you to create and run machine learning models that can significantly improve query performance.
When it comes to machine learning and real-time streaming workloads, BigQuery clearly outperforms Snowflake. Finally, when it comes to selecting a new cloud data platform, the use case you are attempting to solve should be at the forefront of every decision you make.
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A high-performance cloud data warehouse is required for successful businesses that rely on sound intelligence. Snowflake and BigQuery are both important stops on the road to better business intelligence. Whatever data warehouse you choose, ingesting all of your organization's data is critical to providing the background you need for better business intelligence.
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