Dedicated SQL PoolsĬomputing power within Dedicated SQL pools (previously Azure SQL Data Warehouse) is determined by compute nodes known as data warehouse units (DWU). Whereas Snowflake uses virtual warehouses, Azure Synapse offers what’s known as Dedicated SQL pools, Serverless SQL pools, and Spark Pools. At the center of Azure Synapse is Synapse studio, which is the UI to monitor resources, perform tasks, write code, and manage user access.Īzure Synapse uses a distributed query system that leverages T-SQL (T-SQL is very similar to conventional SQL but it comes with a few added benefits and is primarily used with Microsoft services. Storage and compute within Azure Synapse is separate just like Snowflake and the platform is ANSI SQL compliant. On top of this, Snowflake is cloud-agnostic and runs on all three major clouds, AWS, GCP, and Azure Azure SynapseĪzure Synapse is built solely to run on Azure Cloud. This means that Snowflake supports near-unlimited concurrency for both queries and users. Warehouses do not share resources with other virtual warehouses. None of this is visible to an individual user and it can only be accessed through SQL query operations run within Snowflake.Įvery warehouse within Snowflake is on its own independent compute cluster. This optimized data is then stored in cloud storage and Snowflake handles every aspect of file size, structure, compression, metadata, statistics, etc. Once data is loaded into the platform, Snowflake automatically uses micro partitions to internally optimize and organize data into compressed columnar storage. the unit of scale), where a subset of the data is stored locally. This data is then processed using MPP (massively parallel processing) compute clusters known as data warehouses (i.e. To be specific, Snowflake uses a central data repository for all persisted data and makes it accessible to different compute nodes within the platform. It is based around a shared-disk and shared-nothing architecture. Snowflake is an ANSI SQL compliant and serverless solution that has completely separated storage and compute processing layers. Snowflake is a data platform that is not built around any specific database technology or big data software platform. However, whereas Snowflake is focused on business intelligence workloads, Synapse integrates with Apache Spark to handle streaming, artificial intelligence, and machine learning workloads in addition to conventional SQL and Business Intelligence workloads. In addition to analytics use cases, Synapse is designed to act as a central hub to connect additional Azure offerings. Similar to Snowflake, Synapse also provides a single platform where companies can collect and consolidate data and use SQL for analytics purposes. Believe it or not, Synapse is a relatively new offering from Microsoft, only being released officially at the end of 2020. What Is Azure Synapse?Īzure Synapse is a PaaS (Platform-as-a-Service) data platform that is offered by Microsoft. Today Snowflake is worth billions of dollars. Founded in 2012, Snowflake officially launched in 2014 and became the single largest software IPO in history in 2020. At its core, Snowflake collects and consolidates data so that users can self-serve and easily query data using SQL to create reports and dashboards and drive business value.Īs a native SaaS offering Snowflake handles all of the backend infrastructure and administration that usually comes with cloud offerings. Amazon Web Services, Microsoft Azure, Google Cloud Platform). Snowflake is a SaaS (Software-as-a-Service) data platform that is built to run on any of the major cloud providers (i.e. With that in mind, here are some of the core differences and pros/cons to Snowflake and Synapse. However, Synapse and Snowflake are different solutions and both should be analyzed from an unbiased lens. This is one of the core reasons that Snowflake and Azure Synapse Analytics have risen to such popularity. Now more than ever, companies need a way to collect and consolidate data into a single platform to derive insights quickly. With the world on pace to reach 175 Zettabytes of data by 2025, it’s no wonder why organizations are placing such a high emphasis on building out their technology stacks.
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