The on-demand pricing model is based on bytes scanned whilst purchasing fixed slots (reservations) will have a defined cost per 500 / 1000 slots. Support for external tables (via Spectrum) There’s a lot I haven’t had a chance to cover including:11th July 2020 – Updated with information about materialised views17th June 2020 – Updated additional information for in preview functions for Snowflake (Geospatial, external functions, external tables, Snowsight).– Designed as a fair feature comparison between the different products– An up to date guide (hopefully with regular updates as new features are released or changed)– I’ve attempted to make the information as accurate as possible, but some details may be condensed for simplicity. None of the three platforms provide an in-built data sourcing capability. Awesome.
This table could be populated as soon as a conversion takes place.As soon as an entry is recorded in the conversion table, automatically trigger some rules to populate a third table with the time of first touch from Facebook (if any) and also from Google (if any) campaigns.If the marketer is using a first touch attribution scheme, this third table can then provide insights into which campaigns (FB/Google) brought the user in first.To implement this technically in each of the three platforms, the following components would need to be usedComponents used: Third-party source connectors (e.g.
Snowflake manages all of this out of the box. The first benchmark we performed was to compare the runtime for five different queries. The company spends on both Facebook and Google ads but in many cases, both the platforms take credit for conversions. A batch data pipeline could then be triggered every night to process last day’s conversions from RDS and move the rows into Amazon Redshift which is a specialized product for querying large amounts of data.Unlike Amazon and Google Cloud, the Snowflake platform actually provides data warehousing capabilities that internally use either Amazon, Google, or Azure platforms. Snowflake makes it quite easy to share data between different accounts.
Redshift: The recently introduced RA3 node type allows you to more easily decouple compute from storage workloads but most customers are still on ds2 (dense storage) / dc2 (dense compute) node types. Benchmarks Speed. It mostly works out of the box. We should be skeptical of any benchmark claiming one data warehouse is orders-of-magnitude faster than another.The most important differences between warehouses are the qualitative differences caused by their design choices: some warehouses emphasize tunability, others ease-of-use. Marketers can easily control these options through web/app-based management consoles.
… Note: Snowflake supports a subset of the regions of each of the public clouds, not all regions.
This runs on Borg, a cybernetic life-form that Google has imprisoned inside conveniently located data centers in various regions.Redshift: Proprietary fork of ParAccel (which was partly forked from Postgres) running on AWS virtual machines. This is a welcome addition and follows a continuing trend of bringing query editors and visualisation into the same cohesive interface.You can’t have a great database without a great query language (this is mostly true).Materialised views are often useful when building tables that need to be built incrementally, particularly when building these data models it can save a lot of query time when running against a materialised view rather than the equivalent query. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. The core data processing needs here are not so much about fast query performance on structured data, but more about things like data preparation capabilities, data partitioning, integration with machine learning models, and ability to deploy selected models on new datasets quickly. It is also possible to purchase via capacity storage upfront if you’re after some more predictable long term pricing. Something like customized reports from Google Analytics, Google Ads, or Facebook. This means that unlike the other two platforms, the customer will have to prepare its data externally and then load into Snowflake. On many head-to-head tests, Redshift has proved to show better query times when configured and tweaked correctly. Don’t let the proximity to Postgres fool you, it’s more of a distant second cousin.Snowflake: Proprietary compute engine with intelligent predicate pushdown + smart caching running on commodity virtual machines (AWS, GCP or Azure) depending on your cloud choice.All 3 databases have implementations of hot / warm / cold storage.BigQuery: Proprietary, stored on the Colossus filesystem using ColumnIO as a storage format.
Separation of compute from storage for RA3 nodes, compute and storage co-localised for other node types.Snowflake: Proprietary columnar format, in-memory / SSD / object store running on compute / object storage in your cloud of choice.BigQuery: proprietary compression that is opaque to the user and handled by the ColumnIO columnar format. The fastest unified analytical warehouse at extreme scale with in-database Machine Learning. Amazon Redshift vs Google BigQuery vs ... What are some alternatives to Amazon Redshift, Google BigQuery, and Snowflake? Background.