%PDF-1.3 Thanks for contributing an answer to Stack Overflow! For those not familiar with AWS, Amazon API Gateway is a service for creating, publishing, maintaining, monitoring and securing REST and WebSocket APIs at any scale. Another key point when you design a data platform is which tool(s) you use to transform the data and how you orchestrate multiple data pipelines. I spent the last four years of my career mainly working on GCP (Google Cloud Platform), leading the development of The Telegraph data platform. This technology is scalable, durable event ingestion and delivery system used for stream analytics pipelines. ?�lf�����D}���a!6�Q=x���B(�@��tT�Q1� ��SpB�Ϟ��i�EL������oq�k�~���&������z�u Snowflake has a query service that works with the virtual warehouses that optimizes queries as well as security. Active 4 days ago. org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:657) at Fig-2 Photobox events collection process as it would look like using GCP. Once the fargate worker that runs DBT has executed the task, then it is released freeing up unneeded computational resources. It is obviously impossible to have everything and what is really better depends on the use cases that you have and on which tools your engineering team feels more confident using. If you’re running a business using on-premise technology which doesn’t easily integrate with cloud-based services, Snowflake is the wrong option. Basically you pay only when you have machines up and running, executing your queries and the total cost mostly depends on your usage pattern and the fact that your virtual warehouses are suspended when not in use. << /Length 5 0 R /Filter /FlateDecode >> After my first four months in Photobox I can say that I deeply appreciate some tools that AWS offers and I totally fell in love with Snowflake. Is this a reasonable fingering for an arpeggio in the right hand that starts on a note that is held in the left hand? When is a closeable question also a “very low quality” question? This is obviously only one solution to the problem. a:634) at. py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at During peak time at Photobox the number of external events per second that we collect might exceed that quota in the future but, luckily, this is a soft limit that eventually can be increased if needed. For all the reasons above we decided to opt for Snowflake, having a better decoupling between storage and compute. Redshift itself doesn’t support schema-on-read. The main difference that I can see between the two solutions is the number of components used to achieve the same result — one on AWS vs two on GCP. Asking for help, clarification, or responding to other answers. Snowflake doesn’t sit under the large AWS umbrella but it seemed to us the best option as a first building block for our platform. Please keep in mind that this is from the point of view of a data engineer that has just recently started playing with AWS and Snowflake. This ensures that the only application constantly running in our ECS cluster is Airflow, while all the data pipelines run on ephemeral workers that are alive just long enough to perform the task. With Snowflake, raw data can be stored in S3 and accessed through external tables. Internal events are acquired in a similar manner, but using SNS topics that our data platform subscribes to.
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