![]() ![]() Whether to merge the CSV data to existing table records based on a unique identifier or to append it.If, instead of creating a new table, you're uploading to an existing table, Airtable will show you a different set of options, including: Alternatively, apply a filter to only show data where a particular column is empty. If you want to inspect the missing data, you can sort columns from A - Z so the missing data is shown first. Note: Airtable will give you a warning if your data contains missing values (but you’ll have to do a bit of guesswork as it doesn’t provide any further detail). Once you hit save, it’ll take you to your newly built table. If you're in a rush though (or just feeling plain lazy, no judgment) you can always edit these later. While it does a decent job in recognizing the data types, it's best to manually verify them to ensure the best data quality. If you're creating a new table from your data, Airtable will show you a preview of your data, along with the data types it thinks your columns are. From the top menu, choose Add or import > CSV File and choose to add the data to a new or existing table, depending on where you want it to live. Once your download is complete, go to Airtable and click the base that you want to add this data to. To get around this, use the newer Snowsight instead, which doesn't have a maximum file size limit. However, if your query result exceeds 100 MB and you are using the classic UI, you won't be able to download it. Here you'll find a button to download your data as a CSV file. Once your query finishes running you'll see your data in the Results pane. Note: You’ll need to make sure the dataset you end up with falls within your Airtable plan limits. ```CODE language-sql``` SELECT C_CUSTOMER_ID, C_FIRST_NAME, C_LAST_NAME, C_EMAIL_ADDRESS FROM "SNOWFLAKE_SAMPLE_DATA"."TPCDS_SF100TCL"."CUSTOMER" LIMIT 2000 We’ll set up a query to select the data we want to export to Airtable using the sample dataset and query below. To get started, head on over to the Snowflake web interface so you can run your SQL queries. The downside is it involves manual actions and your data in Airtable might go out of sync with your data in Snowflake if you're not constantly rerunning this manual sync. You can be done in five minutes, making it a great solution for a one-off situation or prototyping before automating. On the pro side, this method is easy and very quick. The manual way: Export CSV data from Snowflake using SQLĪs you can probably guess by the title of this section, this method relies on using SQL in Snowflake to query and download your data locally and manually uploading it to Airtable. In this article, I’ll break down three ways you can load data from Snowflake to Airtable: The manual (read: less fun) way, a semi-automated way using each tool's respective APIs, and an even easier automated method using reverse ETL. ![]() By loading your warehouse data into Airtable, you open up a gateway to a world where everyone can more easily collaborate with data. That is if you can get the data you need from your warehouse into Airtable in the first place.Īs you’re probably well aware, Snowflake isn’t the best place to collaborate with your business teams, especially if those business teams aren’t as technical as your data or engineering teams. Low-code platforms like Airtable give everyone across your business the ability to build and customize workflows, collaborate, and power meaningful business outcomes, such as managing and fulfilling new orders. Automating your data syncs with reverse ETL.Setting up a semi-automated workflow using the Snowflake and Airtable APIs.In this article, Michel breaks down how to load data from Snowflake to Airtable using three methods: ![]()
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