Have you had any experience with any of the above vendors (Firebolt or Starburst) and is it worth checking them up or based on your experience the out of the box functions that vendors like Snowflake - or Bigquery - offer should be more than enough? Have you run in any limitations with them? I see Snowflake has a 16mb limit on a variant column. More precisely, partially flattening, and the use of lambda expressions for more advanced array manipulation, rather than only flattening and aggregating. SQL Queries: Google BigQuery is usually expressed in a standard SQL language. It is also used to import data from Google storage in different formats such as CSV (Comma Separated Values), Parquet, Avro, or JSON. This shows an example of storing different types of data in a VARIANT column by calling PARSEJSON to parse strings. It is good to have a clear understanding of how to parse nested JSON. Bigquery offers the ability to query JSON (and strings with JSON) using path statements such as this: JSONEXTRACT (jsonfield, '.path.to.my. By calling TOJSON on a STRUCT This illustrates both approaches: WITH inputtable. From a feature set and product maturity, Snowflake makes the most sense for us.īut then I saw there are other more niche vendors, like Firebolt - or Starburst (Presto) - that support a wider array of functions to parse json data. Data Management: Google BigQuery is used to create and delete objects such as tables, views, and user-defined functions. To read from a BigQuery table using the Beam SDK for Python, apply a ReadFromBigQuery transform. BigQuery supports loading nested and repeated data from source formats. SAFE.PARSEJSON tells BigQuery to use null for the object if there is a syntax error in the JSON string. ![]() ![]() Currently we have lots of json files in S3, that we would like to load and flatten. ![]() We are still in the early phase of choosing our cloud data warehouse vendor.
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