Snowflake: Winner of Newcomer of the Year

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The deep and shallow copies can be explained by the following query and the result generated by Snowflake: show tables like 'customers%'; Let's now look at how the parquet file can be loaded into an external table. We shall be creating an external table called ext_tbl_userdata1. The creation of the table would require a location from which data can be read into the table. It would also require a file format. In this case, the file type is parquet: create or replace external table ext_tbl_userdata1 Let's start with the creation of views in Snowflake. We shall look into the creation of simple views on tables and then talk about materialized views: In step 3, when the Parquet file is loaded into the external table, the table rows have all the field names captured, as shown in step 4. But in step 5, when the CSV-formatted file is loaded into the external table, there are dummy column names created by Snowflake, as can be seen in step 6. This is because a Parquet file has metadata stored inside the file, while a CSV file does not have that metadata embedded in it. This is a major difference and would usually require additional steps as shown in step 7 to generate meaningful column names, plus the casting of data types. There's more…

Let's first create a stage. A stage is a logical concept or an abstraction of a filesystem location that is external or internal to Snowflake. In this case, an external stage has been used. The location can be managed in one of the object stores supported by the underlying cloud storage. In the case of AWS, S3 is used for this purpose. This recipe uses S3. The following statement creates a stage named sfuser_ext_stage. The stage should be accessible to Snowflake: CREATE OR REPLACE STAGE sfuser_ext_stageSO GROSS. There’s also two – TWO! – scenes describing Debbie raw-shaving her pubes. Why did I need to read this once, let alone twice?! She has the internet, she has a MACBOOK (despite her supposed poverty). GOOGLE. IT.

What we now have created is a deep copy of the CUSTOMERS table and a new table, which has received all data from CUSTOMERS and is called CUSTOMERS_DEEP_COPY. The deep copy means that the table structure and data has been copied, as opposed to a shallow copy, which would copy the table structure only. This copied table is now an independent copy that can have a life cycle of its own with no changes or side effects originating from any operations performed on the CUSTOMERS table. A SELECT QUERY statement on this new table would generate the same results as shown in the previous table. Since a standard view executes its definition at runtime, it can take some time to execute. If there is a complex query that is commonly used, it can be created as a materialized view. A materialized view looks similar to a standard view, but it doesn't run the query in its definition at runtime. Rather, when a materialized view is created, it runs the query right away and stores the results. The advantage is that when the materialized view is queried, it does not need to execute but can retrieve the stored results immediately, providing a performance boost. A materialized view will however incur additional maintenance and storage costs since every time the underlying table is changed, the view recalculates the results and updates the storage. There's more… Looking at the metadata limitations for external tables, it can be argued that Avro files can be the best format for staging data as Avro files can specify field names and data types as well. We shall look into an example in a later chapter where we discuss ETL processing. For schemas that will store temporary tables, such as tables used for ETL processing, a schema can be created as a transient schema, which means that there is no fail-safe storage associated with the tables created in the schema, and therefore it would cost less. Similarly, such schemas can also be set to have time travel set to zero to reduce costs further. By default, the time travel for transient schemas is 1 day.I can’t imagine someone going through life without grasping the concept of the iconic six-armed snow crystal,’ I say. Let's query the newly created external table. This would show each row of the result set as a JSON document. Within each row, you should be able to see different columns with their respective values as key-value pairs: select * from ext_tbl_userdata1; The following steps start with the creation of a stage, which is used to temporarily store data before it can be copied into Snowflake: Until now, we have seen the creation of tables with permanent life – the table will exist and store data until explicitly dropped. This is the default behavior from a life cycle perspective. There are tables that handle transitory data and so they might be required only temporarily. For such scenarios, there are temporary and transient tables. Let's create these tables by running the following SQL statements: CREATE TEMPORARY TABLE customers_temp AS SELECT * FROM customers WHERE TRY_TO_NUMBER(postal_code) IS NOT NULL; Debbie is raised on her family's rural dairy farm. She lives with her mother, Maeve, a skittish woman who keeps her past and spends most of her days alone writing and dreaming. Maeve believes her dreams are prophecies. Rounding out their small house is Maeve's brother Billy, who lives in a caravan behind her house and fiercely love and protect Debbie.