A CTE defines a temporary result set, stored in memory, that persists only for the Session-level derived tables, often referred to as temp tables, are MPP cloud data warehouse, such as Amazon Redshift, Google BigQuery, As I looked at ETL tools like Airflow, NiFi, et al, I … Creating a view on Amazon Redshift is a straightforward process. Paste SQL into Redshift. Now, onto the tutorial. Other methods for loading data to Redshift. As the name suggests, DESCRIBE is used to describe command to describe the structure of a table. The information_schema in Redshift is a superset of pg_table. column_name, you can optionally rename the columns to be created. Redshift is an award-winning, production ready GPU renderer for fast 3D rendering and is the world's first fully GPU-accelerated biased renderer. Copy JSON, CSV, or other data from S3 to Redshift. Views, which are kind of virtual tables, allow users to do the following − Structure data in a way that users or classes of users find natural or intuitive. If you prefix the name with a #, the view is temporary and exists only for the current session. Write data to Redshift from Amazon Glue. A view can be created from one or many tables, which depends on the written PostgreSQL query to create a view. Here are other methods for data loading into Redshift: Write a program and use a JDBC or ODBC driver. The temporary tables exist only for the duration of the session, and once the session ends, they automatically discard the data. Furthermore, pg_table_def seems to suffer from some kind of permission issue: while a schema/table will be in pg_tables, the corresponding information in pg_table_def will NOT be there. From the technical point of view, Redshift is a cluster database without such consistency features as a foreign key and the uniqueness of field values. Use EMR. What I’d like to highlight is that the disconnect between what Redshift is doing and what I want is linked directly to the join predicate on week_ending_date. I ended up deciding to break my view into two views and have the process pre-filter both of them before joining them together. When you’re loading and transforming data in your Redshift data warehouse, you’ll often want to stage data in a temporary table rather than insert it right into the final table. I often use this approach when I’m processing new data as to not disrupt tables that analysts, dashboards or applications are making use of. By default, the names derive from the original table. For example, consider below example of Hive describe command. 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