Name of schema (container of objects, tables) PG_TABLE_DEF Data Dictionary (for our purposes) Label It gives you all of the schemas, tables and columns and helps you to see the relationships between them. PG_TABLE_DEF is kind of like a directory for all of the data in your database. PG stands for Postgres, which Amazon Redshift was developed from. PG_TABLE_DEF is a table (actually a view) that contains metadata about the tables in a database. I’ve ingested data from Facebook, Shopify and a couple of other data sources into my Panoply account and I’m curious about analyzing my Shopify data.įor this exercise, we’ll look at orders by country (we will refer to “country” as the country the order was shipped to). We’ll start at a meta level and then work our way towards more granular (detailed) information.
In this post, I will walk you through the steps of exploring your data using Redshift. The point here is not to draw conclusions, but to get familiar with and understand the data you are working with. Data exploration, or data profiling, is the first step in sound data analysis. Lucky for you, there are some pretty stellar data exploration features to get you started with just a few simple lines of SQL code. But first, you need to get your bearings. You are on your way towards analytics awesomeness. UnionType ¶Īn enumeration.Congratulations! You’ve connected to your data warehouse (or database). SqlTypes ¶īases: object BINARY = ¶ BOOLEAN = 'BOOLEAN' ¶ CHAR = ¶ DATE = 'DATE' ¶ FLOAT = 'FLOAT' ¶ INTEGER = 'INTEGER' ¶ LONG_VARBINARY = ¶ LONG_VARCHAR = ¶ NUMERIC = 'NUMERIC' ¶ SIGNED = 'SIGNED' ¶ TIME = 'TIME' ¶ TIMESTAMP = 'TIMESTAMP' ¶ UNSIGNED = 'UNSIGNED' ¶ VARBINARY = ¶ VARCHAR = ¶ class pypika.enums. SqlTypeLength ( name, length ) ¶īases: object get_sql ( **kwargs ) ¶ class pypika.enums. SqlType ( name ) ¶īases: object get_sql ( **kwargs ) ¶ class pypika.enums. asc = 'ASC' ¶ desc = 'DESC' ¶ class pypika.enums. bin_regex = ' REGEX BINARY ' ¶ ilike = ' ILIKE ' ¶ like = ' LIKE ' ¶ not_ilike = ' NOT ILIKE ' ¶ not_like = ' NOT LIKE ' ¶ regex = ' REGEX ' ¶ class pypika.enums. cross = 'CROSS' ¶ full_outer = 'FULL OUTER' ¶ inner = '' ¶ left = 'LEFT' ¶ left_outer = 'LEFT OUTER' ¶ outer = 'FULL OUTER' ¶ right = 'RIGHT' ¶ right_outer = 'RIGHT OUTER' ¶ class pypika.enums. CLICKHOUSE = 'clickhouse' ¶ MSSQL = 'mssql' ¶ MYSQL = 'mysql' ¶ ORACLE = 'oracle' ¶ POSTGRESQL = 'postgressql' ¶ REDSHIFT = 'redshift' ¶ SNOWFLAKE = 'snowflake' ¶ SQLLITE = 'sqllite' ¶ VERTICA = 'vertica' ¶ class pypika.enums. day = 'DAY' ¶ hour = 'HOUR' ¶ microsecond = 'MICROSECOND' ¶ minute = 'MINUTE' ¶ month = 'MONTH' ¶ quarter = 'QUARTER' ¶ second = 'SECOND' ¶ week = 'WEEK' ¶ year = 'YEAR' ¶ class pypika.enums. and_ = 'AND' ¶ false = 'FALSE' ¶ or_ = 'OR' ¶ true = 'TRUE' ¶ xor_ = 'XOR' ¶ class pypika.enums. Pypika.enums module ¶ class pypika.enums.
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