Now that we have some data, let’s start flattening and separating it out into some logical views to enable some relational queries, and enriching the content so we can generate some insights. Without it, I would get lots of null / empty columns. Querying the dataįirst things first – due to the mixed case employed through out the JSON data, I had to run set enable_case_sensitive_identifier to true in order to get consistent results through out this excercise. So at this point I’m happy that the data has loaded satisfactorily and can be queried as JSON. Provided there are no errors we can inspect the data:Īfter loading, here is the info stored in pg_table_def and svv_table_info: Below is the DDL for creating the table and the copy command: create table cust_ord_hist (load_date timestamp default current_timestamp, order_data super) Ĭredentials 'aws_access_key_id= aws_secret_access_key=' I have pushed the JSON files to an S3 bucket, so will use the copy command to load the data to a table in the database. We’ll start off by loading the data into a table with a single super type column, and use the ‘noshred’ option. We’ll generate a medium sized dataset first in JSON text format and see how this works with the super type (we’ll address avro later). We also have a map type to store the customer’s current contact information. Within each order record we can have numerous order lines, so 2 levels of nesting. Effectively for every customer record we are storing their order history as a sub-record/nested table.
The above is an avro schema representation of the recordset. The super type offers an advantage over character types as it currently supports upto 1MB of data per field. Redshift has long provided support for querying and manipulating JSON formatted data, and previously you might have used a varchar type to store this, or accessed and unnested formatted files via Spectrum and external tables so this is functionality is a welcome addition. By following the step-by-step guide provided in this post, you can leverage the power of the generate_series function in your own projects and gain valuable insights from your data.The super type became generally available in Redshift recently, making semi-structured data more manageable in-database.
Redshift create table as select series#
This technique is particularly useful for data scientists who need to analyze time series data or fill in gaps in their datasets.
Redshift create table as select how to#
In this blog post, we have explored how to join a table with a generate series in Amazon RedShift. This query generates a series of dates for the entire month of January 2023, joins it with the sales_data table, and calculates the total quantity of product 42 sold for each date. product_id IS NULL ) SELECT date, SUM ( quantity ) AS total_quantity FROM joined_data GROUP BY date ORDER BY date quantity, 0 ) AS quantity FROM date_series ds LEFT JOIN sales_data sd ON ds. WITH date_series AS ( SELECT generate_series ( '', '', '1 day' :: interval ) AS date ), joined_data AS ( SELECT ds. The syntax for the generate_series function in RedShift is as follows: This can be particularly useful when working with time series data, as it allows you to create a continuous range of dates or timestamps, which can then be used to fill in gaps in your dataset. Generate series is a powerful function available in many SQL databases, including RedShift, that allows you to create a series of values based on a specified start and end value, as well as an optional step value. In this post, we will focus on one such feature: joining a table with a generate series. One of the key features of RedShift is its support for SQL, which allows you to perform complex data manipulation tasks using familiar syntax.
It is optimized for high-performance analysis and reporting of large datasets, making it an excellent choice for data scientists who need to work with massive amounts of data. Table of ContentsĪmazon RedShift is a fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence tools. We will walk through a step-by-step guide on how to achieve this, and by the end of this post, you will have a better understanding of how to use this powerful feature in your own projects. In this blog post, we will explore how to join a table with a generate series in Amazon RedShift. | Miscellaneous RedShift: Join a Table with Generate Series for Data Scientists
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