2025年2月27日 · With over 25 built-in SodaCL checks and metrics to choose from, it can be hard to know where to begin. This tutorial offers suggestions for some basic checks you can write to begin surfacing missing, invalid, unexpected data in your datasets.

2025年2月27日 · When you add or edit a data source in Soda Cloud, use the discover datasets and/or profile columns configurations to automatically profile data in your data source. Examine the profile information to gain insight into the type of SodaCL checks you can prepare to test for data quality. Use profiled data to create no-code data quality checks.

2025年2月27日 · Soda Checks Language (SodaCL) is a YAML-based, domain-specific language for data reliability. Used in conjunction with Soda tools, you use SodaCL to write checks for data quality, then run a scan of the data in your data source to execute those checks. A Soda Check is a test that Soda performs when it scans a dataset in your data source.

Soda Core is a free, open-source, command-line tool and Python library that enables you to use the Soda Checks Language to turn user-defined input into aggregated SQL queries. When it runs a scan on a dataset, Soda Core executes the checks to find invalid, missing, or unexpected data.

2024年6月2日 · In this blog post, we’ll explore how to use Soda Core in Databricks, and practical implementation steps. Soda Core is a free, open-source Python library and command-line tool designed to ensure...

Soda Core is a free, open-source command-line tool. It utilizes user-defined input to prepare SQL queries that run checks on datasets in a data source to find invalid, missing, or unexpected data. When checks fail, they surface the data that you defined as "bad" in the check.

Osaka Soda HPLC columns are available in a wide product lineup covering all domains, ranging from low polarity to medium polarity and high polarity, enabling users to perform various desired separation processes. Select the optimum column for your analysis to match where it will be used and the purpose of the analysis.

2022年4月5日 · Generating and pushing profiling/EDA metrics to cloud will be done in soda-core via a profiling run. The original proposition looks like this: profiling advanced: tables: - SODATEST_% - include SODATEST_% - exclude SODATEST_% columns: - ...

2025年2月27日 · Use a schema check to validate the presence, absence or position of columns in a dataset, or to validate the type of data column contains. In the context of SodaCL check types, schema checks are unique. Schema checks always employ alert configurations – specifying warn and/or fail alert conditions – with validation keys.

Performs a full text search for a value. Note that for equality comparisons, the $where clause can be replaced with using the column name as the query parameter. See filtering for more details. These parameters can then be directly added to the API endpoint.