It helps data teams eliminate pipeline debt through data testing, documentation, and profiling. Great Expectations (GE) is an open-source library and is available in GitHub for public use. Amazon Redshift lets you save the results of your queries back to your Amazon Simple Storage Service (Amazon S3) data lake using open formats like Apache Parquet, so that you can perform additional analytics from other analytics services like Amazon EMR, Amazon Athena, and Amazon SageMaker. With Amazon Redshift, you can query and combine exabytes of structured and semi-structured data across your data warehouse, operational database, and data lake using standard SQL. You can automate the process for data checks via the extensive built-in Great Expectations glossary of rules using PySpark, and it’s flexible for adding or creating new customized rules for your use case.Īmazon Redshift is a cloud data warehouse solution and delivers up to three times better price-performance than other cloud data warehouses. This post discusses a solution for running data reliability checks before loading the data into a target table in Amazon Redshift using the open-source library Great Expectations. DynamoDB, or Amazon Timestream databases.
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