Remove 2012 Remove Data Lake Remove Data Quality Remove Testing
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Measure performance of AWS Glue Data Quality for ETL pipelines

AWS Big Data

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

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How SumUp made digital analytics more accessible using AWS Glue

AWS Big Data

Founded in 2012, SumUp is the financial partner for more than 4 million small merchants in over 35 markets worldwide, helping them start, run and grow their business. Unless, of course, the rest of their data also resides in the Google Cloud. The Data Science teams also use this data for churn prediction and CLTV modeling.

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Handle UPSERT data operations using open-source Delta Lake and AWS Glue

AWS Big Data

Many customers need an ACID transaction (atomic, consistent, isolated, durable) data lake that can log change data capture (CDC) from operational data sources. There is also demand for merging real-time data into batch data. Delta Lake framework provides these two capabilities. Choose Create policy.

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Build efficient ETL pipelines with AWS Step Functions distributed map and redrive feature

AWS Big Data

Solution overview One of the common functionalities involved in data pipelines is extracting data from multiple data sources and exporting it to a data lake or synchronizing the data to another database. Choose the workflow named ETL_Process. Run the workflow with default input.

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