Remove Data Lake Remove Data Quality Remove Marketing
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Drug Launch Case Study: Amazing Efficiency Using DataOps

DataKitchen

data engineers delivered over 100 lines of code and 1.5 data quality tests every day to support a cast of analysts and customers. They opted for Snowflake, a cloud-native data platform ideal for SQL-based analysis. It is necessary to have more than a data lake and a database.

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Data Quality Power Moves: Scorecards & Data Checks for Organizational Impact

DataKitchen

A DataOps Approach to Data Quality The Growing Complexity of Data Quality Data quality issues are widespread, affecting organizations across industries, from manufacturing to healthcare and financial services. 73% of data practitioners do not trust their data (IDC).

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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

With improved access and collaboration, you’ll be able to create and securely share analytics and AI artifacts and bring data and AI products to market faster. This innovation drives an important change: you’ll no longer have to copy or move data between data lake and data warehouses.

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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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AWS Glue Data Quality is Generally Available

AWS Big Data

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning. It takes days for data engineers to identify and implement data quality rules.

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Fire Your Super-Smart Data Consultants with DataOps

DataKitchen

The strategic value of analytics is widely recognized, but the turnaround time of analytics teams typically can’t support the decision-making needs of executives coping with fast-paced market conditions. When internal resources fall short, companies outsource data engineering and analytics.

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Steps taken to build Sevita’s first enterprise data platform

CIO Business Intelligence

For our pediatrics business, we’re using data to improve our marketing efforts to better recruit foster care providers, and to help us see where the greatest needs are by state, region, and program. We pulled these people together, and defined use cases we could all agree were the best to demonstrate our new data capability.