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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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Simplify and speed up Apache Spark applications on Amazon Redshift data with Amazon Redshift integration for Apache Spark

AWS Big Data

For sales across multiple markets, the product sales data such as orders, transactions, and shipment data is available on Amazon S3 in the data lake. The data engineering team can use Apache Spark with Amazon EMR or AWS Glue to analyze this data in Amazon S3. enableHiveSupport().getOrCreate()

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Somehow, the gravity of the data has a geological effect that forms data lakes. Also, data science workflows begin to create feedback loops from the big data side of the illo above over to the DW side. DG emerges for the big data side of the world, e.g., the Alation launch in 2012.

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

AWS Big Data

AWS Step Functions is a fully managed visual workflow service that enables you to build complex data processing pipelines involving a diverse set of extract, transform, and load (ETL) technologies such as AWS Glue , Amazon EMR , and Amazon Redshift. Choose the workflow named ETL_Process. Run the workflow with default input.

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Run Spark SQL on Amazon Athena Spark

AWS Big Data

Modern applications store massive amounts of data on Amazon Simple Storage Service (Amazon S3) data lakes, providing cost-effective and highly durable storage, and allowing you to run analytics and machine learning (ML) from your data lake to generate insights on your data.

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How The Cloud Made ‘Data-Driven Culture’ Possible | Part 1

BizAcuity

2012: Amazon Redshift, the first of its kind cloud-based data warehouse service comes into existence. Microsoft also releases Power BI, a data visualization and business intelligence tool. Google launches BigQuery, its own data warehousing tool and Microsoft introduces Azure SQL Data Warehouse and Azure Data Lake Store.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

He’s been out of Wolfram for a while and writing exquisite science books including Elements: A Visual Explanation of Every Known Atom in the Universe and Molecules: The Architecture of Everything. Historically, grad students in physics and physical sciences have been excellent candidates for data science teams. Or something.