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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

A modern data platform entails maintaining data across multiple layers, targeting diverse platform capabilities like high performance, ease of development, cost-effectiveness, and DataOps features such as CI/CD, lineage, and unit testing. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

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Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

AWS Big Data

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

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Introducing Cloudera DataFlow Designer: Self-service, No-Code Dataflow Design

Cloudera

Developers need to onboard new data sources, chain multiple data transformation steps together, and explore data as it travels through the flow. This allows developers to make changes to their processing logic on the fly while running some test data through their flow and validating that their changes work as intended.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

Data processes that depended upon the previously defective data will likely need to be re-initiated, especially if their functioning was at risk or compromised by the defected data. These processes could include reports, campaigns, or financial documentation. Accuracy should be measured through source documentation (i.e.,

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Cloudera DataFlow Designer: The Key to Agile Data Pipeline Development

Cloudera

Allows them to iteratively develop processing logic and test with as little overhead as possible. Plays nice with existing CI/CD processes to promote a data pipeline to production. Provides monitoring, alerting, and troubleshooting for production data pipelines.

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What is Data Mapping?

Jet Global

This field guide to data mapping will explore how data mapping connects volumes of data for enhanced decision-making. Why Data Mapping is Important Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation.

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The 5 Data Mapping Steps Involved in a Data Migration

Octopai

While the financial stress of migration is usually shouldered by company executives, the entire data team shares the stress of packing up data assets, moving them, unpacking them and putting them in the right locations, and hoping everything got there in one piece, with nothing broken or lost. Test the process. Did it work?

Testing 52