Remove Data Governance Remove Data Observability Remove ETL
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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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Data Fabric and Address Verification Interface

IBM Data Science in Practice

IBM’s Next Generation DataStage is an ETL tool to build data pipelines and automate the effort in data cleansing, integration and preparation. As a part of data pipeline, Address Verification Interface (AVI) can remediate bad address data.

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Modern Data Architectures Provide a Foundation for Innovation

Precisely

Salam noted that organizations are offloading computational horsepower and data from on-premises infrastructure to the cloud. This provides developers, engineers, data scientists and leaders with the opportunity to more easily experiment with new data practices such as zero-ETL or technologies like AI/ML.

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The Evolution of Metadata Platforms vs. Data Platforms

Dataversity

Watching closely the evolution of metadata platforms (later rechristened as Data Governance platforms due to their focus), as somebody who has implemented and built Data Governance solutions on top of these platforms, I see a significant evolution in their architecture as well as the use cases they support.

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Exploring Innovations in Data Integrity

Precisely

Business and IT teams need a simple, seamless data integrity solution that enables them to: Collaborate on making data accessible Ensure and maintain its quality Enrich the data with third-party context Real-time Access to Data In the past, businesses struggled to get timely access to data for both analytical and operational use cases.

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Maximize the Power of dbt and Snowflake to Achieve Efficient and Scalable Data Vault Solutions

phData

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Having model-level data validations along with implementing a data observability framework helps to address the data vault’s data quality challenges.

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

Multiple data applications and formats make it harder for organizations to access, govern, manage and use all their data for AI effectively. Scaling data and AI with technology, people and processes Enabling data as a differentiator for AI requires a balance of technology, people and processes.

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