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generally available on May 24, Alation introduces the Open DataQuality Initiative for the modern data stack, giving customers the freedom to choose the dataquality 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.
First, private cloud infrastructure providers like Amazon (AWS), Microsoft (Azure), and Google (GCP) began by offering more cost-effective and elastic resources for fast access to infrastructure. Now, almost any company can build a solid, cost-effective data analytics or BI practice grounded in these new cloud platforms.
Watch the webinar AI You Can Trust Watch this webinar and see how we explore organizational challenges in maintaining data integrity for AI applications and real-world use cases showcasing the transformative impact of high-integrity data on AI success. Fuel your AI applications with trusted data to power reliable results.
For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.
At Precisely’s Trust ’23 conference, Chief Operating Officer Eric Yau hosted an expert panel discussion on modern data architectures. The group kicked off the session by exchanging ideas about what it means to have a modern data architecture. Dataobservability also helps users identify the root cause of problem in the data.
Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high dataquality, and informed decision-making capabilities. AWS Glue AWS Glue is Amazon’s serverless ETL tool.
Modern Data Architectures Panel To discuss the importance of dataquality, governance, and observability for digital transformation success, Precisely COO Eric Yau is joined by panelists Sanjeev Mohan, former Gartner Research VP, Atif Salam, CxO Advisor & Enterprise Technologist at AWS, and Tendü Yogurtçu, PhD, CTO at Precisely.
For example, let’s take Airflow , AWS SageMaker pipelines. We’re building on top of Hamilton, which is an open-source framework for describing data flows. One of the features that Hamilton has is that it has a really lightweight dataquality runtime check. Piotr: Sounds like something with data, right?
They’re where the world’s transactional data originates – and because that essential data can’t remain siloed, organizations are undertaking modernization initiatives to provide access to mainframe data in the cloud. That approach assumes that good dataquality will be self-sustaining.
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