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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 DataGovernance application.
DataObservability and Data Quality are two key aspects of data management. The focus of this blog is going to be on DataObservability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.
The speaker is Andrew Madson, a data analytics leader and educator. The event is for anyone interested in learning about generative AI and data storytelling, including business leaders, datascientists, and enthusiasts. 360 curates’ content and learning paths to suit virtually every IT role and team configuration.
It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may require consider your dataobservability strategy. Is your datagovernance structure up to the task?
This will become more important as the volume of this data grows in scale. DataGovernanceDatagovernance is the process of managing data to ensure its quality, accuracy, and security. Datagovernance is becoming increasingly important as organizations become more reliant on data.
The financial services industry has been in the process of modernizing its datagovernance for more than a decade. But as we inch closer to global economic downturn, the need for top-notch governance has become increasingly urgent. That’s why data pipeline observability is so important.
Data domain teams have a better understanding of the data and their unique use cases, making them better positioned to enhance the value of their data and make it available for data teams. With this approach, demands on each team are more manageable, and analysts can quickly get the data they need.
Some popular end-to-end MLOps platforms in 2023 Amazon SageMaker Amazon SageMaker provides a unified interface for data preprocessing, model training, and experimentation, allowing datascientists to collaborate and share code easily. Check out the Kubeflow documentation.
Cloud adoption is key to creating a modern data architecture environment because it offers cost efficiencies, rapid deployment, and agility. Salam noted that organizations are offloading computational horsepower and data from on-premises infrastructure to the cloud.
Alation and Bigeye have partnered to bring dataobservability and data quality monitoring into the data catalog. Read to learn how our newly combined capabilities put more trustworthy, quality data into the hands of those who are best equipped to leverage it.
And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data. Protected and compliant data.
This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Instead, they can readily access and analyze datasets with greater confidence.
Alation and Soda are excited to announce a new partnership, which will bring powerful data-quality capabilities into the data catalog. Soda’s dataobservability platform empowers data teams to discover and collaboratively resolve data issues quickly. Defining good data across all personas, too, is essential.
Data quality is crucial across various domains within an organization. For example, software engineers focus on operational accuracy and efficiency, while datascientists require clean data for training machine learning models. Without high-quality data, even the most advanced models can't deliver value.
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