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Key Takeaways: Enhance capabilities through partnerships: AWS, Confluent, and Precisely accelerate mainframe modernization efforts, providing you with essential tools for success. Replicating mainframe data to cloud environments is a challenging but transformative undertaking that boosts your organization’s operational agility and innovation.
Learn more about the DataObservability Summit AI Expo in Austin The AI Expo is a yearly conference in Austin, Texas, organized by Amazon, which showcases the latest advancements in artificial intelligence (AI). The AI Expo is a great opportunity to learn from experts from companies like AWS, IBM, etc.
By walking through this specific implementation, we aim to showcase how you can adapt batch inference to suit various data processing needs, regardless of the data source or nature. Prerequisites To use the batch inference feature, make sure you have satisfied the following requirements: An active AWS account.
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.
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.
This code typically runs inside an AWS Lambda function. The MLOps expert user has access to a SageMaker notebook and the AWS Management Console. For more information, refer to Getting Started with the AWS Management Console. The predicted value indicates the expected value for our target metric based on the training data.
This has created many different data quality tools and offerings in the market today and we’re thrilled to see the innovation. People will need high-quality data to trust information and make decisions. This builds on existing column-level lineage support for AWS Redshift, Google BigQuery, and Snowflake.
Modern Data Architectures Panel To discuss the importance of data quality, 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.
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. But early adopters realized that the expertise and hardware needed to manage these systems properly were complex and expensive.
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 data quality, and informed decision-making capabilities. AWS Glue AWS Glue is Amazon’s serverless ETL tool.
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.
Signals around the quality and integrity of the data are essential if people are to understand and trust it. Data provenance and lineage, for example, clarify an asset’s origin and past usages, important details for a newcomer to understand and trust that asset. Examples include public cloud vendors like AWS, Azure, and GCP.
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. Like they didn’t have to think about, you know, dataobservability, but look, if you provided those data, we captured things about it.
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.
Apache Nifi Apache Nifi is a tool for automating the movement of data between systems. It allows users to design, automate, and monitor data flows, making it easier to handle complex data pipelines. Monte Carlo Monte Carlo is a dataobservability platform that helps engineers detect and resolve data quality issues.
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