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In modern data architectures, Apache Iceberg has emerged as a popular table format for data lakes, offering key features including ACID transactions and concurrent write support. Consider a common scenario: A streaming pipeline continuously writes data to an Iceberg table while scheduled maintenance jobs perform compaction operations.
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Amazon DataZone is a data management service that makes it faster and easier for customers to catalog, discover, share, and govern data stored across AWS, on premises, and from third-party sources. Use case Amazon DataZone addresses your data sharing challenges and optimizes data availability.
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We are excited to announce the acquisition of Octopai , a leading data lineage and catalog platform that provides data discovery and governance for enterprises to enhance their data-driven decision making.
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If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). You’re responsible for the design, the product-market fit, and ultimately for getting the product out the door. Why AI software development is different.
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Specifically, in the modern era of massive data collections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. Clearly, such a content delivery system is not good for business productivity. I believe that this product is good” is quite different from a post that states “Yeah, sure.
This week on the keynote stages at AWS re:Invent 2024, you heard from Matt Garman, CEO, AWS, and Swami Sivasubramanian, VP of AI and Data, AWS, speak about the next generation of Amazon SageMaker , the center for all of your data, analytics, and AI. The relationship between analytics and AI is rapidly evolving.
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Below is our third post (3 of 5) on combining data mesh with DataOps to foster greater innovation while addressing the challenges of a decentralized architecture. We’ve talked about data mesh in organizational terms (see our first post, “ What is a Data Mesh? ”) and how team structure supports agility. Source: Thoughtworks.
A look at the landscape of tools for building and deploying robust, production-ready machine learning models. A few factors are contributing to this strong interest in implementing ML in products and services. Quality depends not just on code, but also on data, tuning, regular updates, and retraining.
A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. So, why is this new open source project resonating with data scientists and machine learning engineers? The upcoming 0.9.0
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Re-platforming to reduce friction Marsh McLennan had been running several strategic data centers globally, with some workloads on the cloud that had sprung up organically. Several co-location centers host the remainder of the firm’s workloads, and Marsh McLennans big data centers will go away once all the workloads are moved, Beswick says.
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Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. These changes may include requirements drift, data drift, model drift, or concept drift. I suggest that the simplest business strategy starts with answering three basic questions: What?
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Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Curate the data.
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