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Five Modern Data Architecture Trends

David Menninger's Analyst Perspectives

I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.

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Creating an AI-Ready Data Architecture in the Cloud


But the data architectures that feed into them are just as vital. Sophisticated ML-as-a-service platforms such as Facebook’s FBLearner Flow are ideal for delivering AI-at-scale. Automation is what AI algorithms do best.


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Exploring Data Mesh: A Paradigm Shift in Data Architecture


Let’s explore Data Mesh, a modern approach to data architecture that decentralizes data ownership and management.

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DataOps: The Foundation for Your Agile Data Architecture


Learn about four data architectures patterns for agility - DataOps, Data Fabric, Data Mesh & Functional Data Engineering - & an example combining all four. The post DataOps: The Foundation for Your Agile Data Architecture first appeared on DataKitchen.

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The Next-Generation Cloud Data Lake: An Open, No-Copy Data Architecture

However, they often struggle with increasingly larger data volumes, reverting back to bottlenecking data access to manage large numbers of data engineering requests and rising data warehousing costs. This new open data architecture is built to maximize data access with minimal data movement and no data copies.

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Building a Modern Data Architecture for the 2020s


The post Building a Modern Data Architecture for the 2020s first appeared on DataKitchen.

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Why Is Data Architecture Important for the Success of AI?


If there’s one thing we’ve learned at Dataiku after talking to thousands of prospects and customers about their data architecture it’s that architecture frameworks tend to be more aspirational than realistic because, at the enterprise level, data architecture is both complex and constantly changing.

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Build Your Open Data Lakehouse on Apache Iceberg

Speaker: Veena Vasudevan and Jason Hughes

By moving analytic workloads to the data lakehouse you can save money, make more of your data accessible to consumers faster, and provide users a better experience. In this webinar, Dremio and AWS will discuss the most common challenges in data architecture and how to overcome them with an open data lakehouse architecture on AWS.

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The Unexpected Cost of Data Copies

Unfortunately, data replication, transformation, and movement can result in longer time to insight, reduced efficiency, elevated costs, and increased security and compliance risk.

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Modern Data Architecture for Embedded Analytics

Every data-driven project calls for a review of your data architecture—and that includes embedded analytics. Before you add new dashboards and reports to your application, you need to evaluate your data architecture with analytics in mind. 9 questions to ask yourself when planning your ideal architecture.

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Checklist Report: Preparing for the Next-Generation Cloud Data Architecture

Data architectures to support reporting, business intelligence, and analytics have evolved dramatically over the past 10 years. Download this TDWI Checklist report to understand: How your organization can make this transition to a modernized data architecture. The decision making around this transition.

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How to Democratize Data Across Your Organization Using a Semantic Layer

Speaker: speakers from Verizon, Snowflake, Affinity Federal Credit Union, EverQuote, and AtScale

Using predictive/prescriptive analytics, given the available data. The impact that data literacy programs and using a semantic layer can deliver. Avoiding common analytics infrastructure and data architecture challenges. Thursday, July 29th, 2021 at 11AM PDT, 2PM EDT, 7PM GMT.