Data Architecture Related Topics 
David Menninger's Analyst Perspectives
MARCH 30, 2020
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.
Corinium
APRIL 17, 2020
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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CIO Business Intelligence
NOVEMBER 27, 2024
However, the biggest challenge for most organizations in adopting Operational AI is outdated or inadequate data infrastructure. To succeed, Operational AI requires a modern data architecture.
CIO Business Intelligence
DECEMBER 20, 2024
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.
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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.
DataKitchen
AUGUST 3, 2021
The post Building a Modern Data Architecture for the 2020s first appeared on DataKitchen.
KDnuggets
OCTOBER 13, 2023
Let’s explore Data Mesh, a modern approach to data architecture that decentralizes data ownership and management.
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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.
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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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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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.
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.
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