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Accelerate Your Path to a Modern Analytics Architecture

Teradata

A modern analytics architecture means something different to everyone. What does it mean for your organization? Find out more.

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Modernizing a public health system with Teradata’s connected analytic architecture

Teradata

How do you accelerate disease prevention and response? Teradata provides a response to help accelerate public health infrastructure modernization.

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How to Get Your Cloud Analytic Architecture Right

Teradata

Getting your Cloud data architecture right starts with understanding which data products you need, the roles they perform, & the functional & non-functional characteristics that those roles demand.

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A Multipurpose Database For Transactions And Analytics To Simplify Your Data Architecture With Singlestore

Data Engineering Podcast

By supporting fast, in-memory row-based queries and columnar on-disk representation, it lets your transactional and analytical workloads run in the same database. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

Database 100
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Best Practices for Deploying & Scaling Embedded Analytics

Read more about how to simplify the deployment and scalability of your embedded analytics, along with important considerations for your: Environment Architecture: An embedded analytics architecture is very similar to a typical web architecture.

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Spotter: Your AI Analyst

ThoughtSpot

While foundational models like GPT are trained for natural language, they cant accommodate every complexity of real-world data on their ownthink: business context, analytics expressibility, massive and messy datasets. Thats where ThoughtSpots architecture comes in.

BI 59
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Beyond Kafka: Conversation with Jark Wu on Fluss - Streaming Storage for Real-Time Analytics

Data Engineering Weekly

Kafka is designed for streaming events, but Fluss is designed for streaming analytics. Architecture Difference The first difference is the Data Model. The fourth difference is the Lakehouse Architecture. Fluss embraces the Lakehouse Architecture. It excels in event-driven architectures and data pipelines.

Kafka 74