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The Importance of Architecture in a Data Platform
Guiding Architecture Principles for Data Platforms
A production-grade, end-to-end agentic AI platform — chat UI, self-hosted LLM, MCP server, LLM observability, medallion data architecture, security guardrails, HA, and cost analy...
The Data Challenge Every industry has its version of the same data engineering problem: massive, complex payloads generated at the edge — far from the cloud, often on unreliable ne...
advancing-data-strategy-with-people-first-architecture
Lambda vs Kappa Architecture
Six months after we standardized on OpenSearch, a pull request introduced Datadog into a service. The ADR existed. It had been discussed, approved, and stored in the repo. The PR w...
An in-depth look at the specific features of Palantir's Foundry, the commercial software platform on which FDP is built, and how the system makes use of them
Build and troubleshoot Snowflake Openflow pipelines faster with Cortex Code, Snowflake's AI agent for data integration and CDC. Get started today.
Why Operations Can’t Keep Up Anymore Modern infrastructure has evolved much faster than the way we operate it. Today’s systems are distributed, constantly changing, and deeply inte...
Представьте: вы заходите в репозиторий, открываете папку schemas и через пять минут понимаете, как устроена база во всём проекте, со всеми связями. Никаких устаревших диаграмм в Co...
When we talk about data analytics the way we set up our tables is really important. This is because it can make a difference, in how well our systems work and how fast they can gro...
Editor’s Note: The following is an article written for and published in DZone’s 2026 Trend Report, Platform Engineering and DevOps: How Internal Platforms, Developer Experience, an...
How shifting the operational focus from isolated data products to systemic domain architecture resolves technical bottlenecks and optimizes platform investment. The post The Domain...
Cloud platforms have historically been built to execute applications at scale. Over the past decade enterprises pushed workloads from private datacenters to the cloud, taking advan...
Architecture diagrams lie, a little. Not on purpose. They show boxes and arrows in clean arrangements and make everything look sequential and tidy. What they cannot show is what fa...
Most enterprise AI projects start with retrieval. You connect Jira, Confluence, SharePoint, and Slack. Maybe a few internal databases nobody has touched in five years. You tune emb...
To act at the speed of business, AI agents must operate in fast and trusted reasoning loops. They need to “think” by reasoning across both your historical context and your live ope...
How modern DevOps teams manage operations using automation, observability, AIOps and self-service to reduce toil and improve reliability.
Enterprise data stacks were built for humans running scheduled queries. As AI agents increasingly act autonomously on behalf of businesses around the clock, that architecture is br...
By Tina Martin of Ideaspired As organizations evolve, the complexity of their digital infrastructure often grows at a faster rate than
One of the challenges of migrating older applications to a cloud-native, modern IT architecture is how to provide persistent storage
In this post, we show you a reference architecture that automates sensitive data discovery across legal document repositories on Amazon Web Services (AWS), demonstrate how to captu...
Artificial intelligence is evolving from experimental to operational, with significant infrastructure costs tied to its deployment. Enterprises must reconsider their architectural...
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