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The Importance of Architecture in a Data Platform
Guiding Architecture Principles for Data Platforms
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...
If you're building production AI agent systems, you've probably assembled an architecture that looks something like this: a relational database (or document store) for current stat...
Everything in this stack runs on your laptop. No API keys. No cloud bills. No data leaving the building. Here’s how to build it.Continue reading on Medium »
Learn why customer data pipelines are moving to infrastructure as code and how IaC improves reliability, governance, and scalability.
Snowflake Storage for Apache Icebergâ„¢ Tables removes self-managed storage complexity while delivering resilient, high-performance interoperability across engines using the open I...
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...
Built on IBM watsonx, Tata Play Fiber’s new data lakehouse will optimise and scale AI workloads while consolidating structured and semi-structured data into a trusted, unified foun...
In the enterprise landscape, data is often highly fragmented across multiple source systems. Data curation is the process of organizing, cleaning, and enriching raw data to transfo...
In many enterprise lakehouse environments, the biggest ingestion challenge is not data volume; it is inconsistency. As platforms grow, data starts arriving from many different syst...
How shifting the operational focus from isolated data products to systemic domain architecture resolves technical bottlenecks and optimizes platform investment. The post The Domain...
Traditional lakehouses were engineered for the era of reporting, not the high-velocity, multimodal demands of AI agents. To bridge this gap, architecture must evolve into an AI-nat...
Railway data integration is becoming a planning priority Railway data integration is no longer just a technical concern sitting in the background of wider digital change. It has be...
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
Modern data engineering increasingly relies on streaming data, and Databricks Lakeflow provides a metadata-driven way to orchestrate streaming pipelines. Instead of writing imperat...
Here’s how embedded analytics quietly fails. Product teams promise analytics features that customers actually want, but once development starts, most of the time disappears into pr...
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...
Agentic AI pipelines are computational architectures where multiple specialized AI agents collaborate to complete complex tasks. Each agent in the pipeline handles a specific funct...
Presented by EquinixDigital systems are central to economic resilience. But the governance models supporting them were designed for a bygone era, when systems were smaller, often c...
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...
Part 3 of 5: Object Store and Annotation InfrastructureContinue reading on Medium »
Today, at the Apache Iceberg Summit in San Francisco, we are announcing the preview of read and write interoperability between BigQuery and Iceberg-compatible engines, including Tr...
In this post, you build a unified pipeline using Apache Iceberg and Amazon Managed Service for Apache Flink that replaces the dual-pipeline approach. This walkthrough is for interm...
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