1 min read
Building with Fabric Lakehouse: The Best of Data Lakes and Warehouses
The Lakehouse is where most of your Fabric data engineering work will happen. Tomorrow, I will cover Data Factory in Fabric for orchestrating data movement…
Blog
Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 49 of 92
1 min read
OneLake: The Foundation of Microsoft Fabric
OneLake is a single, unified, logical data lake for your entire organization. Think of it as "OneDrive for data" - automatically provisioned when you enable…
1 min read
Understanding Microsoft Fabric as a Unified Analytics Platform
This fragmentation creates operational overhead that Fabric aims to eliminate.
3 min read
Looking Ahead to Microsoft Build 2023: What to Expect for Data and AI
Microsoft Build 2023 is scheduled for May 23–25, 2023, in Seattle—and based on the product roadmap signals Microsoft had been sending through Ignite 2022…
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Building AI Agents: From Chatbots to Autonomous Systems
AI agents represent the next evolution of AI systems. By combining planning, tool use, and memory, they can autonomously accomplish complex goals while…
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LLM Caching Strategies: Reducing Costs and Latency
LLM caching strategies are essential for production systems. By combining exact matching, semantic similarity, and intelligent invalidation, you can…
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Semantic Search for Enterprise: Beyond Keyword Matching
Enterprise semantic search transforms how organizations find and use knowledge. By understanding meaning rather than just matching keywords, these systems…
1 min read
AI Orchestration Patterns: Coordinating Multi-Model Systems
AI orchestration patterns enable building sophisticated AI systems from modular components. The key is designing for reliability, observability, and…
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Intelligent Document Processing Pipelines with AI
Intelligent document processing transforms unstructured documents into structured, actionable data. Combining OCR, layout analysis, and LLM understanding…
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Entity Extraction at Scale with LLMs
Entity extraction at scale transforms unstructured text into structured knowledge. Combining LLM intelligence with distributed processing enables insights…
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Anomaly Detection at Scale with AI
Anomaly detection at scale combines statistical rigor with AI understanding. Systems that both detect and explain anomalies enable faster, more confident…
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AI-Powered Data Quality: Intelligent Data Validation
AI-powered data quality goes beyond static rules. Intelligent systems understand context, detect subtle anomalies, and provide actionable recommendations…
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Feature Engineering with LLMs: AI-Powered Feature Creation
LLM-powered feature engineering unlocks value from unstructured data. Combine semantic understanding with traditional ML for more powerful predictive models.
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Spark ML Patterns for Production Systems
Spark ML provides battle-tested patterns for production machine learning. From feature engineering to model persistence, these patterns ensure reliable ML…
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SynapseML: Distributed AI at Scale
SynapseML democratizes distributed machine learning. Train on massive datasets, tune hyperparameters in parallel, and deploy models at scale - all with…
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Cognitive Services in Azure Synapse: Built-in AI Capabilities
Cognitive Services in Synapse brings AI capabilities directly into your data engineering workflows. Process millions of records with sentiment, entities…
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Azure Synapse Analytics + OpenAI: Intelligent Data Warehousing
Azure Synapse with OpenAI transforms data warehousing from technical SQL expertise to conversational data access. Business users can explore enterprise data…
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Code Completion for Data Science Notebooks
Return only the docstring (including quotes).""" response = await self.client.chatcompletion( model="gpt-35-turbo", messages=[{"role": "user", "content"…
3 min read
AI Assistants in Jupyter and Databricks Notebooks
Error: {type(error).name}: {str(error)} response = openai.ChatCompletion.create( engine="gpt-4", messages=[{"role": "user", "content": prompt}] ) return…
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Building AI Data Analysis Assistants
AI data analysis assistants make insights accessible to everyone, regardless of technical expertise. The key is combining natural language understanding…
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LLM-Powered SQL Generation: Patterns and Practices
LLM-powered SQL generation democratizes data access. With proper validation and safety measures, it enables anyone to query databases using natural language.
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Azure Databricks + OpenAI: LLMs for Data Engineering
Azure Databricks with Azure OpenAI creates intelligent data platforms where natural language becomes the interface for data engineering tasks.
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Edge Copilot: AI Built Into Your Browser
Edge Copilot brings AI assistance to every webpage, transforming passive browsing into active learning and productivity.
1 min read
Bing Chat: GPT-4 Powered Search
Bing Chat represents the future of search: not just finding links, but synthesizing answers from the web with AI understanding.