How to Connect Power BI to Local and Cloud PostgreSQL Databases via Aiven

·1 views

Power BI can be connected to PostgreSQL databases both locally and via cloud-hosted services like Aiven, following a structured setup process. For local connections, users configure DBeaver with localhost credentials before linking Power BI Desktop through the Get Data option using the default port 5432. Cloud connections through Aiven require an additional SSL step, where a CA certificate downloaded from the Aiven dashboard must be imported into Windows' Trusted Root Certification Authorities. Once the certificate is successfully imported, the Aiven cloud database behaves similarly to a local connection within Power BI. After either setup, users can model data, write DAX formulas, and build live dashboards reflecting the latest database content.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Hybrid Search RAG Cuts Missed Retrievals by 30–40% in Internal Doc Systems

A production guide published on DEV Community details how combining vector and keyword search in a Retrieval-Augmented Generation (RAG) pipeline significantly improves retrieval accuracy over internal documents. The approach runs dense vector search (embeddings) and sparse keyword search (BM25 via Elasticsearch) in parallel, then fuses their normalized scores to surface the most relevant results. One team at LogicLoop reported a 22% improvement in MRR@10 after switching from vector-only to hybrid search on their engineering wiki, with benchmark tests across 500,000 Confluence pages and Jira tickets showing hybrid search achieving an MRR@10 of 0.50 versus 0.41 for vector-only. The tradeoff includes roughly 30ms of additional latency and doubled storage overhead from maintaining two indexes simultaneously. A notable edge case was identified for very short queries of one to two words, where keyword search tends to dominate, prompting the team to route such queries to keyword-only search via a simple length check.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Top LLM Observability Platforms for Enterprises Compared Ahead of 2026

As enterprises scale AI agents from prototypes to production, standard monitoring tools have proven inadequate for tracking the unique challenges of large language model applications. A new category of specialized LLM observability platforms has emerged to provide visibility into prompts, responses, latency, cost, and output quality. Key evaluation criteria for these platforms include end-to-end tracing, root cause analysis, data curation, and enterprise-grade security and scalability. Leading options in 2026 include Maxim AI, LangSmith, Langfuse, and Arize, each offering distinct strengths suited to different team needs and existing toolchains. The right platform choice ultimately depends on an organization's scale, technical ecosystem, and the complexity of the AI agents it deploys.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How to Build Production-Ready AI Agents with FastAPI and OpenAI SDK

A developer with over a year of running AI agents in production has shared a practical guide to building them using FastAPI and the OpenAI Python SDK. The tutorial covers a minimal FastAPI setup that accepts prompts and returns OpenAI responses, handling common errors like rate limits and timeouts. It then demonstrates how to enable tool use, allowing the agent to call external functions such as internal APIs, with the OpenAI SDK managing the function-calling loop. The guide also addresses stateful agents, recommending Redis for persisting chat history and user preferences across sessions using Pydantic models. The author emphasizes avoiding manual state machine implementations in favor of letting the SDK handle the tool call and response cycle for reliability.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Power BI Basics: How Relationships, Schemas, and Joins Work Together

A beginner Power BI user shares lessons learned after struggling to connect multiple tables in their first multi-table assignment. Unlike Excel's single-sheet approach, Power BI encourages splitting data into separate tables linked by common ID columns, reducing repetition and mirroring real database design. Relationships are created in Model view by connecting shared columns, with one-to-many being the most common type. The star schema — one central fact table surrounded by dimension tables — proved simpler to manage than the more complex snowflake schema for beginner projects. In Power Query, joins like Left Outer and Inner merges allow tables to be combined before loading into the model, with Left Outer recommended as a safer default to avoid unintended data loss.

0 comments Read more at DEV Community