Best of Tinybird — 2025

  1. 1
    Article
    Avatar of tinybirdTinybird·1y

    Local first.

    Tinybird introduces Tinybird Local, a Docker container that allows developers to run a full instance of Tinybird's data processing platform on their laptops. This local-first approach enables development, testing, and deployment of data applications both locally and in the cloud seamlessly. The container includes core Tinybird functionalities and several optimizations for performance but lacks some cloud-specific features. The initiative aims to provide a more controlled, offline, and versatile development environment.

  2. 2
    Article
    Avatar of tinybirdTinybird·1y

    Using LLMs to generate user-defined real-time data visualizations

    Developers are increasingly using Tinybird to track LLM usage, costs, and performance in AI applications. A new app template called the LLM Performance Tracker allows users to generate real-time data visualizations. The core components include a Tinybird datasource, a Tinybird pipe, a React component, and an AI API route. The backend processes user input to generate chart parameters, while the frontend visualizes the data. This approach emphasizes the importance of performant analytics backends and cautious LLM usage for secure and scalable data visualization.

  3. 3
    Article
    Avatar of tinybirdTinybird·1y

    Which LLM writes the best analytical SQL?

    Tinybird's LLM SQL Generation Benchmark evaluates how 19 popular language models perform in generating SQL queries to filter and aggregate large datasets. Comparing models like OpenAI's GPT-4 Turbo and Anthropic's Claude, the benchmark measures accuracy, efficiency, and query latency, highlighting the challenges LLMs face in writing semantically correct SQL efficiently. The analysis shows humans leading in efficiency, while LLMs often struggle with contextual understanding and optimization opportunities.

  4. 4
    Article
    Avatar of tinybirdTinybird·44w

    Why LLMs struggle with analytics

    LLMs face significant challenges when working with analytical data, struggling with tabular data interpretation, SQL generation accuracy, and complex database schemas. The key to successful agentic analytics lies in providing comprehensive context through detailed documentation, semantic models, and sample data rather than expecting perfect SQL generation. Building query validation loops with error feedback, using LLM-as-a-judge evaluators, and focusing on business understanding over technical perfection enables more reliable analytical insights.

  5. 5
    Article
    Avatar of tinybirdTinybird·1y

    Writing tests sucks. Use LLMs so it sucks less.

    The post discusses the challenges and solutions for testing in data engineering. It highlights several key obstacles, such as data variability, complex transformations, and lack of tooling. Tinybird aims to address these issues with tools like 'tb mock' for generating realistic test data, and 'tb test' for validating data transformations. The use of LLMs to handle mundane aspects of test generation is emphasized, making testing less tedious and more efficient.

  6. 6
    Article
    Avatar of tinybirdTinybird·1y

    Build a Datadog alternative in 5 minutes

    A free Next.js app and Tinybird backend can be deployed in less than 5 minutes as a simple Datadog alternative for log analytics. This guide covers the stack, development workflow, instrumentation, data handling, and API setup. A focus is placed on building a basic, functional version to iterate quickly, with optimizations for scale coming later. Detailed instructions provide insight into using mock data for testing, integrating APIs with Next.js, and deploying using CI/CD processes.

  7. 7
    Article
    Avatar of tinybirdTinybird·1y

    Run Tinybird on your own infrastructure

    Tinybird Self-Managed is now available in beta, allowing users to deploy Tinybird's real-time data platform on their own AWS infrastructure, with support for GCP and Azure coming soon. This version provides greater control over data environments, integrating with private data sources and optimizing hardware resources as needed. Future updates will include expanded cloud support, automated upgrades, and advanced monitoring.

  8. 8
    Article
    Avatar of tinybirdTinybird·23w

    Build a Real-Time E-Commerce Analytics API from Kafka in 15 Minutes

    A step-by-step guide to building a real-time e-commerce analytics API using Kafka as the data source. Covers connecting to Kafka, ingesting order events, enriching data with dimension tables and PostgreSQL, creating materialized views for pre-aggregated metrics, and exposing multiple API endpoints. The tutorial progresses from a basic 5-minute setup querying raw Kafka data to advanced features including data enrichment, automated PostgreSQL syncing, and optimized aggregations using materialized views. All implementation uses SQL and configuration without requiring application code.

  9. 9
    Article
    Avatar of tinybirdTinybird·31w

    Flink is a 95% problem

    Apache Flink is marketed as essential for real-time data processing, but it's overkill for 95% of use cases. Most real-time problems can be solved with simpler solutions: HTTP services with Postgres (65%), OLAP databases like ClickHouse (25%), or custom solutions (5%). Only about 5% of companies actually need Flink's complexity. The platform introduces massive operational overhead including new APIs to learn, additional infrastructure (Kafka, ZooKeeper/K8s), 700+ configuration parameters, complex observability requirements, and JVM dependency. Even Flink's creators acknowledge its limitations, and recent acquisitions of Flink-based companies suggest limited market traction. For most organizations under 100 developers, simpler alternatives like ClickHouse with SQL or native programming language Kafka consumers provide better cost-benefit tradeoffs without the engineering complexity.

  10. 10
    Article
    Avatar of tinybirdTinybird·1y

    Ship data as you ship code: Tinybird is local-first.

    Tinybird is transitioning to a local-first workflow to simplify working with large amounts of real-time data. The new approach allows developers to build, test, validate, and deploy data projects locally before pushing changes to the cloud. Key features include local project validation, seamless CI/CD integration, live schema migrations, and AI-powered IDE support. The beta version will be available soon.

  11. 11
    Article
    Avatar of tinybirdTinybird·1y

    dbt in real-time

    Tinybird offers an alternative to dbt for real-time analytics, simplifying the process of migrating API use cases from dbt. It provides built-in support for real-time processing, API endpoint creation, and simplifies the tech stack by consolidating all data operations. Tinybird uses ClickHouse for faster performance, especially for API responses. Migrating involves mapping dbt concepts to Tinybird equivalents, such as materialized views for incremental updates, and creating optimized data source schemas.

  12. 12
    Article
    Avatar of tinybirdTinybird·1y

    You built a Datadog alternative. Now scale it.

    Learn how to optimize a Logs Explorer template to handle trillions of logs with sub-second refresh times in a Next.js application accessed by thousands of concurrent users. Discover strategies like using materialized views, optimizing the data layer without affecting web components, and enhancing UX for scale. Key topics include optimizing sidebar counters, time series charts, free text search, and pagination.

  13. 13
    Article
    Avatar of tinybirdTinybird·48w

    Introducing the Tinybird MCP Server: Your real-time data, LLM-ready

    Tinybird launches its MCP Server, a hosted solution that enables LLMs and AI agents to securely access real-time data from Tinybird workspaces. The server provides tools for data exploration, text-to-SQL conversion, and query execution, with token-based security and built-in observability. It supports various AI frameworks including Agno, Pydantic AI, and Vercel AI SDK, making real-time analytics accessible through natural language queries without infrastructure setup.

  14. 14
    Article
    Avatar of tinybirdTinybird·50w

    MCP vs APIs: When to Use Which for AI Agent Development

    Model Context Protocol (MCP) and traditional APIs serve different purposes in AI agent development. MCP excels at enabling dynamic tool selection, agent autonomy, and rapid prototyping by providing a standardized way for LLMs to discover and use tools conversationally. Traditional APIs are better for performance-critical applications, complex data operations, and deterministic workflows requiring strict security controls. The most effective approach often combines both: using MCP for flexible reasoning and natural language interactions, while leveraging direct API calls for bulk operations and enforcing constraints. MCP doesn't replace APIs but adds a conversational layer that makes them LLM-friendly.

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