data-analysis

Serverless vector database at the edge with Cloudflare Vectorize. Use when: building semantic search on Cloudflare Workers, RAG pipelines at the edge, low-latency vector similarity search, or storing and querying embeddings without managing a separate vector database.

Analyze spreadsheet data, generate insights, and create visualizations. Use when a user asks to analyze data, explore a dataset, find trends, generate statistics, create charts from CSV or Excel data, summarize data, or answer questions about tabular data.

Configure and manage Datadog for infrastructure monitoring, application performance monitoring (APM), log management, and alerting. Use when a user needs to set up Datadog agents, create dashboards, configure monitors and alerts, integrate services, or query metrics and logs…

dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.

You are an expert in dlt, the open-source Python library for building data pipelines. You help developers load data from any API, file, or database into warehouses and lakes using simple Python decorators — with automatic schema inference, incremental loading, and built-in data…

DuckDB is an in-process analytical database that runs embedded inside your application with zero external dependencies. It can query CSV, Parquet, and JSON files directly without loading them into tables first, making it ideal for local data exploration, ETL pipelines, and…

Configure Fluentd for unified log collection, routing, filtering, and forwarding across distributed systems. Use when a user needs to aggregate logs from multiple sources, transform log data with filters, route logs to different destinations, or set up Fluentd as a Kubernetes…

Analyze massive datasets with Google BigQuery. Run SQL queries on petabytes of data, load and stream data in real-time, create materialized views, and use BigQuery ML for machine learning models directly in SQL.

Provision and manage Cloud SQL instances on Google Cloud for MySQL, PostgreSQL, and SQL Server. Configure high availability, read replicas, automated backups, IAM database authentication, the Cloud SQL Auth Proxy, and Terraform deployments. Use for managed relational databases…

Investigate Google Cloud networking issues by analyzing VPC Flow Logs, firewall logs, Cloud NAT logs, threat logs, and networking metrics. Run Connectivity Tests for path diagnostics and use BigQuery linked datasets for high-volume traffic analysis. Use when troubleshooting…

Grafana is an open-source visualization and dashboarding platform that connects to dozens of data sources including Prometheus, PostgreSQL, ClickHouse, and Elasticsearch. It lets you build interactive dashboards with panels, set up alerting rules, and manage everything as code…

Great Expectations is a Python framework for data quality testing and validation. Learn to define expectations, create validation suites, build data docs, and integrate with data pipelines for automated quality checks.

Assists with interactive data analysis, visualization, and reproducible research using Jupyter notebooks. Use when building notebooks that combine code with rich output, managing kernels, converting to reports, or parameterizing notebooks for batch execution. Trigger words:…

Configure and use Kibana for Elasticsearch data visualization, dashboard creation, and log exploration. Use when a user needs to build dashboards with Lens, write KQL queries, set up data views, create visualizations, or configure Kibana spaces and role-based access.

Embedded vector database with LanceDB — serverless, zero-config vector search for AI applications. Use when someone asks to "vector search without a server", "embedded vector database", "LanceDB", "local vector search", "serverless vector DB", "vector search in a file", or…

Metabase is an open-source business intelligence tool for creating dashboards and visualizations. Learn Docker deployment, database connections, creating questions and dashboards, embedding analytics, and API usage.

MySQL is the world's most popular open-source relational database management system. Learn installation, SQL queries, indexing strategies, replication setup, and client integration with Node.js (mysql2) and Python (mysql-connector).

Set up end-to-end observability for microservices. Use when someone asks to "add tracing", "set up monitoring", "configure OpenTelemetry", "build Grafana dashboards", "distributed tracing", "structured logging", "metrics collection", or "debug production issues". Covers…

Configure PagerDuty for incident management, on-call scheduling, alert routing, and escalation policies. Use when a user needs to set up PagerDuty services, create escalation policies, configure integrations with monitoring tools, manage on-call rotations, or automate incident…

PandasAI enables natural language queries on pandas DataFrames using LLMs. Learn to ask questions in plain English, generate charts, clean data, and integrate with OpenAI and local models for conversational data analysis.

Assists with loading, cleaning, transforming, and analyzing tabular data using pandas. Use when importing CSV/Excel/SQL data, handling missing values, performing groupby aggregations, merging datasets, working with time series, or building analysis-ready datasets. Trigger words:…

Pinecone is a managed vector database for AI and machine learning applications. Learn to create indexes, upsert embeddings, query by similarity, use namespaces and metadata filtering for semantic search and RAG pipelines.

Polars is a blazingly fast DataFrame library written in Rust with a Python API. Learn eager and lazy evaluation, expressions, groupby, joins, and how Polars outperforms pandas for large datasets through parallel execution.

Assists with designing schemas, writing performant queries, managing indexes, and operating PostgreSQL databases. Use when working with JSONB, full-text search, window functions, CTEs, row-level security, replication, or performance tuning. Trigger words: postgresql, postgres,…

Expert guidance for product analytics, helping product teams define metrics, build funnels, analyze retention, run A/B tests, and make data-driven decisions. Applies frameworks for North Star metrics, pirate metrics (AARRR), cohort analysis, and experiment design.