Results for “snowflake”

Expert en data warehousing (Snowflake, BigQuery, Redshift, star schema, slowly changing dimensions)

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Star schema, snowflake schema, SCD types, and Kimball methodology for analytical data modeling. Activate on: dimensional model, star schema, snowflake schema, SCD, fact table, dimension table, Kimball, grain, surrogate key. NOT for: dbt implementation (use dbt-analytics-engineer), warehouse tuning (use data-warehouse-optimizer).

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Write complex SQL, improve slow queries, and design database schemas for transaction processing and analytics. Uses schema details and query execution plans to guide indexing, validate results, and assess performance with realistic data volumes.

• Investigate and improve slow analytical queries
• Design a database schema for a SaaS application
• Write customer retention and cohort analysis queries

Design and implement systems that collect, transform, validate, and store data for analysis. Covers scheduled and real-time processing, data warehouses, quality checks, monitoring, and cost tuning using tools such as Apache Spark, dbt, and Airflow.

• Building scheduled data collection and transformation workflows
• Designing a data warehouse for analysis
• Synchronizing database changes in real time

Create or improve a company-specific data analysis skill by gathering analysts’ knowledge and examining database structures. Document business terms, metric formulas, table relationships, and common query patterns in reusable reference files.

• Create a data analysis skill for a company
• Document metric formulas and standard exclusions
• Add knowledge about a new business domain

Design systems that collect, transform, and store data. Choose between scheduled and continuous processing, generate workflow configurations, validate data quality, and analyze performance using guidance for tools including Python, SQL, Spark, Airflow, dbt, and Kafka.

• Designing data ingestion and transformation pipelines
• Choosing scheduled or real-time data processing
• Checking data freshness, completeness, and uniqueness

Turn everyday questions into SQL queries, the commands used to retrieve and summarize database records. Uses your database structure and platform to produce queries with explanations, performance notes, and optional validation scripts.

• Extract users who meet specific activity conditions
• Build revenue reports by region and customer tier
• Create queries from database diagrams

Find the Wren CLI workflow for answering data questions, connecting databases or SaaS sources, generating model definitions, adding business metrics, or building a shareable analytics app. Guides are retrieved from the installed CLI version.

• Set up Wren and connect a database
• Load HubSpot or Stripe data through dlt
• Generate model definitions and add business metrics

Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sources are added as their own `references/<source>/` sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata DDL/SQL/stored procedures/macros/BTEQ to Redshift/RSQL, or assess Teradata-to-Redshift migration complexity. Applies only to migrations targeting Amazon Redshift; migrations to other platforms (Snowflake, BigQuery, Databricks, etc.) are out of scope regardless of source. Does not cover general Redshift administration, performance tuning, or troubleshooting of existing Redshift clusters (no migration involved), or sources not listed under references/.

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Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.

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Build cost-safe geospatial SQL for BigQuery, DuckDB, Snowflake, Wherobots, and Postgres and/or render results on an interactive Dekart map.

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Learn from public breach disclosures — extract the audit question each one implies and check your own stack. Capital One IMDS abuse, LastPass vault exfiltration, Okta Lapsus$, Snowflake credential reuse, MOVEit, SolarWinds, Equifax, Target POS, Codecov, Uber, Twilio — what would you check now if your boss said 'could that happen to us?' Use when the user mentions 'breach analysis,' 'lessons learned,' 'security postmortem,' 'breach patterns,' 'breach lessons,' 'has this happened to us,' 'apply breach lessons,' 'preempt breaches,' 'security retrospective,' 'real-world security incidents,' or wants to harden against known attacker playbooks.

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This skill should be used when the user asks to "create a plot arc", "story structure", "add a plot point", "story timeline", "track foreshadowing", "pacing", "sagging middle", "act structure", "story arc", "plot outline", "snowflake method", or wants to plan and manage the narrative structure of a story. It owns book-level pacing; NOT for the craft of scene outcomes or chapter hooks, which this skill only plans in the outline (use scene-craft), or a pacing check as a revision pass (use revision-continuity).

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Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

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dlt (data load tool) is an open-source Python library that loads data from APIs, files and databases into warehouses and lakes, inferring the schema and tracking incremental state for you. Use when a user asks to build a data pipeline in Python, load a REST API into DuckDB, BigQuery, Snowflake or Postgres, set up incremental or merge loading, enforce a schema contract, or replace a hand-written ETL script or a hosted ELT connector.

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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.

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Transition from static LLM chats to autonomous agents that execute multi-step tasks. Use this when you need to automate cross-platform reports (e.g., Snowflake to Google Docs), build self-service tools for non-technical teams, or create "anticipatory" engineering workflows that draft PRs based on Slack discussions.

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Expert FinOps guidance for cloud, AI, and SaaS technology spend. Covers AI cost management, agentic FinOps, GenAI capacity planning, self-hosted vs managed inference, Anthropic billing, open-weight vendor APIs (DeepSeek, Qwen, Kimi, GLM), AWS (EC2, Bedrock, SageMaker, Savings Plans, CUR), Azure (reservations, AHB, OpenAI PTUs), GCP (Vertex AI, BigQuery), OCI, Kubernetes cost allocation, data platforms (Databricks, Fabric F-SKUs, Snowflake), tagging governance, SaaS management (SAM, shadow IT), AI coding tools (Cursor, Claude Code, Copilot, Codex), ITAM, allocation, showback, chargeback, anomaly management, KPIs and benchmarking, waste detection playbooks, workload onboarding and M&A, and GreenOps (cloud carbon, CSRD). Use for any question about technology cost or waste in your estate - expiring commitments or RIs, NAT gateway bills, idle VMs, snapshot sprawl - and for commitment sizing, rightsizing, allocation, SaaS sprawl, AI dev tool spend, or connecting spend to business value. Built by OptimNow.

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Use this skill when configuring Data Manager in CRM Analytics: enabling objects for sync, scheduling data sync runs, setting up remote connections to external databases, monitoring sync status and error logs, or troubleshooting connected object issues. Trigger keywords: data sync, connected objects, Data Manager, sync schedule, remote connection, Snowflake connector, sync error, incremental sync, CRM Analytics data pipeline. NOT for pushing external data in programmatically via the External Data API — use data/analytics-external-data. NOT for authoring dataflow nodes — use admin/analytics-dataflow-development.

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Writes and reviews dbt projects targeting Snowflake: models, schema.yml metadata, sources, tests, macros, and project structure. Use when building or reviewing dbt models, generating dbt YAML, or applying dbt best practices.

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Snowflake, BigQuery, Redshift, dimensional modeling, and modern data warehouse architecture

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Use when the user asks about revenue, payments, subscriptions, billing, CRM deals, support tickets, production database tables, or other data that PostHog does not collect natively. Also use when a query fails because a table does not exist or returns no results for expected external data. The data warehouse can import from SaaS tools (Stripe, Hubspot, etc.), production databases (Postgres, MySQL, BigQuery, Snowflake), and other arbitrary data sources. Covers checking existing sources, identifying the right source type, and guiding the setup.

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Guide the user through connecting a new data warehouse source — Postgres, MySQL, Stripe, Hubspot, MongoDB, Salesforce, BigQuery, Snowflake, and so on. Use when the user wants to "connect Stripe", "import data from Postgres", "add a new data source", "sync my warehouse tables", or wants to pick sync methods for each table. Walks through source-type discovery, credential validation, table discovery, per-table sync_type selection, and the final create call. Also covers picking a good prefix and what to do right after creation.

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Static-to-EDS conversion that preserves the original design while making content authorable in Document Authoring. Two modes — page-level (overlay template with slot markers) and block-level (each section becomes an independent EDS block). Use when converting an AI-generated static HTML page (Stardust, Mobirise, Relume, Lovable, v0, Figma-derived hand-coded, etc.) into an Edge Delivery Services page. Triggers on "convert this page to EDS", "static-to-EDS overlay", "convert to EDS blocks", "next experimentation", "next run", "start run", or when a user provides a source URL and asks to make it editable in DA while keeping the original design intact. Do NOT use for canonical EDS block-rewrite migrations — that's the page-import skill.

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