--- name: data-pipeline-db9ba0f6adb6 description: "Data pipeline and ETL automation - extract, transform, load workflows for data integration and analytics" version: "1.0.0" author: claude-office-skills license: MIT category: data tags: - etl - data-pipeline - integration - analytics - automation department: Data Engineering models: recommended: - claude-sonnet-4 mcp: server: data-mcp tools: - database_query - api_fetch - spreadsheet_write - file_transform capabilities: - data_extraction - transformation - loading - scheduling - monitoring languages: - en - zh related_skills: - sheets-automation - data-analysis - airtable-automation --- # Data Pipeline Build data pipelines and ETL workflows for data integration, transformation, and analytics automation. Based on n8n's data workflow templates. ## Overview This skill covers: - Data extraction from multiple sources - Transformation and cleaning - Loading to destinations - Scheduling and monitoring - Error handling and alerts --- ## ETL Patterns ### Basic ETL Flow ``` ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ EXTRACT │───▶│ TRANSFORM │───▶│ LOAD │ │ │ │ │ │ │ │ • APIs │ │ • Clean │ │ • Database │ │ • Databases │ │ • Map │ │ • Warehouse │ │ • Files │ │ • Aggregate │ │ • Files │ │ • Webhooks │ │ • Enrich │ │ • APIs │ └─────────────┘ └─────────────┘ └─────────────┘ ``` ### n8n ETL Workflow ```yaml workflow: "Daily Sales ETL" schedule: "2am daily" nodes: # EXTRACT - name: "Extract from Shopify" type: shopify action: get_orders filter: created_at >= yesterday - name: "Extract from Stripe" type: stripe action: get_payments filter: created >= yesterday # TRANSFORM - name: "Merge Data" type: merge mode: combine_by_key key: order_id - name: "Transform" type: code code: | return items.map(item => ({ date: item.created_at.split('T')[0], order_id: item.id, customer_email: item.email, total: parseFloat(item.total_price), currency: item.currency, items: item.line_items.length, source: item.source_name, payment_status: item.payment.status })); # LOAD - name: "Load to BigQuery" type: google_bigquery action: insert_rows table: sales_daily - name: "Update Google Sheets" type: google_sheets action: append_rows spreadsheet: "Daily Sales Report" ``` --- ## Data Sources ### Common Extractors ```yaml extractors: databases: - postgresql: connection: connection_string query: "SELECT * FROM orders WHERE date >= $1" - mysql: connection: connection_string query: custom_sql - mongodb: connection: connection_string collection: orders filter: {date: {$gte: yesterday}} apis: - rest_api: url: "https://api.example.com/data" method: GET headers: {Authorization: "Bearer {token}"} pagination: handle_automatically - graphql: url: "https://api.example.com/graphql" query: graphql_query files: - csv: source: sftp/s3/google_drive delimiter: "," encoding: utf-8 - excel: source: file_path sheet: "Sheet1" - json: source: api/file path: "data.items" saas: - salesforce: get_objects - hubspot: get_contacts/deals - stripe: get_charges - shopify: get_orders ``` --- ## Transformations ### Common Transformations ```yaml transformations: cleaning: - remove_nulls: drop_or_fill - trim_whitespace: all_string_fields - deduplicate: by_key - validate: against_schema mapping: - rename_fields: {old_name: new_name} - convert_types: {date_string: date} - map_values: {status_code: status_name} aggregation: - group_by: [date, category] - sum: [revenue, quantity] - count: orders - average: order_value enrichment: - lookup: from_reference_table - geocode: from_address - calculate: derived_fields filtering: - where: condition - limit: n_rows - sample: percentage ``` ### Code Transform Examples ```javascript // Clean and normalize data function transform(items) { return items.map(item => ({ // Clean strings name: item.name?.trim().toLowerCase(), // Parse dates date: new Date(item.created_at).toISOString().split('T')[0], // Convert types amount: parseFloat(item.amount) || 0, // Map values status: statusMap[item.status_code] || 'unknown', // Calculate fields total: item.quantity * item.unit_price, // Filter nested tags: item.tags?.filter(t => t.active).map(t => t.name), // Default values source: item.source || 'direct' })); } // Aggregate data function aggregate(items) { const grouped = {}; items.forEach(item => { const key = `${item.date}_${item.category}`; if (!grouped[key]) { grouped[key] = { date: item.date, category: item.category, total_revenue: 0, order_count: 0 }; } grouped[key].total_revenue += item.amount; grouped[key].order_count += 1; }); return Object.values(grouped); } ``` --- ## Data Destinations ### Common Loaders ```yaml loaders: data_warehouses: - bigquery: project: project_id dataset: analytics table: sales write_mode: append/truncate - snowflake: account: account_id warehouse: compute_wh database: analytics schema: public - redshift: cluster: cluster_id database: analytics databases: - postgresql: upsert: on_conflict_update - mysql: batch_insert: 1000_rows files: - s3: bucket: data-lake path: /processed/{date}/ format: parquet - google_cloud_storage: bucket: data-bucket spreadsheets: - google_sheets: mode: append/overwrite - airtable: base: base_id table: table_name apis: - webhook: url: destination_url batch_size: 100 ``` --- ## Scheduling & Monitoring ### Pipeline Scheduling ```yaml scheduling: patterns: hourly: cron: "0 * * * *" use_for: real_time_dashboards daily: cron: "0 2 * * *" use_for: daily_reports weekly: cron: "0 3 * * 1" use_for: weekly_summaries on_demand: trigger: webhook/manual use_for: ad_hoc_analysis dependencies: - pipeline_a: must_complete_before pipeline_b - wait_for: all_extracts_complete retries: max_attempts: 3 delay: exponential_backoff alert_on: final_failure ``` ### Monitoring & Alerts ```yaml monitoring: metrics: - rows_processed - execution_time - error_count - data_freshness alerts: pipeline_failed: channels: [slack, pagerduty] template: | 🚨 *Pipeline Failed* Pipeline: {pipeline_name} Stage: {failed_stage} Error: {error_message} [View Logs]({logs_url}) data_quality: trigger: anomaly_detected conditions: - row_count: differs_by > 50% - null_rate: exceeds_threshold - schema: changed_unexpectedly stale_data: trigger: last_update > threshold threshold: 2_hours ``` --- ## Data Quality ### Quality Checks ```yaml data_quality: schema_validation: - required_fields: [id, date, amount] - field_types: id: integer date: date amount: number - allowed_values: status: [active, pending, closed] statistical_checks: - null_rate: < 5% - duplicate_rate: < 1% - value_range: amount: [0, 1000000] business_rules: - total_equals_sum_of_line_items - dates_are_not_in_future - email_format_valid trend_analysis: - row_count: within_2_std_of_mean - total_value: within_expected_range ``` --- ## Output Example **Request**: "Create a daily sales data pipeline" **Output**: ```markdown # Daily Sales Data Pipeline ## Pipeline Overview ``` Shopify + Stripe → Transform → BigQuery + Sheets ``` ## Schedule - Runs: 2am daily - Timezone: UTC - Retry: 3 attempts ## Extract ### Shopify Orders ```yaml source: shopify filter: created_at >= yesterday fields: [id, email, total_price, line_items, created_at] ``` ### Stripe Payments ```yaml source: stripe filter: created >= yesterday fields: [id, amount, status, metadata.order_id] ``` ## Transform ```javascript // Join and clean data { date: order.created_at.split('T')[0], order_id: order.id, customer: order.email, revenue: parseFloat(order.total_price), items: order.line_items.length, payment_status: payment.status } ``` ## Load ### BigQuery - Table: `analytics.sales_daily` - Mode: Append ### Google Sheets - Sheet: "Daily Sales Dashboard" - Tab: "Raw Data" ## Quality Checks - [ ] Row count > 0 - [ ] No null order_ids - [ ] Revenue sum matches Stripe ## Alerts - Slack: #data-alerts - On failure: @data-team ``` --- *Data Pipeline Skill - Part of Claude Office Skills* --- Source attribution: claude-office-skills Original project: https://github.com/diegosouzapw/awesome-omni-skill/blob/a6b3c3005cedf056a48b084671c6282426d6e9fc/skills/data-ai/data-pipeline/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; 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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: claude-office-skills Original project: https://github.com/diegosouzapw/awesome-omni-skill/blob/a6b3c3005cedf056a48b084671c6282426d6e9fc/skills/data-ai/data-pipeline/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Data & Analytics, Marketing Roles: Data Engineer, Data Analyst, Marketing Analyst Specialties: API, Database Tools: n8n, Google Sheets, Salesforce, Stripe, HubSpot, PostgreSQL, MySQL, MongoDB Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: b0261e15c0b1e0ea388c8db781c0467462d6a82e41b9401a0de7112f48b2c905