[{"data":1,"prerenderedAt":404},["ShallowReactive",2],{"categories-init":3,"stack-n8n-streamlit-pipeline":4},true,{"stack_id":5,"slug":6,"name":7,"tagline":8,"long_description":9,"key_features":10,"use_cases":17,"pros":23,"cons":28,"cover_image_url":32,"scores":33,"options":41,"additions":90,"option_groups":91,"multi_select_option_types":92,"tools_by_category":93,"related_stacks":304,"faqs":372,"pricing":388,"system_requirements":32,"experience_level":328,"project_type":329,"stack_type_slug":312,"stack_type_icon_url":313,"published_date":148,"last_updated_date":32,"seo_meta":400},187,"n8n-streamlit-pipeline","n8n + Streamlit Pipeline Dashboard","n8n collects and processes data into PostgreSQL, Streamlit visualizes the pipeline output.","This stack combines n8n's automation capabilities with Streamlit's Python dashboard UI and PostgreSQL as the **shared data store**. n8n workflows collect data from external sources (APIs, webhooks, file uploads, scheduled fetches), process it, and write structured results to PostgreSQL. Streamlit reads those results and renders them as interactive charts and tables using Pandas and Python visualization libraries.\n\nThe **separation of concerns** is clean: n8n handles data ingestion and transformation (no Python required for most sources), PostgreSQL provides durable storage and queryable history, and Streamlit provides the visualization layer where data analysts can customize the display. This avoids building a custom ETL script for every data source while keeping the visualization in familiar Python.\n\nThis stack is ideal for data and operations teams who need a **lightweight data pipeline** with a custom dashboard, without committing to a full data warehouse platform.",[11,12,13,14,15,16],"n8n workflows ingest data from APIs, webhooks, and schedules without coding","PostgreSQL as the shared data store between n8n ingestion and Streamlit display","Streamlit Python UI with Pandas for data transformation and chart rendering","n8n conditional logic for data cleaning and routing before database write","Pandas data manipulation between PostgreSQL queries and Streamlit visualizations","Modular: add new n8n data source workflows without touching the Streamlit layer",[18,19,20,21,22],"Building a business metrics dashboard populated by n8n API polling workflows","Monitoring pipeline with n8n collecting data and Streamlit showing historical trends","Operations dashboard combining data from multiple external SaaS tools via n8n","Marketing analytics pipeline pulling ad spend and conversion data into a Streamlit report","Internal data quality monitoring dashboard fed by n8n validation workflows",[24,25,26,27],"n8n removes the need to write Python ingestion scripts for standard API sources","PostgreSQL provides queryable historical data for trend analysis","Streamlit gives full Python flexibility for custom visualizations","Stack is modular, and each layer can evolve independently",[29,30,31],"Three components add operational complexity compared to a single BI tool","n8n + PostgreSQL + Streamlit each need maintenance and monitoring","Not suitable for real-time sub-second dashboards; refresh latency is seconds to minutes",null,{"popularity":34,"learning_curve":36,"flexibility":37,"performance":39,"portability":40},{"score":35,"reasoning":32},3,{"score":35,"reasoning":32},{"score":38,"reasoning":32},4,{"score":35,"reasoning":32},{"score":38,"reasoning":32},{"database":42,"orm":46,"authentication":50,"analytics":54,"coding_agent":58,"llm":62,"language":66,"frontend_framework":70,"cms":74,"hosting":78,"reverse_proxy":82,"self_hosted_paas":86},{"tools":43,"descriptions":44,"aliases":45,"see_all":32},[],{},{},{"tools":47,"descriptions":48,"aliases":49,"see_all":32},[],{},{},{"tools":51,"descriptions":52,"aliases":53,"see_all":32},[],{},{},{"tools":55,"descriptions":56,"aliases":57,"see_all":32},[],{},{},{"tools":59,"descriptions":60,"aliases":61,"see_all":32},[],{},{},{"tools":63,"descriptions":64,"aliases":65,"see_all":32},[],{},{},{"tools":67,"descriptions":68,"aliases":69,"see_all":32},[],{},{},{"tools":71,"descriptions":72,"aliases":73,"see_all":32},[],{},{},{"tools":75,"descriptions":76,"aliases":77,"see_all":32},[],{},{},{"tools":79,"descriptions":80,"aliases":81,"see_all":32},[],{},{},{"tools":83,"descriptions":84,"aliases":85,"see_all":32},[],{},{},{"tools":87,"descriptions":88,"aliases":89,"see_all":32},[],{},{},{},{},[],{"Programming Languages":94,"Databases":149,"Automation & Integration":187,"Data & ML Libraries":229,"Data Apps":267},[95],{"tool_id":38,"name":96,"slug":97,"tooltip_description":98,"logo_url":99,"logo_bg":100,"pricing_model":101,"learning_curve_score":105,"popularity_score":106,"hosting_assignment_type":32,"hosting_provider_restriction":107,"hosting_target_restriction":107,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":108,"subcategory":32,"categories":111,"subcategories":114,"flexibility_score":106,"performance_score":35,"portability_score":106,"is_featured":3,"tags":115,"score_reasonings":141,"published_date":147,"last_updated_date":148},"Python","python","Python is a high-level, interpreted, dynamically typed programming language emphasising readability and simplicity. It dominates data science, machine learning, and general-purpose scripting.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fpython.svg","dark",{"slug":102,"display_name":103,"description":104},"open_source","Open Source","Source code is publicly available and free to use, modify, and distribute. No paid plans from the project itself.",2,5,"open",{"category_id":35,"name":109,"slug":110},"Programming Languages","programming-languages",[112],{"category_id":35,"name":109,"slug":110,"is_primary":3,"display_order":113},0,[],[116,119,123,128,132,137],{"tag_id":117,"name":96,"slug":97,"tag_type":118},1,"technology",{"tag_id":120,"name":103,"slug":121,"tag_type":122},11,"open-source","feature",{"tag_id":124,"name":125,"slug":126,"tag_type":127},25,"Machine Learning","machine-learning","use_case",{"tag_id":129,"name":130,"slug":131,"tag_type":127},39,"Data Science","data-science",{"tag_id":133,"name":134,"slug":135,"tag_type":136},48,"Functional","functional","paradigm",{"tag_id":138,"name":139,"slug":140,"tag_type":136},49,"Object-oriented","object-oriented",{"learning_curve":142,"flexibility":143,"performance":144,"popularity":145,"portability":146},"Clean, readable syntax with vast learning resources; beginner-friendly from day one.","No constraints; equally suited to scripting, data science, web servers, and systems programming.","Interpreted and GIL-limited; efficient for I\u002FO-bound work but slow for CPU-intensive tasks.","The most widely used programming language globally; dominant in data science, AI, and automation.","Universal language; skills transfer across every domain and environment.","2026-05-29","2026-09-27",[150],{"tool_id":151,"name":152,"slug":153,"tooltip_description":154,"logo_url":155,"logo_bg":100,"pricing_model":156,"learning_curve_score":35,"popularity_score":106,"hosting_assignment_type":157,"hosting_provider_restriction":107,"hosting_target_restriction":107,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":158,"subcategory":161,"categories":164,"subcategories":166,"flexibility_score":106,"performance_score":38,"portability_score":106,"is_featured":3,"tags":168,"score_reasonings":181,"published_date":147,"last_updated_date":148},40,"PostgreSQL","postgresql","PostgreSQL is a free, open-source object-relational database known for reliability, standards compliance, and extensibility, with rich data types, JSONB, and a large ecosystem of extensions such as PostGIS and pgvector.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fpostgresql.svg",{"slug":102,"display_name":103,"description":104},"deployable",{"category_id":38,"name":159,"slug":160},"Databases","databases",{"subcategory_id":124,"name":162,"slug":163},"OLTP Databases","oltp-databases",[165],{"category_id":38,"name":159,"slug":160,"is_primary":3,"display_order":113},[167],{"subcategory_id":124,"name":162,"slug":163,"category_id":38,"is_primary":3,"display_order":113},[169,172,173,177],{"tag_id":38,"name":170,"slug":171,"tag_type":118},"SQL","sql",{"tag_id":120,"name":103,"slug":121,"tag_type":122},{"tag_id":174,"name":175,"slug":176,"tag_type":122},12,"Self-hostable","self-hostable",{"tag_id":178,"name":179,"slug":180,"tag_type":122},22,"ACID Compliant","acid-compliant",{"learning_curve":182,"flexibility":183,"performance":184,"portability":185,"popularity":186},"Standard SQL is broadly known, but CTEs, window functions, and JSONB take time to master.","Extensions like PostGIS and pgvector, custom types, and PL\u002FpgSQL cover virtually any need.","Battle-tested query planner; efficient with proper indexing and regular vacuum.","Open SQL standard with no lock-in; data and skills transfer to any SQL-compatible system.","The most popular relational database among developers per Stack Overflow 2024; rapidly growing.",[188],{"tool_id":178,"name":189,"slug":189,"tooltip_description":190,"logo_url":191,"logo_bg":100,"pricing_model":192,"learning_curve_score":35,"popularity_score":38,"hosting_assignment_type":196,"hosting_provider_restriction":107,"hosting_target_restriction":107,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":197,"subcategory":32,"categories":201,"subcategories":203,"flexibility_score":106,"performance_score":38,"portability_score":38,"is_featured":3,"tags":204,"score_reasonings":223,"published_date":147,"last_updated_date":148},"n8n","Fair-code workflow automation tool with a visual node-based editor, 500+ integrations, and code nodes. Self-hostable alternative to Zapier and Make, also offered as n8n Cloud.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fn8n.svg",{"slug":193,"display_name":194,"description":195},"freemium","Freemium","A free tier is available; additional features, usage limits, or managed hosting require a paid plan.","self_hostable",{"category_id":198,"name":199,"slug":200},10,"Automation & Integration","automation-integration",[202],{"category_id":198,"name":199,"slug":200,"is_primary":3,"display_order":113},[],[205,206,207,211,215,219],{"tag_id":120,"name":103,"slug":121,"tag_type":122},{"tag_id":174,"name":175,"slug":176,"tag_type":122},{"tag_id":208,"name":209,"slug":210,"tag_type":122},13,"Free Tier","free-tier",{"tag_id":212,"name":213,"slug":214,"tag_type":122},17,"No-code","no-code",{"tag_id":216,"name":217,"slug":218,"tag_type":127},33,"Workflow Automation","workflow-automation",{"tag_id":220,"name":221,"slug":222,"tag_type":136},44,"Event-driven","event-driven",{"learning_curve":224,"performance":225,"flexibility":226,"portability":227,"popularity":228},"Visual workflow canvas is intuitive; advanced node expressions and APIs take more time.","Workflow execution is efficient; throughput scales with self-hosted resources.","Custom nodes via code editor, self-hostable, and API-accessible for any workflow imaginable.","Open source; JSON workflow export and self-hosting make migration feasible.","Very popular among technical users for self-hosted automation; large open-source community.",[230],{"tool_id":231,"name":232,"slug":233,"tooltip_description":234,"logo_url":235,"logo_bg":236,"pricing_model":237,"learning_curve_score":105,"popularity_score":35,"hosting_assignment_type":238,"hosting_provider_restriction":107,"hosting_target_restriction":107,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":239,"subcategory":243,"categories":247,"subcategories":249,"flexibility_score":35,"performance_score":35,"portability_score":38,"is_featured":251,"tags":252,"score_reasonings":261,"published_date":147,"last_updated_date":148},8,"Pandas","pandas","The standard Python library for tabular data manipulation and analysis, built around the DataFrame and Series and used across data science, analytics, and machine learning work.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fpandas.svg","white",{"slug":102,"display_name":103,"description":104},"library",{"category_id":240,"name":241,"slug":242},14,"Data & ML Libraries","data-ml-libraries",{"subcategory_id":244,"name":245,"slug":246},43,"Data Processing","data-processing",[248],{"category_id":240,"name":241,"slug":242,"is_primary":3,"display_order":113},[250],{"subcategory_id":244,"name":245,"slug":246,"category_id":240,"is_primary":3,"display_order":113},false,[253,254,255,256,260],{"tag_id":117,"name":96,"slug":97,"tag_type":118},{"tag_id":120,"name":103,"slug":121,"tag_type":122},{"tag_id":124,"name":125,"slug":126,"tag_type":127},{"tag_id":257,"name":258,"slug":259,"tag_type":127},27,"Data Engineering","data-engineering",{"tag_id":129,"name":130,"slug":131,"tag_type":127},{"learning_curve":262,"flexibility":263,"performance":264,"portability":265,"popularity":266},"Intuitive DataFrame API; tabular data manipulation becomes second nature quickly.","Excellent for tabular data but not designed for streaming, graph, or out-of-core processing.","Efficient for datasets that fit in RAM; slower than Polars for large-scale transformations.","DataFrame concept transfers well; Polars API is similar enough for easy migration.","The standard DataFrame library for Python data science; used widely in academia and industry.",[268],{"tool_id":106,"name":269,"slug":270,"tooltip_description":271,"logo_url":272,"logo_bg":100,"pricing_model":273,"learning_curve_score":105,"popularity_score":35,"hosting_assignment_type":196,"hosting_provider_restriction":107,"hosting_target_restriction":107,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":274,"subcategory":32,"categories":278,"subcategories":280,"flexibility_score":35,"performance_score":105,"portability_score":35,"is_featured":251,"tags":281,"score_reasonings":298,"published_date":147,"last_updated_date":148},"Streamlit","streamlit","Open-source Python framework for turning data scripts into interactive web apps in a few lines, with no frontend code. Part of Snowflake since 2022.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fstreamlit.svg",{"slug":193,"display_name":194,"description":195},{"category_id":275,"name":276,"slug":277},15,"Data Apps","data-apps",[279],{"category_id":275,"name":276,"slug":277,"is_primary":3,"display_order":113},[],[282,283,284,285,289,293,297],{"tag_id":117,"name":96,"slug":97,"tag_type":118},{"tag_id":120,"name":103,"slug":121,"tag_type":122},{"tag_id":208,"name":209,"slug":210,"tag_type":122},{"tag_id":286,"name":287,"slug":288,"tag_type":127},26,"Data Visualization","data-visualization",{"tag_id":290,"name":291,"slug":292,"tag_type":127},28,"Web Development","web-development",{"tag_id":294,"name":295,"slug":296,"tag_type":127},38,"Dashboards","dashboards",{"tag_id":129,"name":130,"slug":131,"tag_type":127},{"learning_curve":299,"flexibility":300,"performance":301,"popularity":302,"portability":303},"Minimal Python API; a working web app runs in under 20 lines of code.","Simple script model is easy but constrains layout, state management, and navigation.","Reruns the entire script on each interaction; not suited for high-frequency or real-time workloads.","Popular within the data science community; less known outside ML and analytics circles.","Script model is unique; switching to Gradio or Dash requires a full rewrite.",[305,324,335,357],{"stack_id":306,"slug":307,"name":308,"tagline":309,"experience_level":310,"project_type":311,"stack_type_slug":312,"stack_type_icon_url":313,"score_popularity":35,"score_learning_curve":35,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":314},186,"n8n-streamlit-ai","n8n + Streamlit AI Agent","n8n orchestrates AI workflows surfaced as an interactive Streamlit dashboard.","intermediate","automation","project","https:\u002F\u002Fassets.tekyous.dev\u002Ficons\u002Fstack-types\u002Fproject.svg",[315,316,317,318,319],{"tool_id":38,"slug":97,"name":96,"logo_url":99,"logo_bg":100},{"tool_id":151,"slug":153,"name":152,"logo_url":155,"logo_bg":100},{"tool_id":178,"slug":189,"name":189,"logo_url":191,"logo_bg":100},{"tool_id":106,"slug":270,"name":269,"logo_url":272,"logo_bg":100},{"tool_id":320,"slug":321,"name":322,"logo_url":323,"logo_bg":100},153,"claude","Claude","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fclaude.svg",{"stack_id":38,"slug":325,"name":326,"tagline":327,"experience_level":328,"project_type":329,"stack_type_slug":312,"stack_type_icon_url":313,"score_popularity":106,"score_learning_curve":105,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":330},"python-dashboard-starter","Python Dashboard Starter","Interactive Streamlit dashboard with pandas analytics and a PostgreSQL backend.","beginner","dashboard",[331,332,333,334],{"tool_id":38,"slug":97,"name":96,"logo_url":99,"logo_bg":100},{"tool_id":151,"slug":153,"name":152,"logo_url":155,"logo_bg":100},{"tool_id":231,"slug":233,"name":232,"logo_url":235,"logo_bg":236},{"tool_id":106,"slug":270,"name":269,"logo_url":272,"logo_bg":100},{"stack_id":220,"slug":336,"name":337,"tagline":338,"experience_level":310,"project_type":339,"stack_type_slug":312,"stack_type_icon_url":313,"score_popularity":105,"score_learning_curve":35,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":340},"svelte-fastapi","Svelte + FastAPI","Svelte SPA frontend with FastAPI backend: minimal JavaScript output meets Python API performance.","web_app",[341,346,350,351,356],{"tool_id":342,"slug":343,"name":344,"logo_url":345,"logo_bg":100},20,"svelte","Svelte","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fsvelte.svg",{"tool_id":240,"slug":347,"name":348,"logo_url":349,"logo_bg":100},"fastapi","FastAPI","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Ffastapi.svg",{"tool_id":38,"slug":97,"name":96,"logo_url":99,"logo_bg":100},{"tool_id":352,"slug":353,"name":354,"logo_url":355,"logo_bg":100},103,"typescript","TypeScript","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Ftypescript.svg",{"tool_id":151,"slug":153,"name":152,"logo_url":155,"logo_bg":100},{"stack_id":358,"slug":359,"name":360,"tagline":361,"experience_level":328,"project_type":311,"stack_type_slug":362,"stack_type_icon_url":363,"score_popularity":38,"score_learning_curve":105,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":364},194,"n8n-quick-deploy","n8n Quick Deploy","Deploy n8n in one click from a VPS marketplace template or a container-platform template, without assembling Docker, a proxy, or TLS yourself.","infrastructure","https:\u002F\u002Fassets.tekyous.dev\u002Ficons\u002Fstack-types\u002Finfrastructure.svg",[365,366,367],{"tool_id":151,"slug":153,"name":152,"logo_url":155,"logo_bg":100},{"tool_id":178,"slug":189,"name":189,"logo_url":191,"logo_bg":100},{"tool_id":368,"slug":369,"name":370,"logo_url":371,"logo_bg":100},72,"docker","Docker","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fdocker.svg",[373,376,379,382,385],{"question":374,"answer":375},"How is this different from the n8n + Streamlit AI Agent stack?","Same architecture, different direction of flow. Here data moves one way on a schedule: n8n fetches and transforms, PostgreSQL stores, Streamlit displays, and no user input ever enters the workflow. The AI Agent stack inverts that: a user action in Streamlit triggers an n8n workflow through a webhook and an LLM processes the input before the result returns to the screen. That difference sets the failure modes and the bill: this stack worries about ingestion schedules and refresh cadence with essentially zero marginal cost, while the AI stack worries about webhook latency and per-token spend. If the dashboard answers known questions from collected data, use this one; if it responds to what users type, use the other.",{"question":377,"answer":378},"What does it cost to run?","Effectively the price of one small server. Every component is free open source: n8n self-hosted, Streamlit, Pandas, Python, and PostgreSQL carry no license cost, so a $5 to $10 a month VPS runs the whole pipeline for a small team, with no per-seat or per-row charges anywhere. The managed alternatives: n8n's cloud plans start at $20 a month billed yearly (2,500 executions) and Streamlit's Community Cloud hosts the dashboard free, which removes the server entirely for light use. That flat cost is the stack's main advantage over SaaS dashboard and ETL tools, which bill per connector, per row, or per seat as volume grows.",{"question":380,"answer":381},"Why not just use a BI tool like Metabase over the same Postgres?","If the dashboard's questions are known and the transformations are simple, a BI tool genuinely is the faster choice: connect Metabase to the same PostgreSQL and non-developers build charts without Python. This stack earns its extra moving parts when the pipeline or the visualization needs logic BI tools cannot express: an n8n workflow that cleans, enriches, and cross-references API data before it ever reaches the database, or a Streamlit page that runs a Pandas transformation, a statistical calculation, or a custom chart a BI query builder cannot produce. A practical hybrid: keep n8n for ingestion regardless, and choose Streamlit only when the display layer outgrows drag-and-drop charts.",{"question":383,"answer":384},"How do I control how fresh the dashboard data is?","Freshness has two dials, and they are independent. Ingestion cadence is set in n8n: each source workflow runs on its own schedule (every five minutes, hourly, nightly), which bounds how current the PostgreSQL tables can ever be. Read freshness is effectively real time: every Streamlit rerun queries PostgreSQL directly, so the dashboard always reflects whatever the last ingestion wrote. Between the two sits caching: Streamlit's cache_data decorator with a time-to-live keeps heavy queries from re-running on every widget interaction. For sources that push instead of polling, an n8n webhook trigger makes ingestion event-driven and the schedule dial disappears. Sub-second end-to-end freshness is not this stack's shape; seconds-to-minutes is.",{"question":386,"answer":387},"Can this grow into a full data platform later?","Yes, and the pieces graduate separately. The typical path: PostgreSQL fills up, and either it moves to a managed instance or the history outgrows it and graduates to a warehouse, at which point the catalog's ELT stacks (Airbyte, dbt, and a warehouse like Snowflake) take over the heavy transformation work. n8n remains viable much longer than teams expect: scheduled ingestion of API sources keeps working well into the tens of thousands of rows a day, and its replacement, when needed, is a orchestrator like Airflow rather than a rewrite of the sources. Streamlit never needs replacing for an internal audience. The realistic inflection point is when transformations need dbt-style versioned SQL rather than visual n8n steps; that is the layer to move first.",{"summary":389,"starting_cost_label":390,"has_free_tier":3,"line_items":391},"Everything in the stack is free open source: n8n self-hosted, Streamlit, Pandas, Python, and PostgreSQL carry no license cost, so the only bill is the server it all runs on, $5 to $10 a month for a small team's pipeline. The managed alternatives: n8n's cloud plans start at $20 a month billed yearly (2,500 executions) with Pro at $50, and Streamlit's Community Cloud hosts apps free. There is no per-seat or per-row cost anywhere, which is the stack's main advantage over SaaS dashboard and ETL tools that bill by volume.","Free to start",[392,396],{"label":393,"cost":394,"note":395},"Orchestration (n8n)","Free–$50\u002Fmo","Self-hosted free with unlimited workflows; cloud plans from $20 (Starter, 2.5K executions) to $50 (Pro, 10K) a month, billed yearly.",{"label":397,"cost":398,"note":399},"Data + UI layer (PostgreSQL, Streamlit)","Free (open source)","PostgreSQL, Streamlit, Pandas, and Python are free; self-host on a $5 to $10 VPS, or use Streamlit's free Community Cloud for the dashboard.",{"title":401,"description":402,"og_image":32,"canonical":403},"n8n + Streamlit Pipeline Dashboard: Tools, Pricing & How to Deploy | Tekyous","n8n collects and processes data into PostgreSQL, Streamlit visualizes the… Compare n8n + Streamlit Pipeline Dashboard tools, pricing & how to deploy on Tekyous.","https:\u002F\u002Ftekyous.dev\u002Fstacks\u002Fn8n-streamlit-pipeline",1790518889156]