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How dlt’s scores are calculated

The full audit trail, recomputed from the verdict data at build time through the same code that produced the leaderboard: verdict × quality × story weight per cell, cells sum to dimension scores, dimensions blend into the PA Score. Every number on the product page is reproducible from this page alone; for why the formula looks like this, see the methodology.

verdict factors: full ×1.0 · partial ×0.6 · disputed ×0.3 · none ×0.0 · n/a excluded from both sides · cell points = weight × quality × factor · cell max = weight × 10

PA Score47/100

Agent-ready 66.1 × 0.30 = 19.83

API quality 10.3 × 0.20 = 2.06

Openness 78.0 × 0.20 = 15.60

Built-in AI 24.9 × 0.15 = 3.73

Automation 37.8 × 0.15 = 5.67

(19.83 + 2.06 + 15.60 + 3.73 + 5.67) ÷ (0.30 + 0.20 + 0.20 + 0.15 + 0.15) = 46.89 ÷ 1.00 = 46.9

Scores are stored to 1 decimal; the product page’s pills round to whole numbers for display. Each dimension below shows the stories, verdicts, and cited evidence behind its number.

Agent-ready66.1/100×0.30 of the PA blend

Outside-in: can YOUR agent reach and drive this product — API, MCP, CLI, headless runs, agent docs.

Point an agent at llms.txt or agent-oriented docsweight 2

2 (weight) × 9 (quality) × 1.0 (full) = 18.0 of 20 max

  • [probe] https://dlthub.com/llms.txt“PROBE llms.txt: HTTP 200 at https://dlthub.com/llms.txt # dlt — data load tool & dltHub > dlt is the open-source Python library for moving data from any source to any destinat”
  • [probe] https://dlthub.com/docs/intro.md“PROBE docs-md: HTTP 200 at https://dlthub.com/docs/intro.md --- title: Introduction description: Introduction to dlt keywords: [introduction, who, what, how] --- # Getting started”
  • [claimed-docs] https://dlthub.com/docs/hub/ai-harness/introduction“The dltHub AI Harness is a set of skills, rules, and MCP servers that teach a general-purpose coding agent (Claude Code, Cursor, or Codex) how to build production-grade pipelines, deploy and run them on dltHub managed infrastructure.”
  • [claimed-docs] https://dlthub.com/context“we turn AI assistants into expert dlt pipeline developers across over 11,200 REST API data sources.”

Run the product headlessly / in CI for automationweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://dlthub.com/docs/intro“dlt can be deployed anywhere Python runs, be it on Airflow, serverless functions”
  • [github] https://github.com/dlt-hub/dlt“Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop, or an AI coding agent—dlt can be dropped in anywhere.”
  • [claimed-docs] https://dlthub.com/docs/reference/command-line-interface“Creates, adds, inspects and deploys dlt pipelines.”
  • [claimed-docs] https://dlthub.com/docs/tutorial/load-data-from-an-api“pipeline = dlt.pipeline( pipeline_name="quick_start", destination="duckdb", dataset_name="mydata")load_info = pipeline.run(data, table_name="users")”
  • [probe] https://dlthub.com/docs/reference/command-line-interface“PROBE runtime (recorded 2026-09-08): a REAL `dlt init chess duckdb` scaffold ran keylessly in a throwaway fixture — it fetched the verified source, wrote chess_pipeline.py with runnable pipeline code, a .dlt/secrets.toml template, and requirements.txt ('Verified source chess was added to your project!'). Self-cleaned.”
  • [probe] https://dlthub.com/docs/reference/installation“PROBE runtime (recorded 2026-09-08): the dlt CLI installed keylessly from pypi via uvx and printed 'dlt 1.30.0'.”
  • [claimed-docs] https://dlthub.com/docs/walkthroughs/deploy-a-pipeline“Deploy your pipelines with a single `dlthub deploy` command. Schedule, refresh, backfill, and observe runs with a familiar decorator-based Python API.”

Plug MCP servers into this product so it can use their toolsweight 3

n/a — not applicable to this product: excluded from numerator and denominator

  • [probe] https://github.com/dlt-hub/dlt-mcp“official MCP server documented at https://github.com/dlt-hub/dlt-mcp”
  • [probe] https://github.com/dlt-hub/dlt-mcp“PROBE runtime (recorded 2026-09-08): dltHub's official MCP server (pypi dlt-mcp, published by dltHub) completed a FULL keyless stdio initialize handshake via `uv run --with dlt-mcp[duckdb] dlt-mcp` — serverInfo {name: 'dlt MCP', version: 4.0.3}, instructions 'Helps you build with the dlt Python library', with tools, prompts, and resources capabilities.”
  • [claimed-docs] https://dlthub.com/docs/hub/ai-harness/introduction“The dltHub AI Harness is a set of skills, rules, and MCP servers that teach a general-purpose coding agent (Claude Code, Cursor, or Codex) how to build production-grade pipelines, deploy and run them on dltHub managed infrastructure.”

Connect an agent via an official MCP serverweight 3

3 (weight) × 9 (quality) × 1.0 (full) = 27.0 of 30 max

  • [probe] https://github.com/dlt-hub/dlt-mcp“official MCP server documented at https://github.com/dlt-hub/dlt-mcp”
  • [probe] https://github.com/dlt-hub/dlt-mcp“PROBE runtime (recorded 2026-09-08): dltHub's official MCP server (pypi dlt-mcp, published by dltHub) completed a FULL keyless stdio initialize handshake via `uv run --with dlt-mcp[duckdb] dlt-mcp` — serverInfo {name: 'dlt MCP', version: 4.0.3}, instructions 'Helps you build with the dlt Python library', with tools, prompts, and resources capabilities.”
  • [claimed-docs] https://dlthub.com/docs/hub/ai-harness/introduction“The dltHub AI Harness is a set of skills, rules, and MCP servers that teach a general-purpose coding agent (Claude Code, Cursor, or Codex) how to build production-grade pipelines, deploy and run them on dltHub managed infrastructure.”
  • [claimed-docs] https://dlthub.com/context“we turn AI assistants into expert dlt pipeline developers across over 11,200 REST API data sources.”

Use an official CLIweight 2

2 (weight) × 9 (quality) × 1.0 (full) = 18.0 of 20 max

  • [claimed-docs] https://dlthub.com/docs/reference/command-line-interface“Creates, adds, inspects and deploys dlt pipelines.”
  • [probe] https://dlthub.com/docs/reference/command-line-interface“official CLI documented at https://dlthub.com/docs/reference/command-line-interface”
  • [probe] https://dlthub.com/docs/reference/command-line-interface“PROBE runtime (recorded 2026-09-08): a REAL `dlt init chess duckdb` scaffold ran keylessly in a throwaway fixture — it fetched the verified source, wrote chess_pipeline.py with runnable pipeline code, a .dlt/secrets.toml template, and requirements.txt ('Verified source chess was added to your project!'). Self-cleaned.”
  • [probe] https://dlthub.com/docs/reference/installation“PROBE runtime (recorded 2026-09-08): the dlt CLI installed keylessly from pypi via uvx and printed 'dlt 1.30.0'.”
  • [claimed-docs] https://dlthub.com/docs/hub/ai-harness/introduction“The dltHub AI Harness is a set of skills, rules, and MCP servers that teach a general-purpose coding agent (Claude Code, Cursor, or Codex) how to build production-grade pipelines, deploy and run them on dltHub managed infrastructure.”

Drive the product through a documented public APIweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

  • [claimed-docs] https://dlthub.com/docs/tutorial/load-data-from-an-api“pipeline = dlt.pipeline( pipeline_name="quick_start", destination="duckdb", dataset_name="mydata")load_info = pipeline.run(data, table_name="users")”
  • [claimed-docs] https://dlthub.com/docs/general-usage/dataset-access/dataset“use `pipeline.dataset()` to query the data. You can build the query with data frame expressions, Ibis, or SQL.”
  • [claimed-docs] https://dlthub.com/docs/reference/command-line-interface“Creates, adds, inspects and deploys dlt pipelines.”
  • [community] https://hn.algolia.com/api/v1/items/45712393“One of the reasons why I like dlt is because I can do everything via code, which makes things more maintainable, for me.”
  • [probe] https://dlthub.com/docs/reference/command-line-interface“PROBE runtime (recorded 2026-09-08): a REAL `dlt init chess duckdb` scaffold ran keylessly in a throwaway fixture — it fetched the verified source, wrote chess_pipeline.py with runnable pipeline code, a .dlt/secrets.toml template, and requirements.txt ('Verified source chess was added to your project!'). Self-cleaned.”
  • [probe] https://dlthub.com/docs/reference/installation“PROBE runtime (recorded 2026-09-08): the dlt CLI installed keylessly from pypi via uvx and printed 'dlt 1.30.0'.”
  • [probe] https://dlthub.com/openapi.json“PROBE openapi: all candidate paths 404 (https://dlthub.com/openapi.json, https://dlthub.com/swagger.json, https://dlthub.com/api/openapi.json, https://dlthub.com/.well-known/openapi.json)”
  • [probe] https://github.com/dlt-hub/dlt-mcp“official MCP server documented at https://github.com/dlt-hub/dlt-mcp”
  • [probe] https://github.com/dlt-hub/dlt-mcp“PROBE runtime (recorded 2026-09-08): dltHub's official MCP server (pypi dlt-mcp, published by dltHub) completed a FULL keyless stdio initialize handshake via `uv run --with dlt-mcp[duckdb] dlt-mcp` — serverInfo {name: 'dlt MCP', version: 4.0.3}, instructions 'Helps you build with the dlt Python library', with tools, prompts, and resources capabilities.”

Issue scoped/least-privilege API credentials for an agentweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Build against official SDKsweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://dlthub.com/docs/hub/getting-started/introduction“A developer or analyst comfortable with Python and a coding agent can build and operate ingestion, transformations, quality checks, and data apps end-to-end without managing infrastructure.”
  • [claimed-docs] https://dlthub.com/docs/hub/ai-harness/introduction“The dltHub AI Harness is a set of skills, rules, and MCP servers that teach a general-purpose coding agent (Claude Code, Cursor, or Codex) how to build production-grade pipelines, deploy and run them on dltHub managed infrastructure.”
  • [claimed-docs] https://dlthub.com/context“we turn AI assistants into expert dlt pipeline developers across over 11,200 REST API data sources.”
  • [probe] https://dlthub.com/llms.txt“PROBE llms.txt: HTTP 200 at https://dlthub.com/llms.txt # dlt — data load tool & dltHub > dlt is the open-source Python library for moving data from any source to any destinat”
  • [probe] https://github.com/dlt-hub/dlt-mcp“official MCP server documented at https://github.com/dlt-hub/dlt-mcp”
  • [probe] https://github.com/dlt-hub/dlt-mcp“PROBE runtime (recorded 2026-09-08): dltHub's official MCP server (pypi dlt-mcp, published by dltHub) completed a FULL keyless stdio initialize handshake via `uv run --with dlt-mcp[duckdb] dlt-mcp` — serverInfo {name: 'dlt MCP', version: 4.0.3}, instructions 'Helps you build with the dlt Python library', with tools, prompts, and resources capabilities.”
  • [probe] https://dlthub.com/docs/reference/installation“PROBE runtime (recorded 2026-09-08): the dlt CLI installed keylessly from pypi via uvx and printed 'dlt 1.30.0'.”
  • [github] https://github.com/dlt-hub/dlt“Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop, or an AI coding agent—dlt can be dropped in anywhere.”

Subscribe to events via webhooksweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Agent-ready = 119.0 ÷ 180 × 100 = 66.1

API quality10.3/100×0.20 of the PA blend

The programmable surface once an agent is there — machine-readable spec, interactive docs, sandbox, versioning discipline.

Explore an interactive API reference with runnable examplesweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://dlthub.com/openapi.json“PROBE openapi: all candidate paths 404 (https://dlthub.com/openapi.json, https://dlthub.com/swagger.json, https://dlthub.com/api/openapi.json, https://dlthub.com/.well-known/openapi.json)”
  • [claimed-docs] https://dlthub.com/docs/tutorial/load-data-from-an-api“let's load a list of Python dictionaries into DuckDB and inspect the created dataset”
  • [claimed-docs] https://dlthub.com/docs/tutorial/load-data-from-an-api“pipeline = dlt.pipeline( pipeline_name="quick_start", destination="duckdb", dataset_name="mydata")load_info = pipeline.run(data, table_name="users")”

Download a machine-readable API spec (OpenAPI or equivalent)weight 2

2 (weight) × 3 (quality) × 0.6 (partial) = 3.6 of 20 max

  • [probe] https://dlthub.com/llms.txt“PROBE llms.txt: HTTP 200 at https://dlthub.com/llms.txt # dlt — data load tool & dltHub > dlt is the open-source Python library for moving data from any source to any destinat”
  • [probe] https://dlthub.com/openapi.json“PROBE openapi: all candidate paths 404 (https://dlthub.com/openapi.json, https://dlthub.com/swagger.json, https://dlthub.com/api/openapi.json, https://dlthub.com/.well-known/openapi.json)”

Test against a sandbox environment without touching production dataweight 1

1 (weight) × 6 (quality) × 0.6 (partial) = 3.6 of 10 max

  • [github] https://github.com/dlt-hub/dlt“pip install "dlt[duckdb]" # local DuckDB destination”
  • [claimed-docs] https://dlthub.com/docs/tutorial/load-data-from-an-api“let's load a list of Python dictionaries into DuckDB and inspect the created dataset”
  • [claimed-docs] https://dlthub.com/docs/tutorial/load-data-from-an-api“pipeline = dlt.pipeline( pipeline_name="quick_start", destination="duckdb", dataset_name="mydata")load_info = pipeline.run(data, table_name="users")”
  • [probe] https://dlthub.com/docs/reference/command-line-interface“PROBE runtime (recorded 2026-09-08): a REAL `dlt init chess duckdb` scaffold ran keylessly in a throwaway fixture — it fetched the verified source, wrote chess_pipeline.py with runnable pipeline code, a .dlt/secrets.toml template, and requirements.txt ('Verified source chess was added to your project!'). Self-cleaned.”

Rely on versioned APIs with a documented deprecation policyweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

API quality = 7.2 ÷ 70 × 100 = 10.3

Openness78.0/100×0.20 of the PA blend

Can you leave, inspect, or self-host — data export, open source, portability.

Do everything through the API that I can do in the UIweight 2

2 (weight) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

  • [claimed-docs] https://dlthub.com/docs/reference/command-line-interface“Creates, adds, inspects and deploys dlt pipelines.”
  • [claimed-docs] https://dlthub.com/docs/walkthroughs/deploy-a-pipeline“Deploy your pipelines with a single `dlthub deploy` command. Schedule, refresh, backfill, and observe runs with a familiar decorator-based Python API.”
  • [claimed-docs] https://dlthub.com/docs/hub/pipeline-operations/monitoring“Use the dltHub CLI and the Web UI at app.dlthub.com to monitor pipeline health, inspect logs, and diagnose failures.”
  • [community] https://hn.algolia.com/api/v1/items/45712393“One of the reasons why I like dlt is because I can do everything via code, which makes things more maintainable, for me.”
  • [probe] https://dlthub.com/docs/reference/command-line-interface“PROBE runtime (recorded 2026-09-08): a REAL `dlt init chess duckdb` scaffold ran keylessly in a throwaway fixture — it fetched the verified source, wrote chess_pipeline.py with runnable pipeline code, a .dlt/secrets.toml template, and requirements.txt ('Verified source chess was added to your project!'). Self-cleaned.”

Export all of my data in open formats and leaveweight 3

3 (weight) × 7 (quality) × 1.0 (full) = 21.0 of 30 max

  • [claimed-docs] https://dlthub.com/docs/general-usage/destination“This approach is especially useful when switching between destinations without modifying the actual pipeline code.”
  • [claimed-docs] https://dlthub.com/docs/general-usage/dataset-access/dataset“You can build the query with data frame expressions, Ibis, or SQL. You can read the result as records, Pandas frames, or Arrow tables.”
  • [claimed-docs] https://dlthub.com/docs/general-usage/dataset-access/dataset“use `pipeline.dataset()` to query the data. You can build the query with data frame expressions, Ibis, or SQL.”
  • [claimed-docs] https://dlthub.com/docs/intro“dlt supports a variety of popular destinations and has an interface to add custom destinations to create reverse ETL pipelines.”
  • [github] https://github.com/dlt-hub/dlt“pip install "dlt[duckdb]" # local DuckDB destination”
  • [claimed-docs] https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database“We support all SQLAlchemy dialects, which include, but are not limited to... PostgreSQL, MySQL, SQLite, Oracle, Microsoft SQL Server”

Read the product's source under an open licenseweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [probe] https://dlthub.com/llms.txt“PROBE llms.txt: HTTP 200 at https://dlthub.com/llms.txt # dlt — data load tool & dltHub > dlt is the open-source Python library for moving data from any source to any destinat”
  • [github] https://github.com/dlt-hub/dlt“pip install "dlt[duckdb]" # local DuckDB destination”
  • [github] https://github.com/dlt-hub/dlt“Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop, or an AI coding agent—dlt can be dropped in anywhere.”

Self-host the core productweight 3

3 (weight) × 9 (quality) × 1.0 (full) = 27.0 of 30 max

  • [claimed-docs] https://dlthub.com/docs/intro“dlt can be deployed anywhere Python runs, be it on Airflow, serverless functions”
  • [github] https://github.com/dlt-hub/dlt“pip install "dlt[duckdb]" # local DuckDB destination”
  • [github] https://github.com/dlt-hub/dlt“Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop, or an AI coding agent—dlt can be dropped in anywhere.”
  • [probe] https://dlthub.com/docs/reference/command-line-interface“PROBE runtime (recorded 2026-09-08): a REAL `dlt init chess duckdb` scaffold ran keylessly in a throwaway fixture — it fetched the verified source, wrote chess_pipeline.py with runnable pipeline code, a .dlt/secrets.toml template, and requirements.txt ('Verified source chess was added to your project!'). Self-cleaned.”
  • [probe] https://dlthub.com/docs/reference/installation“PROBE runtime (recorded 2026-09-08): the dlt CLI installed keylessly from pypi via uvx and printed 'dlt 1.30.0'.”
  • [claimed-docs] https://dlthub.com/docs/reference/command-line-interface“Creates, adds, inspects and deploys dlt pipelines.”

Openness = 78.0 ÷ 100 × 100 = 78.0

Built-in AI24.9/100×0.15 of the PA blend

Inside-out: how agentic the product itself is for its users — built-in assistants, autonomous features.

Get AI-generated insights and suggestions from my data inside the productweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Set up automations that run autonomously in the backgroundweight 2

2 (weight) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

  • [claimed-docs] https://dlthub.com/docs/walkthroughs/deploy-a-pipeline“Deploy your pipelines with a single `dlthub deploy` command. Schedule, refresh, backfill, and observe runs with a familiar decorator-based Python API.”
  • [claimed-docs] https://dlthub.com/docs/intro“dlt can be deployed anywhere Python runs, be it on Airflow, serverless functions”
  • [claimed-docs] https://dlthub.com/docs/hub/pipeline-operations/monitoring“Use the dltHub CLI and the Web UI at app.dlthub.com to monitor pipeline health, inspect logs, and diagnose failures.”
  • [claimed-docs] https://dlthub.com“Any engineer on your team can ship production data, with agents doing the work on infra we run. Every run is logged and auditable.”
  • [github] https://github.com/dlt-hub/dlt“Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop, or an AI coding agent—dlt can be dropped in anywhere.”

Delegate tasks to a built-in AI assistant inside the productweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

  • [claimed-docs] https://dlthub.com/docs/hub/ai-harness/introduction“The dltHub AI Harness is a set of skills, rules, and MCP servers that teach a general-purpose coding agent (Claude Code, Cursor, or Codex) how to build production-grade pipelines, deploy and run them on dltHub managed infrastructure.”
  • [claimed-docs] https://dlthub.com/context“we turn AI assistants into expert dlt pipeline developers across over 11,200 REST API data sources.”
  • [probe] https://github.com/dlt-hub/dlt-mcp“PROBE runtime (recorded 2026-09-08): dltHub's official MCP server (pypi dlt-mcp, published by dltHub) completed a FULL keyless stdio initialize handshake via `uv run --with dlt-mcp[duckdb] dlt-mcp` — serverInfo {name: 'dlt MCP', version: 4.0.3}, instructions 'Helps you build with the dlt Python library', with tools, prompts, and resources capabilities.”

Operate the product with natural-language commandsweight 2

2 (weight) × 7 (quality) × 0.6 (partial) = 8.4 of 20 max

  • [claimed-docs] https://dlthub.com/docs/hub/getting-started/introduction“A developer or analyst comfortable with Python and a coding agent can build and operate ingestion, transformations, quality checks, and data apps end-to-end without managing infrastructure.”
  • [claimed-docs] https://dlthub.com/docs/hub/ai-harness/introduction“The dltHub AI Harness is a set of skills, rules, and MCP servers that teach a general-purpose coding agent (Claude Code, Cursor, or Codex) how to build production-grade pipelines, deploy and run them on dltHub managed infrastructure.”
  • [claimed-docs] https://dlthub.com/context“we turn AI assistants into expert dlt pipeline developers across over 11,200 REST API data sources.”
  • [probe] https://github.com/dlt-hub/dlt-mcp“official MCP server documented at https://github.com/dlt-hub/dlt-mcp”
  • [probe] https://github.com/dlt-hub/dlt-mcp“PROBE runtime (recorded 2026-09-08): dltHub's official MCP server (pypi dlt-mcp, published by dltHub) completed a FULL keyless stdio initialize handshake via `uv run --with dlt-mcp[duckdb] dlt-mcp` — serverInfo {name: 'dlt MCP', version: 4.0.3}, instructions 'Helps you build with the dlt Python library', with tools, prompts, and resources capabilities.”

Built-in AI = 22.4 ÷ 90 × 100 = 24.9

Automation37.8/100×0.15 of the PA blend

Depth of automation primitives — rules, scheduling, bulk operations, webhooks.

Perform bulk operations across many items at onceweight 2

2 (weight) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

  • [claimed-docs] https://dlthub.com/docs/general-usage/incremental-loading“Merge: Merges new data into the destination using merge_key and/or deduplicates/upserts new data using primary_key.”
  • [claimed-docs] https://dlthub.com/docs/general-usage/incremental-loading“Incremental loading is the act of loading only new or changed data and not old records that we have already loaded.”
  • [claimed-docs] https://dlthub.com/docs/general-usage/schema-evolution“dlt automatically infers the initial schema for your first pipeline run... dlt handles these schema changes, enabling you to adapt to changes without losing velocity.”
  • [probe] https://dlthub.com/docs/reference/command-line-interface“PROBE runtime (recorded 2026-09-08): a REAL `dlt init chess duckdb` scaffold ran keylessly in a throwaway fixture — it fetched the verified source, wrote chess_pipeline.py with runnable pipeline code, a .dlt/secrets.toml template, and requirements.txt ('Verified source chess was added to your project!'). Self-cleaned.”
  • [claimed-docs] https://dlthub.com/docs/tutorial/load-data-from-an-api“pipeline = dlt.pipeline( pipeline_name="quick_start", destination="duckdb", dataset_name="mydata")load_info = pipeline.run(data, table_name="users")”

Define rules that trigger actions automatically on eventsweight 3

3 (weight) × 4 (quality) × 0.6 (partial) = 7.2 of 30 max

  • [claimed-docs] https://dlthub.com/docs/intro“dlt automates pipeline maintenance with incremental loading, schema evolution, and schema and data contracts.”
  • [claimed-docs] https://dlthub.com/docs/general-usage/schema-evolution“dlt automatically infers the initial schema for your first pipeline run... dlt handles these schema changes, enabling you to adapt to changes without losing velocity.”
  • [claimed-docs] https://dlthub.com/docs/general-usage/incremental-loading“Merge: Merges new data into the destination using merge_key and/or deduplicates/upserts new data using primary_key.”
  • [claimed-docs] https://dlthub.com/docs/general-usage/incremental-loading“Incremental loading is the act of loading only new or changed data and not old records that we have already loaded.”

Schedule recurring jobs or workflowsweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://dlthub.com/docs/intro“dlt can be deployed anywhere Python runs, be it on Airflow, serverless functions”
  • [claimed-docs] https://dlthub.com/docs/walkthroughs/deploy-a-pipeline“Deploy your pipelines with a single `dlthub deploy` command. Schedule, refresh, backfill, and observe runs with a familiar decorator-based Python API.”
  • [claimed-docs] https://dlthub.com/docs/hub/pipeline-operations/monitoring“Use the dltHub CLI and the Web UI at app.dlthub.com to monitor pipeline health, inspect logs, and diagnose failures.”

Version, review, and roll back my automationsweight 1

1 (weight) × 3 (quality) × 0.6 (partial) = 1.8 of 10 max

  • [claimed-docs] https://dlthub.com“Any engineer on your team can ship production data, with agents doing the work on infra we run. Every run is logged and auditable.”
  • [claimed-docs] https://dlthub.com/docs/hub/pipeline-operations/monitoring“Use the dltHub CLI and the Web UI at app.dlthub.com to monitor pipeline health, inspect logs, and diagnose failures.”
  • [claimed-docs] https://dlthub.com/docs/intro“dlt automates pipeline maintenance with incremental loading, schema evolution, and schema and data contracts.”
  • [claimed-docs] https://dlthub.com/docs/general-usage/schema-evolution“dlt automatically infers the initial schema for your first pipeline run... dlt handles these schema changes, enabling you to adapt to changes without losing velocity.”

Automation = 30.2 ÷ 80 × 100 = 37.8