https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/getting-started/Recent content in Getting Started onHugo -- gohugo.iohttps://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/introduction/Mon, 01 Jan 0001 00:00:00 +0000https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/introduction/Quickstart # 👉 Trying to deploy DJ for internal use? See the guide to Deploying DJ. 👉 Trying to onboard onto DJ for data modeling? See the guide to Data Modeling. 👉 Want to contribute to DJ develpopment? See the Developers Guide What is it? # DataJunction (DJ) is an open source metrics platform that allows users to define metrics and the data models behind them using SQL, serving as a semantic layer on top of a physical data warehouse.https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/using-the-dj-cli/Mon, 01 Jan 0001 00:00:00 +0000https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/using-the-dj-cli/The DataJunction CLI (dj) provides a command-line interface for managing your DataJunction deployment, nodes, and namespaces. Installation # The CLI is installed automatically with the DataJunction Python client: pip install datajunction Configuration # Authentication # Set your DJ credentials using environment variables: export DJ_URL="http://localhost:8000" export DJ_USER="your-username" export DJ_PWD="your-password" Available Commands # Node Inspection # dj describe <node-name> # Display detailed information about a node including its type, description, query, columns, and metadata.https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/using-dj-with-ai-assistants/Mon, 01 Jan 0001 00:00:00 +0000https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/using-dj-with-ai-assistants/DataJunction integrates with AI assistants like Claude, letting you explore your semantic layer, generate SQL, and query metrics through natural conversation — without writing code or crafting API requests directly. The integration has three components that work together: MCP tools — give Claude the ability to query your live DJ instance: search nodes, generate SQL, inspect lineage, run queries, and more. Built on MCP (Model Context Protocol), an open standard for connecting AI assistants to external tools.https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/listing-metrics/Mon, 01 Jan 0001 00:00:00 +0000https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/listing-metrics/One of the most imporant entities in DataJunction are metrics. Exploring DataJunction usually starts with exploring available metrics. curl curl -X GET http://localhost:8000/metrics/ python from datajunction import DJClient dj = DJClient(DJ_URL) metrics = dj.metrics() You can also narrow the list of metrics to a specific namespace. Here’s an example of only listing metrics in a default namespace. curl curl -X GET http://localhost:8000/namespaces/default/?type_=metric python namespace = dj.namespace("default") print(namespace.metrics()) Metric Details # After selecting a metric, you can retrieve details for the given metric.https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/requesting-sql/Mon, 01 Jan 0001 00:00:00 +0000https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/requesting-sql/Metrics SQL # DJ can generate SQL for one or more metrics with a set of compatible filters and group by dimensions. python dj.sql( metrics=[ "default.avg_repair_price", "default.num_repair_orders", ], dimensions=[ "default.hard_hat.city", "default.hard_hat.state", ], filters=[ "default.hard_hat.state = 'NY'" ], ) curl curl -X 'GET' \ 'http://localhost:8000/sql/?metrics=default.num_repair_orders&&metrics=default.avg_repair_price&dimensions=default.hard_hat.city&dimensions=default.hard_hat.state&filters=default.hard_hat.state%20%3D%20%27NY%27' \ -H 'accept: application/json' javascript dj.sql.get( metrics=[ "default.num_repair_orders", "default.avg_repair_price", ], dimensions=[ "default.hard_hat.city", "default.hard_hat.state", ], filters=["default.hard_hat.state = 'NY'"] ).then(data => console.log(data)) 👉 You can optionally provide an engine_name and engine_version.https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/requesting-data/Mon, 01 Jan 0001 00:00:00 +0000https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/requesting-data/DJ can generate SQL for one or more metrics with a set of compatible filters and dimensions. curl curl -X 'GET' \ 'http://localhost:8000/data/?metrics=default.num_repair_orders&dimensions=default.all_dispatchers.company_name&filters=default.all_dispatchers.company_name%20IS%20NOT%20NULL' \ -H 'accept: application/json' python dj.data( metrics=[ "num_repair_orders", "avg_repair_price" ], dimensions=[ "hard_hat.city", "hard_hat.state", "dispatcher.company_name" ], filters=[ "hard_hat.state = 'AZ'" ], ) javascript dj.data.get( metrics=["default.num_repair_orders"], dimensions=["default.all_dispatchers.company_name"], filters=["default.all_dispatchers.company_name IS NOT NULL"] ).then(data => console.log(data)) 👉 You can optionally provide an engine_name and engine_version. A typical DataJunction query service will include a default engine.https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/datajunction--trino-quickstart/Mon, 01 Jan 0001 00:00:00 +0000https://6a024bf2a7e6a60008bd1284--thriving-cassata-78ae72.netlify.app/docs/0.1.0/getting-started/datajunction--trino-quickstart/This tutorial will guide you through the process of setting up DataJunction, a powerful metrics platform, to work with Trino as the query compute engine. By the end of this tutorial, you’ll have a functional DataJunction instance layered on top of a local dockerized Trino deployment, and you’ll be able to register tables, define dimensions, create metrics, and more. Prerequisites # Docker installed on your system. Basic knowledge of SQL and REST APIs.