# Instant data services > Design and deploy APIs, event streams, and data pipelines with a simple data contract. From BFF endpoints to ETL workflows, Orbital handles the plumbing. Source: https://orbitalhq.com/features/instant-data-endpoints ![](/assets/hero-03-instant-endpoints-BAnRkih_.webp) Instant data services # Deploy data services **in minutes** Define the data you want. Orbital works out how to fetch, join and transform it, then exposes the result as a REST API, event stream or scheduled job. **No BFFs, pipelines or integration code to maintain.** [Get Started](/docs)[Try Playground](https://playground.taxilang.org) ## Query. Commit. Deploy. Publish any query as an API, stream or background job. Commit, push, and Orbital deploys it. Deploy ### Deploy from Git Commit a query and Orbital turns it into a running service. REST APIs, Kafka streams, WebSockets and batch jobs all use the same deployment model, with no per-endpoint infrastructure to provision. [Queries as endpoints](/docs/querying/queries-as-endpoints) ![A list of deployed queries in Orbital, each published as a running endpoint.](/assets/endpoints-queries-CyJYjUOM.png) Observability ### See how your endpoints are running Track throughput, latency and errors for every deployed endpoint, stream and job. Drill into individual executions when something looks wrong. [Explore observability](/features/observability) ![An endpoint performance dashboard, charting request rate and latency over time.](/assets/endpoints-performance-CwxMvKJT.png) - [ ### Streams for each consumer Give each consumer exactly the event shape it needs. Orbital can filter, join and enrich the same underlying stream differently for every subscriber. ](/docs/querying/streaming-data) - [ ### Data transformation Build ETL jobs as TaxiQL queries. Read from files, databases and APIs; join, enrich and transform the data; then write the result to its destination.. ](/docs/querying/streaming-data) - [ ### Adapts as systems change When upstream APIs and schemas evolve, Orbital replans the integration behind your endpoint. ](/docs/querying/queries-as-endpoints) [View docs](/docs/querying/streaming-data) ## **APIs, streams and ETL** Design and deploy APIs, event streams and data pipelines with a simple query language. From BFF endpoints to ETL workflows, Orbital handles the infrastructure. Define the response your frontend needs. Orbital fetches and joins the data from the services behind it, then publishes it as one endpoint. Define a different event shape for each consumer, all from the same source topic. Orbital filters and enriches each stream independently. Read files from S3, normalize different source formats into one model, enrich them from APIs if needed, then write the result downstream. endpoints.taxi ``` @HttpOperation(url='/api/q/customers/{customerId}', method = 'GET')query CombineCustomerData(@PathVariable customerId: CustomerId) { given { customerId } find { // Customer data comes from our CRM firstName : FirstName lastName : LastName balances : { // Account balances come from the accounts API accountBalance: AccountBalance } // Points come from the RewardsPoints API pointsStatus: RewardsPointStatus }} ``` endpoints.taxi ``` @WebsocketOperation(url='/api/q/customers/{customerId}')query CustomerPurchaseEventStream { stream { CustomerPurchaseEvent } as { // Customer data comes from our CRM firstName : FirstName lastName : LastName purchases: OrderId[] as { orderId: OrderId // Fetch the product name from the product db productName : ProductName // Fetch the stock levels from the stock API stockLevels : StockLevels // Calculate if we have sufficient in stock hasSufficient: Boolean = StockLevels <= PurchasedQty }[] }} ``` endpoints.taxi ``` // This snippet shows two warehouses - WarehouseA and WarehouseB providing stock// reports in different CSV formats.// Read them from S3, and enrich the data.@Csvclosed model WarehouseA { Product_Name : ProductName Stock_Level : StockLevel Unit_Price : Price} @Csvclosed model WarehouseB { ItemName : ProductName Quantity : StockLevel Cost : Price}query NormalizeSupplierFeeds { find { warehouseA : WarehouseA[] as StandardInventory[] warehouseB : WarehouseB[] as StandardInventory[] }} ``` [Try this example in the Taxi Playground](https://playground.taxilang.org)[Try this example in the Taxi Playground](https://playground.taxilang.org)[See the full example in the Taxi Playground](https://playground.taxilang.org) ![](/assets/andrew-kew-DbOojK19.jpg) > Data Semantics unlocks your company’s true data value, and Orbital makes it effortless. > > Using Taxi & Orbital for our re-platforming project, we cut time to market from months to days and reduced operational resources by two-thirds. > > If you’re not using this solution yet, you’re falling behind. Andrew Kew Head of Platform Engineering - APIs, Microservices and Integration ![](/assets/jack-versfeld-Cb_6dK9P.jpg) > Orbital and Taxi is a game-changer for data integration. > > The elegance and simplicity of the concept of declarative semantic schemas leads to a composable data-oriented architecture fit for the 21st century. [ Jack Versfeld Chapter Lead, Kafka and APIs - Metro Bank ](https://www.metrobankonline.co.uk/) ## Frequently asked questions Got another gnarly question? We’d love to hear it. Come and chat on [Slack](https://join.slack.com/t/orbitalapi/shared_invite/zt-697laanr-DHGXXak5slqsY9DqwrkzHg). Commit the query to Git and push. Orbital deploys it as a running API, stream or background job. There is no separate integration project or deployment pipeline to build for each endpoint. Queries can be exposed as HTTP APIs, Kafka streams, WebSockets, scheduled jobs and batch workloads. The query defines the data contract; Orbital handles the integration and runtime behind it. When an updated schema is published, Orbital replans the integration behind the endpoint. Compatible changes can be absorbed automatically. If the requested contract can no longer be satisfied safely, Orbital surfaces the problem rather than guessing. Yes. Orbital integrates with OIDC-compatible identity providers and carries the caller’s identity into policy evaluation. Access can be controlled down to individual operations and fields, including masking sensitive data before it is returned. Yes. Orbital captures throughput, latency and errors for deployed APIs, streams and jobs. You can also inspect the execution behind a request, including which services were called and where returned data came from. ## **Ship an endpoint** before your next stand-up Write a query, push it, and Orbital runs it as a REST API, a stream or a scheduled job. 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