Libraries tagged by data integration

Connect brokerage accounts to your app for live positions and trading. ## Rate limiting Two limits apply to requests signed with your `clientId`. The stricter one wins, and exceeding either returns `429 Too Many Requests`. - **Customer-level** — 250 requests/minute by default, scoped to your `clientId` and applied across all endpoints. Reported in `X-RateLimit-Limit`, `X-RateLimit-Remaining` and `X-RateLimit-Reset`. - **Account-level** — 10 requests/minute per account, scoped to (`clientId`, `accountId`). All covered operations for one account draw on the same bucket — reading balances and reading positions share it — and enforcement does not depend on the HTTP method, so updating an account consumes the same bucket as reading it. Only enforced for Personal users, and only for integrations it has been rolled out to — it is not yet in force for every Personal integration. It also does not apply on every operation that documents a 429 below. Where it applies it is reported in `X-RateLimit-Account-Limit`, `X-RateLimit-Account-Remaining` and `X-RateLimit-Account-Reset`. Do not read the absence of those headers as proof the limit is off — some configurations omit the rate limit headers while still enforcing the limit, so header absence tells you nothing about your allowance. On a 429, `X-RateLimit-Remaining: 0` means you hit the customer-level limit and `X-RateLimit-Account-Remaining: 0` means the account-level one. Wait for the corresponding `*-Reset` value (seconds) before retrying, or fall back to exponential backoff with jitter. Not every 429 is explained by those headers. A separate per-authenticated-user limit, reported in no `X-RateLimit-*` header, covers OAuth-authenticated requests and signed requests in configurations where the customer-level limit is not in effect — on the operations that use the default throttles. A few operations override those and are governed by the customer-level limit alone. The two do not stack: a signed request governed by the customer-level limit above is not additionally subject to the per-user one. If a 429 arrives with no header at zero — or with no `X-RateLimit-*` headers at all — honour `Retry-After` and back off. Treat the remaining counts as a hint, not a guarantee that the next request will succeed. Because the customer-level limit applies everywhere, any signed request can return 429. **OAuth-authenticated requests are an exception.** They are not subject to the customer-level limit and do not receive `X-RateLimit-Limit`, `X-RateLimit-Remaining` or `X-RateLimit-Reset` — do not wait on those headers or design around a customer-level allowance on this path. The account-level limit still applies to them on the account-data endpoints above, reported in the `X-RateLimit-Account-*` headers. On operations using the default throttles the per-user limit above applies to them as well, so an OAuth request can be rejected while the account headers still show capacity; on the few operations that override those throttles, OAuth callers have no per-user ceiling at all. Drive retries from `Retry-After` and exponential backoff with jitter rather than from the headers. See https://docs.snaptrade.com/docs/ratelimiting.

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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Privacy-friendly analytics integration for TYPO3 CMS. Analyze your audience with Plausible Analytics and see nice dashboards with analytics data directly in the TYPO3 backend.

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Official PHP SDK for Trading Card API - comprehensive tools for accessing trading card data, players, sets, and market information with enhanced error handling and Laravel integration

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Matomo Analytics integration for Filament Panels with a set of widgets to display your analytics data in a beautiful way.

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Data provider for manifests generation and integration tests

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Laravel integration for omerkoseoglu/devextreme-data: server-side DevExtreme data processing over Eloquent models, query builders and collections.

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The Nepali Date package is designed for working with the Nepali calendar. It provides functionality for converting dates between the English (AD) and Nepali (BS) calendars, along with a wide range of methods for handling and manipulating Nepali dates. This comprehensive tool facilitates seamless integration and operations within the Nepali calendar system.

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laravel integration for a timezone-agnostic calendar date value object

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Provides integration of InfluxDB ODM with Doctrine Data Fixtures

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ModEl integration with Zebra DatePicker (latest, via CDN)

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Complete fake simulation layer for MySQL, DBAL, Redis, and Mongo adapters.

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An extension that posts Magento/Adobe Commerce data to Kustomer via webhooks

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Provides integration of InfluxDB ODM with Nelmio alice extension to persist the loaded fixtures

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Laravel package for URL value objects, sanitization pipelines, browser URL support, validation rules, request macros, and Laravel Data integration.

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