RWS Docs

The RWS Connect API exposes your RWS Connect tables as a REST API. Every query is a GET request to https://connect.rwsintegration.com, and what you retrieve is controlled entirely by query parameters. The examples on this page use a fictional tenant acme with a table employees (columns employee_id, name, department, city, salary, hired_at, plus the standard columns).

Setting up the connection

RWS Connect uses a standard Simple connection:

Field	Value
Name	`RWS Connect`
Type	`API`
URL	`https://connect.rwsintegration.com`
Base Path	`/`
Authentication	`Simple`
Headers	`x-api-key`: the key provided by RWS

Request basics

Two parameters are required on every query:

Parameter	Value
`database`	Your tenant name, provided during onboarding
`table`	The table name, agreed when the connector was requested

GET https://connect.rwsintegration.com/?database=acme&table=employees

The response always has the same shape:

{
  "Items": [
    {
      "employee_id": "1042",
      "name": "Ana Souza",
      "department": "Sales",
      "city": "Manaus",
      "salary": 4200.0,
      "extracted_at": "2026-07-28 06:00:12.000",
      "extraction_date": "2026-07-28"
    }
  ],
  "Total": 1580
}

Items: the records for the requested page. In your extract configuration, set Datapoint Path in Response to Items.
Total: how many records match the query overall (across all pages). For grouped queries, the number of groups.

Without pagination parameters, a query returns the first 20 records.

Column prefixes

A pipeline often combines several endpoints or source tables into one dataset. In that case each column is prefixed with the source it came from, and every record arrives as a single flat JSON object:

{
  "employees_employee_id": "1042",
  "employees_name": "Ana Souza",
  "contracts_position": "Sales Analyst",
  "contracts_start_date": "2024-03-01"
}

Filters, selects and all other parameters use the full prefixed column name (for example filter[contracts_start_date][>=]=2026-01-01). The examples on this page use a single-source table with unprefixed columns for brevity.

Selecting columns

Project only the columns you need with select[column] (empty value):

?database=acme&table=employees&select[name]=&select[city]=

Filtering

Filter with filter[column][operator]=value. Omitting the operator means equality:

?database=acme&table=employees&filter[city]=Manaus
?database=acme&table=employees&filter[salary][>=]=3000

Supported operators:

Operator	Meaning
`=` (default), `!=`, `<>`	Equal / not equal
`>`, `>=`, `<`, `<=`	Comparison
`in`, `not in`	Value in a list
`between`, `not between`	Value inside a range (two values)
`like`, `not like`	Pattern matching, `%` as wildcard
`ilike`, `not ilike`	Case-insensitive pattern matching
`is`, `is not`	Identity comparison

List and range operators take array values:

?database=acme&table=employees&filter[department][in][]=Sales&filter[department][in][]=Finance
?database=acme&table=employees&filter[hired_at][between][]=2026-01-01&filter[hired_at][between][]=2026-06-30

Multiple filters combine with AND:

?database=acme&table=employees&filter[city]=Manaus&filter[salary][>=]=3000

When calling the API by hand (for example with curl), remember to URL-encode special characters: % in a like pattern becomes %25. In the platform’s extract configuration, query parameter values are encoded automatically.

Filtering by date and time

Prefix the column name with timestamp_ to compare as date/time instead of text. The prefix only exists in the filter; the column keeps its real name in the response:

?database=acme&table=employees&filter[timestamp_extracted_at][>=]=2026-07-01

This is where dynamic parameters shine. For example, a daily integration that only reads yesterday’s snapshot:

filter[timestamp_extraction_date][>=]={{ now.subtract(1, days).format(YYYY-MM-DD) }}

Sorting

?database=acme&table=employees&sort[name]=asc
?database=acme&table=employees&sort[salary]=desc

Pagination

The API paginates with page[size] and page[number] (starting at 1):

?database=acme&table=employees&page[size]=100&page[number]=2

In your extract configuration this maps directly onto Simple pagination:

Field	Value
Pagination Type	`Simple`
Page size parameter	`page[size]`
Page size value	e.g. `100`
Initial page parameter	`page[number]`
Initial page value	`1`
Pagination end type	`Object`

Grouping and aggregations

Group with group[column] (or the shorthand select[column]=group, which also returns the column) and aggregate with select[column]=<function>:

?database=acme&table=employees&select[city]=group&select[salary]=sum

{
  "Items": [
    { "city": "Manaus", "salary": 182000.0 },
    { "city": "Belém", "salary": 97000.0 }
  ],
  "Total": 2
}

Aggregation	Meaning
`sum`	Sum of the column per group
`sum_distinct`	Sum of distinct values per group
`avg`	Average per group
`min` / `max`	Minimum / maximum per group
`count`	Count per group

For grouped queries, Total is the number of groups.

Latest record per key

RWS Connect tables preserve history: every pipeline run adds a snapshot. The over_ filter answers the most common question about such tables: “give me only the newest record for each key.”

?database=acme&table=employees&filter[over_employee_id][extracted_at]=first

This returns one record per employee_id: the one with the highest extracted_at (first keeps the newest, last keeps the oldest). The syntax is filter[over_<columns>][<sort column>]=first|last. Combine grouping columns with _and_:

?database=acme&table=employees&filter[over_employee_id_and_department][extracted_at]=first

Only one over_ filter is allowed per query.
over_ cannot be combined with select, group or aggregation parameters.
The winning record per key is chosen before other filters are applied. filter[over_employee_id][extracted_at]=first plus a date filter means “take each employee’s newest record overall, then keep it only if it passes the date filter”, not “the newest record within the date range”.

Sums per key

To total a column per key while still returning one record per key, use select[column]=sum_over with over_group (required):

?database=acme&table=employees&select[salary]=sum_over&over_group=department

Standard columns

Every RWS Connect table carries these columns, useful for filtering snapshots:

Column	Meaning
`extracted_at`	Timestamp when the record was extracted
`extraction_date`	Date of the pipeline run that produced the record
`extract_start_date_parameter`	Start of the date window the run extracted
`extract_end_date_parameter`	End of the date window the run extracted

Behavior and limits

Results for identical queries may be served from a cache for up to 10 minutes
Default page size is 20 records
Only one over_ window filter per query, and it cannot be combined with select, group or aggregations

Connections

Business Rules

Extract

Load

Monitoring

Querying the RWS Connect API

Setting up the connection

Request basics

Column prefixes

Selecting columns

Filtering

Filtering by date and time

Sorting

Grouping and aggregations

Latest record per key

Sums per key

Standard columns

Behavior and limits

Next steps

Guide: Extract from RWS Connect

Dynamic Parameters

​Setting up the connection

​Request basics

​Column prefixes

​Selecting columns

​Filtering

​Filtering by date and time

​Sorting

​Pagination

​Grouping and aggregations

​Latest record per key

​Sums per key

​Standard columns

​Behavior and limits

​Next steps

Guide: Extract from RWS Connect

Dynamic Parameters

Setting up the connection

Request basics

Column prefixes

Selecting columns

Filtering

Filtering by date and time

Sorting

Pagination

Grouping and aggregations

Latest record per key

Sums per key

Standard columns

Behavior and limits

Next steps