Deputy to Redshift

This page provides you with instructions on how to extract data from Deputy and load it into Redshift. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Deputy?

Deputy is a workforce management platform that handles employee scheduling, timesheets, tasking, and communication.

What is Redshift?

When it was released in 2013, Amazon Redshift was the first cloud data warehouse. It uses defined schemas, columnar data storage, and massively parallel processing (MPP) architecture to provide a base for analytics reporting.

Getting data out of Deputy

Deputy provides a RESTful API that lets developers retrieve data stored in the platform about employees, timesheets, locations, and other objects. For example, to retrieve information about an employee, you would call GET /api/v1/supervise/employee/{EmployeeId}.

Sample Deputy data

Here's an example of the kind of response you might see with a query like the one above.

    "Id": 123,
    "Company": 9,
    "FirstName": "Jane",
    "LastName": "Doe",
    "DisplayName": "Jane Doe",
    "OtherName": null,
    "Salutation": null,
    "MainAddress": 157,
    "PostalAddress": null,
    "Contact": 154,
    "EmergencyAddress": 158,
    "DateOfBirth": null,
    "Gender": 0,
    "Photo": 0,
    "UserId": 123,
    "JobAppId": null,
    "Active": true,
    "StartDate": "2019-09-27T00:00:00+11:00",
    "TerminationDate": null,
    "StressProfile": 1,
    "Position": null,
    "HigherDuty": null,
    "Role": 50,
    "AllowAppraisal": true,
    "HistoryId": 4321,
    "CustomFieldData": null,
    "Creator": 1,
    "Created": "2019-09-27T11:03:21+11:00",
    "Modified": "2019-09-27T11:03:21+11:00",
    "_DPMetaData": {

Preparing Deputy data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. The Deputy documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Redshift

Once you have identified all of the columns you will want to insert, you can use the CREATE TABLE statement in Redshift to create a table that can receive all of this data.

With a table built, it may seem like the easiest way to migrate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this will be your gut reaction. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you would be better off loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

Keeping Deputy data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Deputy's API results include fields like Created and Modified that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've taken new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

Other data warehouse options

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To BigQuery, To Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to move data from Deputy to Redshift automatically. With just a few clicks, Stitch starts extracting your Deputy data, structuring it in a way that's optimized for analysis, and inserting that data into your Redshift data warehouse.