Revinate is a CRM and email marketing platform for the hotel industry. It aims to address online reputation by soliciting guest surveys and collecting reviews for TripAdvisor or Google.

Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.

With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.

Revinate's API lets developers get at information stored in the platform about things like hotels and reviews. For example, to retrieve a particular review using the Revinate API, you would call GET /reviews/{reviewId}.

Here's an example of the fields you might see in a response to a query like the one above.

{
  "title": "",
  "body": "",
  "author": "",
  "authorLocation": "",
  "dateReview": 0,
  "dateCollected": 0,
  "updatedAt": 0,
  "rating": 0,
  "nps": 0,
  "reviewSite": {
    "name": "",
    "mainUrl": "",
    "slug": "",
    "links": [
      {
        "rel": "",
        "href": "",
        "templated": false
      }
    ]
  },
  "language": {
    "name": "",
    "englishName": "",
    "slug": "",
    "links": [
      {
        "rel": "",
        "href": "",
        "templated": false
      }
    ]
  },
  "crawledUrl": "",
  "subratings": {},
  "tripType": "",
  "guestStay": {
    "checkinDate": "",
    "checkoutDate": "",
    "loyaltyId": "",
    "confirmationCode": "",
    "bookingChannel": "",
    "roomType": "",
    "roomNumber": "",
    "rate": "",
    "rateCurrency": "",
    "ratePlanCode": "",
    "checkedInBy": "",
    "checkedOutBy": "",
    "groupName": "",
    "guest": {
      "title": "",
      "firstName": "",
      "lastName": "",
      "phone": "",
      "email": "",
      "address": "",
      "address2": "",
      "city": "",
      "state": "",
      "country": "",
      "postalCode": "",
      "links": [
        {
          "rel": "",
          "href": "",
          "templated": false
        }
      ]
    },
    "links": [
      {
        "rel": "",
        "href": "",
        "templated": false
      }
    ]
  },
  "surveyTopics": [
    {
      "name": "",
      "questionAnswers": [
        {
          "question": {
            "name": "",
            "type": "",
            "rangeConfig": {
              "leftValue": 0,
              "rightValue": 0,
              "step": 0,
              "leftText": "",
              "rightText": ""
            },
            "multipleChoiceOptions": [
              {
                "position": 0,
                "text": ""
              }
            ]
          },
          "yesNoAnswer": "",
          "textAnswer": "",
          "ratingAnswer": 0,
          "rangeAnswer": 0,
          "multipleChoiceAnswers": [
            {
              "position": 0,
              "text": ""
            }
          ],
          "notApplicableAnswer": false
        }
      ]
    }
  ],
  "response": {
    "body": "",
    "author": "",
    "date": 0
  },
  "links": [
    {
      "rel": "",
      "href": "",
      "templated": false
    }
  ]
}

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. Revinate's 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.

You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.

Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.

In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.

The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.

Once you've created a report, Power BI lets you share it with report "consumers" in your organization.

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, Revinate's API results include fields like dateCollected and updatedAt that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take 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.