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.

Tableau is one of the world's most popular analysis platforms. The software helps companies model, explore, and visualize their data. It also offers cloud capabilities that allow analyses to be shared via the web or company intranets, and its offerings are available as both installed software and as a SaaS platform. Tableau is widely known for its robust and flexible visualization capabilities, which include dozens of specialized chart types.

In addition to its business software, Tableau also offers a free product called Tableau Public for analyzing open data sets. If you're new to Tableau, this offering is a great way to experience Tableau's capabilities at no cost and share your work publicly.

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.

Analyzing data in Tableau requires putting it into a format that Tableau can read. Depending on the data source, you may have options for achieving this goal, but the best practice among most businesses is to build a data warehouse that contains the data, and then connect that data warehouse to Tableau.

Tableau provides an easy-to-use Connect menu that allows you to connect data from flat files, direct data sources, and data warehouses. In most cases, connecting these sources is simply a matter of creating and providing credentials to the relevant services.

Once the data is connected, Tableau offers an option for locally caching your data to speed up queries. This can make a big difference when working with slower database platforms or flat files, but is typically not necessary when using a scalable data warehouse platform. Tableau's flexibility and speed in these areas are among its major differentiators in the industry.

Tableau's report-building interface may seem intimidating at first, but it's one of the most powerful and intuitive analytics UIs on the market. Once you understand its workflow, it offers fast and nearly limitless options for building reports and dashboards.

If you're familiar with Pivot Tables in Excel, the Tableau report building experience may feel somewhat familiar. The process involves selecting the rows and columns desired in the resulting data set, along with the aggregate functions used to populate the data cells. Users can also specify filters to be applied to the data and choose a visualization type to use for the report.

You can learn how to build a report from scratch for free (although a sign-in is required) from the Tableau documentation.

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.