Mambu is customer relationship software for banking and lending businesses. It lets these businesses launch a digitally native financial services platform.

Amazon S3 (Simple Storage Service) provides cloud-based object storage through a web service interface. You can use S3 to store and retrieve any amount of data, at any time, from anywhere on the web. S3 objects, which may be structured in any way, are stored in resources called buckets.

Mambu provides a RESTful API that lets developers retrieve data stored in the platform about all kinds of financial objects and entities. For example, to retrieve information about a specific deposit account, you would call GET /deposits/{depositAccountId}.

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

{
  "accountState": "PENDING_APPROVAL",
  "migrationEventKey": "string",
  "notes": "string",
  "lastSetToArrearsDate": "2019-09-06T13:37:50+03:00",
  "assignedBranchKey": "string",
  "lastOverdraftInterestReviewDate": "2019-09-06T13:37:50+03:00",
  "lastInterestStoredDate": "2019-09-06T13:37:50+03:00",
  "interestSettings": {
    "interestRateSettings": {
      "interestRate": 0,
      "interestRateTiers": [
        {
          "endingBalance": 0,
          "interestRate": 0,
          "encodedKey": "string",
          "endingDay": 0
        }
      ],
      "interestChargeFrequency": "ANNUALIZED",
      "encodedKey": "string",
      "interestChargeFrequencyCount": 0,
      "interestRateTerms": "FIXED"
    },
    "interestPaymentSettings": {
      "interestPaymentDates": [
        {
          "month": 0,
          "day": 0
        }
      ],
      "interestPaymentPoint": "FIRST_DAY_OF_MONTH"
    }
  },
  "balances": {
    "overdraftInterestDue": 0,
    "totalBalance": 0,
    "lockedBalance": 0,
    "technicalOverdraftAmount": 0,
    "overdraftAmount": 0,
    "holdBalance": 0,
    "technicalOverdraftInterestDue": 0,
    "feesDue": 0,
    "availableBalance": 0
  },
  "creditArrangementKey": "string",
  "maturityDate": "2019-09-06T13:37:50+03:00",
  "encodedKey": "string",
  "id": "string",
  "overdraftSettings": {
    "allowOverdraft": true,
    "overdraftLimit": 0,
    "overdraftExpiryDate": "2019-09-06T13:37:50+03:00"
  },
  "lastAccountAppraisalDate": "2019-09-06T13:37:50+03:00",
  "withholdingTaxSourceKey": "string",
  "assignedUserKey": "string",
  "overdraftInterestSettings": {
    "interestRateSettings": {
      "interestRate": 0,
      "interestSpread": 0,
      "interestRateReviewUnit": "DAYS",
      "interestRateSource": "FIXED_INTEREST_RATE",
      "interestRateReviewCount": 0,
      "interestRateTiers": [
        {
          "endingBalance": 0,
          "interestRate": 0,
          "encodedKey": "string",
          "endingDay": 0
        }
      ],
      "interestChargeFrequency": "ANNUALIZED",
      "encodedKey": "string",
      "interestChargeFrequencyCount": 0,
      "interestRateTerms": "FIXED"
    }
  },
  "lastModifiedDate": "2019-09-06T13:37:50+03:00",
  "accountType": "CURRENT_ACCOUNT",
  "lockedDate": "2019-09-06T13:37:50+03:00",
  "creationDate": "2019-09-06T13:37:50+03:00",
  "lastInterestCalculationDate": "2019-09-06T13:37:50+03:00",
  "assignedCentreKey": "string",
  "approvedDate": "2019-09-06T13:37:50+03:00",
  "closedDate": "2019-09-06T13:37:50+03:00",
  "accruedAmounts": {
    "overdraftInterestAccrued": 0,
    "interestAccrued": 0,
    "technicalOverdraftInterestAccrued": 0
  },
  "name": "string",
  "accountHolderKey": "string",
  "productTypeKey": "string",
  "activationDate": "2019-09-06T13:37:50+03:00",
  "internalControls": {
    "recommendedDepositAmount": 0,
    "targetAmount": 0,
    "maxWithdrawalAmount": 0
  },
  "currencyCode": "string",
  "accountHolderType": "CLIENT",
  "linkedSettlementAccountKeys": [
    "string"
  ]
}

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 Mambu 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.

To upload files you must first create an S3 bucket. Once you have a bucket you can add an object to it. An object can be any kind of file: a text file, data file, photo, or anything else. You can optionally compress or encrypt the files before you load them.

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, Mambu's API results include fields like activationDate and lastModifiedDate 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.

S3 is great, but sometimes you want a more structured repository that can serve as a basis for BI reports and data analytics — in short, a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, Microsoft Azure Synapse Analytics, or Panoply, which are RDBMSes that use similar SQL syntax. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure Synapse Analytics, and To Panoply.