LivePerson promotes conversational commerce on digital messaging channels including SMS, Facebook Messenger, Apple Business Chat, and WhatsApp, as well as on websites and mobile apps. It lets businesses create AI-powered chatbots to handle consumer messages alongside human customer service staff.

Azure Synapse (formerly Azure SQL Data Warehouse) is a cloud-based petabyte-scale columnar database service with controls to manage compute and storage resources independently. It offers encryption of data at rest and dynamic data masking to mask sensitive data on the fly, and it integrates with Azure Active Directory. It can replicate to read-only databases in different geographic regions for load balancing and fault tolerance.

LivePerson provides several APIs, including a LiveEngage Data Access API that lets developers retrieve data stored in the platform about agent activity, engagement, web sessions, and surveys. For example, to retrieve information about agent activity, you would call GET https://{domain}/data_access_le/account/{accountID}/le/agentActivity.

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

{
     "dataAccessFiles": {
       "@id": "28045150",
       "link": {
         "@href":
    "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity",
         "@rel": "self"
       },
       "file": [
         {
           "@name": "Agent.1461387600000.1461391200000.part-00001-0",
           "@scopeStartDate": "2019-04-23T01:00:00-04:00",
           "@scopeEndDate": "2019-04-23T02:00:00-04:00",
           "@href": "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity/Agent.1461387600000.1461391200000.part-00001-0.gz"
         },
         {
           "@name": "Agent.1461391200000.1461394800000.part-00001-0",
           "@scopeStartDate": "2019-04-23T02:00:00-04:00",
           "@scopeEndDate": "2019-04-23T03:00:00-04:00",
           "@href": "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity/Agent.1461391200000.1461394800000.part-00001-0.gz"
         },
         {
           "@name": "Agent.1461394800000.1461398400000.part-00001-0",
           "@scopeStartDate": "2019-04-23T03:00:00-04:00",
           "@scopeEndDate": "2019-04-23T04:00:00-04:00",
           "@href": "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity/Agent.1461394800000.1461398400000.part-00001-0.gz"
         },
         {
           "@name": "Agent.1461398400000.1461402000000.part-00000-0",
           "@scopeStartDate": "2019-04-23T04:00:00-04:00",
           "@scopeEndDate": "2019-04-23T05:00:00-04:00",
           "@href": "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity/Agent.1461398400000.1461402000000.part-00000-0.gz"
         },
         {
           "@name": "Agent.1461402000000.1461405600000.part-00000-0",
           "@scopeStartDate": "2019-04-23T05:00:00-04:00",
           "@scopeEndDate": "2019-04-23T06:00:00-04:00",
           "@href": "https://va-a.da.liveperson.net/data_access_le/account/28045150/le/agentActivity/Agent.1461402000000.1461405600000.part-00000-0"
         }
       ]
     }
    }

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 LivePerson 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.

Azure Synapse provides a multi-step process for loading data. After extracting the data from its source, you can move it to Azure Blob storage or Azure Data Lake Store. You can then use one of three utilities to load the data:

• AZCopy uses the public internet.
• Azure ExpressRoute routes the data through a dedicated private connection to Azure, bypassing the public internet by using a VPN or point-to-point Ethernet network.
• The Azure Data Factory (ADF) cloud service has a gateway that you can install on your local server, then use to create a pipeline to move data to Azure Storage.

From Azure Storage you can load the data into Azure Synapse staging tables by using Microsoft's PolyBase technology. You can run any transformations you need while the data is in staging, then insert it into production tables. Microsoft offers documentation for the whole process.

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. 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.

Azure Synapse is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Panoply, which are RDBMSes that use similar SQL syntax. 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 Redshift, To BigQuery, To Postgres, To Snowflake, To Panoply, To S3, and To Delta Lake.