Amazon DynamoDB is a cloud-based NoSQL key-value and document database. DynamoDB automatically spreads data and traffic over multiple servers to handle any level of throughput and storage requirements.

Google Data Studio is a simple dashboard and reporting tool. It's free and easy to use, but it lacks the sophisticated features of higher-end reporting software. Many of the connectors it supports are for Google products, but third parties have written partner connectors to a wide variety of data sources. Its drag-and-drop report editor lets users create about 15 types of charts.

Amazon DynamoDB provides an API that lets developers manage database tables and indexes, and create, read, update, and delete data. The API accepts data in JSON format. As an example, to retrieve a list of n tables starting with a particular table, you would pass to DynamoDB JSON code like this:

{
   "ExclusiveStartTableName": "string",
   "Limit": number
}

The API returns data in JSON format. Here's an example of the kind of response you might see with a query like the one above, specifying a start table name of "Forum" and a limit of 3.

{
    "LastEvaluatedTableName": "Thread",
    "TableNames": ["Forum","Reply","Thread"]
}

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. Amazon DynamoDB'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.

Google Data Studio uses what it calls "connectors" to gain access to data. Data Studio comes bundled with 17 connectors, mostly to pull in data from other Google products. It also supports connectors to MySQL and PostgreSQL databases, and offers 200 connectors to other data sources built and supported by partners.

Google Data Studio provides a graphical canvas onto which users drag and drop datasets. Users can set dimensions and metrics, specify sorting and filtering, and tailor the way reports and charts are displayed.

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. A good approach would be to use API results that include date and time fields 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.