Amazon DynamoDB to Grafana

This page provides you with instructions on how to extract data from Amazon DynamoDB and analyze it in Grafana. (If the mechanics of extracting data from Amazon DynamoDB seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Amazon DynamoDB?

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.

What is Grafana?

Grafana is an open source platform for time series analytics. It can run on-premises on all major operating systems or be hosted by Grafana Labs via GrafanaCloud. Grafana allows users to create, explore, and share dashboards to query, visualize, and alert on data.

Getting data out of DynamoDB

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
}

Sample DynamoDB data

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"]
}

Preparing DynamoDB data

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.

Loading data into Grafana

Analyzing data in Grafana requires putting it into a format that Grafana can read. Grafana natively supports nine data sources, and offers plugins that provide access to more than 50 more. Generally, it's a good idea to move all your data into a data warehouse for analysis. MySQL, Microsoft SQL Server, and PostgreSQL are among the supported data sources, and because Amazon Redshift is built on PostgreSQL and Panoply is built on Redshift, those popular data warehouses are also supported. However, Snowflake and Google BigQuery are not currently supported.

Analyzing data in Grafana

Grafana provides a getting started guide that walks new users through the process of creating panels and dashboards. Panel data is powered by queries you build in Grafana's Query Editor. You can create graphs with as many metrics and series as you want. You can use variable strings within panel configuration to create template dashboards. Time ranges generally apply to an entire dashboard, but you can override them for individual panels.

Keeping DynamoDB data up to date

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.

From Amazon DynamoDB to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Amazon DynamoDB data in Grafana is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Amazon DynamoDB to Redshift, Amazon DynamoDB to BigQuery, Amazon DynamoDB to Azure SQL Data Warehouse, Amazon DynamoDB to PostgreSQL, Amazon DynamoDB to Panoply, and Amazon DynamoDB to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Amazon DynamoDB with Grafana. With just a few clicks, Stitch starts extracting your Amazon DynamoDB data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Grafana.