Like many organizations, tecRacer uses HubSpot as a CRM. Integrating Hubspot with other (internal) systems enables smooth workflows for everyone involved. Since I recently built a custom integration, I thought it may be helpful to explain how to set up a secure interface with AWS.
It’s probably not a surprise that (almost) everything Amazon Web Services offers is accessible through web services. AWS also provides a range of SDKs that make interfacing with and integrating them in your code base relatively painless. These SDKs are sometimes a bit limited, though. There are certain things that you can only do through the AWS console, meaning that there are still services under the surface, but they’re undocumented and not accessible through the official SDKs. That API call is what we refer to as an undocumented API. In this post, I’m going to show you how to discover these APIs and use them in your scripts.
In the most recent installment of the Serverless Dash series, we’ll look at implementing proper authentication using Cognito. This post covers setting up the user pool, the app client and also the logout option.
Many projects get to the point where your sophisticated infrastructure delivers reports to S3 and now you need a way for your end users to get them. Giving everyone access to the AWS account usually doesn’t work. In this post we’ll look at an alternative - we’re going to build a Serverless S3 Explorer with Dash, Lambda and the API Gateway.
I’ll teach you how to add interactive basic auth to the Serverless Dash app that we deployed recently.
Today I’m going to show you how to deploy a Dash app in a Lambda Function behind an API Gateway. This setup is truly serverless and allows you to only pay for infrastructure when there is traffic, which is an ideal deployment model for small (internal) applications. Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas.
The way from a cool POC (proof of concept), like a walk in monets garden, to a production-ready application for an RAG (Retrieval Augmented Generation) application with Amazon Bedrock and Amazon Kendra is paved with some work. Let`s get our hands dirty. With streamlit and langchain, you can quickly build a cool POC. This two-part blog is about what comes after that.
In this blog post, we’ll explore how you can teach the DynamoDB Table resource in boto3 (and the client) to store and retrieve Python’s datetime and float objects, which they can’t do natively. We’ll also discuss why you should or shouldn’t do that.
