DynamoDB supports complex data types like lists. In this post we take a look at different ways to interact with lists. We will use Python to write code that may be used in a data access layer to manipulate items with list attributes.
CDK GO is in preview, here are the pros and cons and a first VPC example TL;DR The GO module system is IMHO neater than node.JS or Python. But you have to get used to the strongly typed language GO.
CDK Lambda Deployment takes about a minute - how about sub second Function Code Deployment? Creation of Lambda infrastructure with the CDK is really powerful. Updating the Function code is really slow. Here is a fix for that to get to a sub-second Lambda function deployment time.
DynamoDB allows us to store complex data structures and deeply nested objects, but this complexity isn’t free. In this post we take a look at how different Lambda configurations impact the read times from boto3. We examine how different resource configurations can improve the read time of the same item by more than a factor of 12.
Data modelling in NoSQL databases is different from what we’re used to in the relational world. In this article we’ll talk about the process of data modelling in DynamoDB, single-table design and how to build a basic data access layer using python. We’ll explore these concepts by building a product catalog for a simple webshop that supports different query patterns and basic inventory management.
In this post I’ll introduce DynamoDB, a very powerful fully managed NoSQL wide-column data store in AWS. We will talk about data structures, the APIs to read and write data, indexes, as well as performance and cost considerations. In the end you will gain a solid understanding of the basics, which will serve as a starting point for further research.
Many Lambda functions are written in Python and use the AWS SDK boto3. Initializing your first boto3 client or resource can take a long time after a Lambda cold start. In this article we explore how much exactly and why it’s up to 10x faster on Lambdas with more memory.
Amazon CloudWatch is a managed service for storing, visualizing and analyzing logs and metrics data of applications and AWS infrastructure. The service is simple to configure and use and is priced based on usage. Thus, adoption is generally both easy and cheap compared to other tools such as Splunk, ElasticSearch, Datadog, and all the others out there. Upon heavy usage, it can become quite expensive though. This post summarizes some common cost drivers in CloudWatch and proposes strategies to identify these and reduce their costs.
