Amazon S3 or Amazon Simple Storage Service is a service offered by Amazon Web Services (AWS) that provides object storage through a web service interface. Amazon S3 uses the same scalable storage infrastructure that Amazon.com uses to run its global e-commerce network.
Amazon S3 can be employed to store any type of object which allows for uses like storage for Internet applications, backup and recovery, disaster recovery, data archives, data lakes for analytics, and hybrid cloud storage.
Streaming a Kafka topic in a Delta table on S3 using Spark Structured Streaming
Our data strategy specifies that we should store data on S3 for further processing. Raw S3 data is not the best way of dealing with data on Spark, though. In this blog I’ll show how you can use Spark Structured Streaming to write JSON records of a Kafka topic into a Delta table.
Caching resized images on S3 with Databricks
When you are training a machine learning image classification model, you often need to resize the images your dataset into smaller ones. When you retrain your model on new data, you resize the images once more. In this blog I’ll share how S3 can be used to cache the resized images.
Trigger Lambda for large S3 Bucket with SQS
At Wehkamp we use AWS Lambda to classify images on S3. The Lambda is triggered when a new image is uploaded to the S3 bucket. Currently we have over 6.400.000 images in the bucket. Now we would like to run the Lambda for all images of the bucket. In this blog I’ll show how we did this with a Python 3.6 script.
AWS Lambda Size: PIL+TF+Keras+Numpy?
At Wehkamp we’ve been using machine learning for a while now. We’re training models in Databricks (Spark) and Keras. This produces a Keras file that we use to make the actual predictions. Training is one thing, but getting them to production is quite another!
The main problem we’ve faced was that it was too big to actually fit into a lambda. This blogs shows how we’ve dealt with that problem.