Working With Images In Spark 2.4

Tomas Nykodym and Weichen Xu give us an update on working with images in the most recent version of Apache Spark:

An image data source addresses many of these problems by providing the standard representation you can code against and abstracts from the details of a particular image representation.
Apache Spark 2.3 provided the ImageSchema.readImages API (see Microsoft’s post Image Data Support in Apache Spark), which was originally developed in the MMLSpark library. In Apache Spark 2.4, it’s much easier to use because it is now a built-in data source. Using the image data source, you can load images from directories and get a DataFrame with a single image column.
This blog post describes what an image data source is and demonstrates its use in Deep Learning Pipelines on the Databricks Unified Analytics Platform.

If you’re interested in working with convolutional neural networks or otherwise need to analyze image data, check it out.

Related Posts

An Explanation Of Convolutional Neural Networks

Shirin Glander explains some of the mechanics behind Convolutional Neural Networks: Convolutional Neural Nets are usually abbreviated either CNNs or ConvNets. They are a specific type of neural network that has very particular differences compared to MLPs. Basically, you can think of CNNs as working similarly to the receptive fields of photoreceptors in the human eye. Receptive fields in our […]

Read More

Testing Kafka Streams Applications

Yeva Byzek continues her series on testing Kafka-based streaming applications: When you create a stream processing application with Kafka’s Streams API, you create a Topologyeither using the StreamsBuilder DSL or the low-level Processor API. Normally, the topology runs with the KafkaStreams class, which connects to a Kafka cluster and begins processing when you call start(). For testing though, connecting to a running […]

Read More


December 2018
« Nov Jan »