Pyspark

Leveraging Time-Based Feature Stores for Efficient Data Science Workflows In our previous post, we briefly touched upon the concept of ML feature stores and their significance in streamlining machine learning workflows. Today, we’ll again explore a specific type of feature store known as a time-based feature store, which plays a crucial role in handling temporal data and enabling efficient feature retrieval for data science tasks. In this post we’ll how a feature-lookup problem may be effectively solved in PySpark using domain knowledge and understanding how Apache Spark works with partitions and columnar data formats....

In this blog post, I will share my experience in building an ML Feature Store using PySpark. I will demonstrate how one can utilize case-when expressions to generate multiple aggregations with minimal data shuffling across the cluster. This approach is significantly more efficient than the naive method of using a combination of groupBy and pivot for generating aggregations (or features in ML terms).

This blog post presents a very detailed step-by-step guide on how to create a SparkConnect protocol extension in Java and call it from PySpark. It will also cover a topic about how to define all the necessary proto3 messages for it. At the end of this guide you will have a way to interact with Spark JVM from PySpark almost like you can with py4j in a non-connect version.

In this post, I want to discuss the groundbreaking changes in the latest LTS release of the Databricks runtime. This release introduced Spark Connect as the default way to work with shared clusters. I will give a brief introduction to the topic of internal JVM calls and Spark Connect, provide examples of 3d-party OSS projects broken in 14.3, and try to understand the reasons for such a move by Databricks.

In this post, I will show you how to use information from the spark plan to track data lineage at the column level. This approach will also works with recently introduced SparkConnect.

Sometimes it is an important question, how much memory does our DataFrame use? And there is no easy answer if you are working with PySpark. You can try to collect the data sample and run local memory profiler. You can estimate the size of the data in the source (for example, in parquet file). But we will go another way and try to analyze the logical plan of Spark from PySpark. In case when we are working with Scala Spark API we are able to work with resolved or unresolved logical plans and physical plan via a special API. But from PySpark API only string representation is available and we will work with it.

Working with File System from PySpark Motivation Any of us is working with File System in our work. Almost every pipeline or application has some kind of file-based configuration. Typically json or yaml files are used. Also for data pipelines, it is sometimes important to be able to write results or state them in a human-readable format. Or serialize some artifacts, like matplotlib plot, into bytes and write them to the disk....