Book Image

Distributed Data Systems with Azure Databricks

By : Alan Bernardo Palacio
Book Image

Distributed Data Systems with Azure Databricks

By: Alan Bernardo Palacio

Overview of this book

Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.
Table of Contents (17 chapters)
Section 1: Introducing Databricks
Section 2: Data Pipelines with Databricks
Section 3: Machine and Deep Learning with Databricks

Handling missing values

Real-life data is far from perfect, and cases of having missing values are really common. The mechanisms in which the data has become unavailable are really important to come up with a good imputation strategy. We call imputation the process in which we deal with values that are missing in our data, which in most contexts are represented as NaN values. One of the most important aspects of this is to know which values are missing:

  1. In the following code example, we will show how we can find out which columns have missing or null values by summing up all the Boolean output of the Spark isNull method by casting this Boolean output to integers:
    from pyspark.sql.functions import col, sum*(sum(col(c).isNull().cast("int")).alias(c) for c in df.columns)).show()
  2. Another alternative would be to use the output of the Spark data frame describe method to filter out the count of missing values in each column and, finally, subtracting the count...