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Optimizing Databricks Workloads

You're reading from Optimizing Databricks Workloads

Product type Book

Published in Dec 2021

Publisher Packt

ISBN-13 9781801819077

Pages 230 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Authors (3):

Anirudh Kala

Anshul Bhatnagar

Sarthak Sarbahi

View More author details

Table of Contents (13) Chapters

Preface

Section 1: Introduction to Azure Databricks

Chapter 1: Discovering Databricks

Chapter 2: Batch and Real-Time Processing in Databricks

Chapter 3: Learning about Machine Learning and Graph Processing in Databricks

Section 2: Optimization Techniques

Chapter 4: Managing Spark Clusters

Chapter 5: Big Data Analytics

Chapter 6: Databricks Delta Lake

Chapter 7: Spark Core

Section 3: Real-World Scenarios

Chapter 8: Case Studies

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Learning about Apache Arrow in Pandas

Apache Arrow is an in-memory columnar data format that helps to efficiently store data between clustered Java Virtual Machines (JVMs) and Python processes. This is highly beneficial for data scientists working with Pandas and NumPy in Databricks. Apache Arrow does not produce different results in terms of the data. It is helpful when we are converting Spark DataFrames to Pandas DataFrames, and vice versa. Let's try to better understand the utility of Apache Arrow with an analogy.

Let's say you were traveling to Europe before the establishment of the European Union (EU). To visit 10 countries in 7 days, you would have has to spend some time at every border for passport control, and money would have always been lost due to currency exchange. Similarly, without using Apache Arrow, inefficiencies exist due to serialization and deserialization processes wasting memory and CPU resources (such as converting a Spark DataFrame to a Pandas DataFrame...

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Authors (3)

Anirudh Kala

Anirudh Kala is an expert in machine learning techniques, artificial intelligence, and natural language processing. He has helped multiple organizations to run their large-scale data warehouses with quantitative research, natural language generation, data science exploration, and big data implementation. He has worked in every aspect of data analytics using the Azure data platform. Currently, he works as the director of Celebal Technologies, a data science boutique firm dedicated to large-scale analytics. Anirudh holds a computer engineering degree from the University of Rajasthan and his work history features the likes of IBM and ZS Associates.

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Anshul Bhatnagar

Anshul Bhatnagar is an experienced, hands-on data architect involved in the architecture, design, and implementation of data platform architectures, and distributed systems. He has worked in the IT industry since 2015 in a range of roles such as Hadoop/Spark developer, data engineer, and data architect. He has also worked in many other sectors including energy, media, telecoms, and e-commerce. He is currently working for a data and AI boutique company, Celebal Technologies, in India. He is always keen to hear about new ideas and technologies in the areas of big data and AI, so look him up on LinkedIn to ask questions or just to say hi.

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Sarthak Sarbahi

Sarthak Sarbahi is a certified data engineer and analyst with a wide technical breadth and a deep understanding of Databricks. His background has led him to a variety of cloud data services with an eye toward data warehousing, big data analytics, robust data engineering, data science, and business intelligence. Sarthak graduated with a degree in mechanical engineering.

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