BookFronter
Learning Spark by Jules Damji

Learning Spark

Lightning-fast Data Analytics

Jules Damji, Brooke Wenig, Tathagata Das

O'Reilly Media · Print & ebook · August 25, 2020

Data is bigger, arrives faster, and comes in a variety of formatsâ??and it all needs to be processed at scale for analytics or machine learning.

Machine LearningData MiningPython Programming
Buy on Amazon

We earn from qualifying purchases.

Who it’s for
Data engineers and data scientists learning Apache Spark
Themes
Spark data analytics, Machine learning, Data pipelines
Genres
Data Mining, Machine Learning, Python Programming

Nearest books

Matched by what the books are about, then ordered by what their readers also loved.

  1. Data Pipelines Pocket ReferenceJames Densmore61% matchShares Data Mining
  2. JSON at WorkTom Marrs50% match
  3. Data Analysis With Python and PysparkJonathan Rioux76% matchShares Data Mining, Machine Learning and Python Programming
  4. Google Bigquery: the Definitive GuideValliappa Lakshmanan, Jordan Tigani57% match
  5. Kafka Streams in Action, Second EditionBill Bejeck57% match
  6. Data Structures and Algorithms in JavaMichael T. Goodrich, Roberto Tamassia49% match
  7. Hands-on Machine Learning With Scikit-learn, Keras, and TensorflowAurélien Géron58% matchShares Machine Learning and Python Programming
  8. Fluent PythonLuciano Ramalho50% matchShares Python Programming

For younger readers: The CS Detective, Basher Science Mini: Artificial Intelligence

Start with three books, and this one can be the first.

Members get a new edition every morning: a route out from their books to essays, research and talks, with the reason for each.

Start with this book

About this book

Data is bigger, arrives faster, and comes in a variety of formatsâ??and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters.

Continue reading

Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, youâ??ll be able to: - Learn Python, SQL, Scala, or Java high-level Structured APIs - Understand Spark operations and SQL Engine - Inspect, tune, and debug Spark operations with Spark configurations and Spark UI - Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka - Perform analytics on batch and streaming data using Structured Streaming - Build reliable data pipelines with open source Delta Lake and Spark - Develop machine learning pipelines with MLlib and productionize models using MLflow

Who it’s for

  • Data engineers and data scientists learning Apache Spark
  • Readers seeking hands-on Spark analytics and machine learning guidance

Where it sits

Machine Learning79%
Data Mining62%
Python Programming44%

Percentages are BookFrontier’s reading of the book.

Publisher categories

  • Productivity Software
  • Data Science
  • Java Programming
  • Open Source Development
Show all 7 publisher categories
  • Math & Stats Software
  • Mathematical Analysis
  • Complex Analysis
Authors
Jules Damji, Brooke Wenig, Tathagata Das
Publisher
O'Reilly Media
Published
August 25, 2020
Format
Print & ebook
Theme
Spark data analytics · Machine learning · Data pipelines
Reading lane
Productivity Software