E-Books

Data Engineering with AWS. Acquire the skills to design and build AWS-based data transformation pipelines like a pro - Second Edition

Gareth Eagar

Data Engineering with AWS Cookbook. A recipe-based approach to help you tackle data engineering problems with AWS services

Trâm Ngoc Pham, Gonzalo Herreros González, Viquar...

Data Engineering with AWS. Learn how to design and build cloud-based data transformation pipelines using AWS

Gareth Eagar

Data Engineering with Azure Databricks. Design, build, and optimize scalable data pipelines and analytics solutions with Azure Databricks

Dmitry Foshin, Dmitry Anoshin, Tonya Chernyshova, Sergii...

Data Engineering with Databricks Cookbook. Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake

Pulkit Chadha

Data Engineering with dbt. A practical guide to building a cloud-based, pragmatic, and dependable data platform with SQL

Roberto Zagni

Data Engineering with Google Cloud Platform. A guide to leveling up as a data engineer by building a scalable data platform with Google Cloud - Second Edition

Adi Wijaya, António Vilares

Data Engineering with Google Cloud Platform. A practical guide to operationalizing scalable data analytics systems on GCP

Adi Wijaya

Data Engineering with Python. Work with massive datasets to design data models and automate data pipelines using Python

Paul Crickard

Data Engineering with Scala and Spark. Build streaming and batch pipelines that process massive amounts of data using Scala

Eric Tome, Rupam Bhattacharjee, David Radford

Data Exploration and Preparation with BigQuery. A practical guide to cleaning, transforming, and analyzing data for business insights

Mike Kahn

Data Forecasting and Segmentation Using Microsoft Excel. Perform data grouping, linear predictions, and time series machine learning statistics without using code

Fernando Roque

Data Governance Handbook. A practical approach to building trust in data

Wendy S. Batchelder

Data Ingestion with Python Cookbook. A practical guide to ingesting, monitoring, and identifying errors in the data ingestion process

Gláucia Esppenchutz

Data Labeling in Machine Learning with Python. Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models

Vijaya Kumar Suda

Data Lake Development with Big Data. Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies

Pradeep Pasupuleti, Beulah Salome Purra