Azure Databricks Cookbook

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Azure Databricks Cookbook

Coursera · Intermediate ·🔄 Data Engineering ·2mo ago

About this lesson

The Azure Databricks Cookbook shows you how to work with the latest as well as older versions of Apache Spark and integrate with various Azure resources for orchestrating, deploying, and monitoring big data solutions. You'll use Azure Databricks to build end-to-end solutions and address challenges in securing, productionizing, and monitoring them. This course provides a hands-on approach to mastering Azure Databricks for scalable analytics and data pipeline development. It covers essential skills for working with data in cloud environments, including integration with Azure services and real-time data processing. Designed for professionals looking to enhance their data engineering and analytics capabilities, it offers practical insights and actionable techniques. With a problem solution approach, this book teaches how to create end-end big data solution using data from various sources like batch and streaming and how to version control, deploy the solution to production and monitor the solution. This course is ideal for data engineers, data scientists, and big data professionals with prior experience in Apache Spark and Azure. It helps learners develop advanced skills in cloud-based data analytics and pipeline development.

Original Description

The Azure Databricks Cookbook shows you how to work with the latest as well as older versions of Apache Spark and integrate with various Azure resources for orchestrating, deploying, and monitoring big data solutions. You'll use Azure Databricks to build end-to-end solutions and address challenges in securing, productionizing, and monitoring them. This course provides a hands-on approach to mastering Azure Databricks for scalable analytics and data pipeline development. It covers essential skills for working with data in cloud environments, including integration with Azure services and real-time data processing. Designed for professionals looking to enhance their data engineering and analytics capabilities, it offers practical insights and actionable techniques. With a problem solution approach, this book teaches how to create end-end big data solution using data from various sources like batch and streaming and how to version control, deploy the solution to production and monitor the solution. This course is ideal for data engineers, data scientists, and big data professionals with prior experience in Apache Spark and Azure. It helps learners develop advanced skills in cloud-based data analytics and pipeline development.

Watch on External: Coursera ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

Related Reads

I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer

Learn how to build a production-ready ETL pipeline with Python, Docker, PostgreSQL, and Kestra by thinking like a data engineer

Towards Data Science

JuiceFS Sync for PB-Scale Data Transfers: Resumable Sync, Encryption, and Bandwidth Control

Learn how to efficiently transfer large volumes of data using JuiceFS Sync, which offers resumable sync, encryption, and bandwidth control, ideal for PB-scale data transfers.

How Airflow is using AI to make data engineering more resilient, not more complex

Airflow uses AI to make data engineering more resilient by detecting data drift, resuming failed pipelines, and fixing issues automatically, reducing complexity and improving reliability.

What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?

Learn how to overcome memory bottlenecks in data engineering using Pandas chunking, Dask, and Polars, and why it matters for processing large datasets

Towards Data Science

A Moment Frozen in Time | Arnav Iyengar | TEDxJenks Youth