Review Databricks Spark job execution overhead tools by following the path of multiple job runs through the system.
- [Tutor] As we take a look at the overhead related to jobs,…we need to understand how jobs are defined…in Databricks Azure.…Spark itself defines jobs as one or more tasks that's run on…node in a distributed compute cluster.…So it's called a Spark job.…So we'll take a look at the overhead related to a Spark job…in this section.…Databricks defines a job as a scheduled Spark job execution.…And we're going to take a look at scheduling Spark jobs…as Databricks jobs later in this course.…
So it also relates to a type of cluster…which is called a job cluster that spins up and down…on the run of the Databricks job.…It is also related to the API for scripting Databricks jobs.…So we're going to start by looking at Spark jobs…and as a review of Spark job execution.…Let's look at the major steps.…The first step is to load the data into RDD…or higher level objects.…So that each of the nodes can perform operations on them.…
The operations can be typical data operations…such as join groupBy or filter.…Once the operations are called, then the cluster DAG…
- Business scenarios for Apache Spark
- Setting up a cluster
- Using Python, R, and Scala notebooks
- Scaling Azure Databricks workflows
- Data pipelines with Azure Databricks
- Machine learning architectures
- Using Azure Databricks for data warehousing
Skill Level Intermediate
1. Big Data on Azure Databricks
2. Core Azure Databricks Workloads
Use a notebook with scikit-learn11m 29s
3. Scaling Azure Databricks Workloads
4. Data Pipelines with Azure Databricks
5. Machine Learning Architectures
Next steps1m 1s
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