Data Engineering using Databricks on AWS and Azure
Data Engineering using Databricks on AWS and Azure
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.65 GB | Duration: 13h 50m
What you'll learn
Data Engineering leveraging Databricks features
Databricks CLI to manage files, Data Engineering jobs and clusters for Data Engineering Pipelines
Deploying Data Engineering applications developed using PySpark on job clusters
Deploying Data Engineering applications developed using PySpark using Notebooks on job clusters
Perform CRUD Operations leveraging Delta Lake using Spark SQL for Data Engineering Applications or Pipelines
Perform CRUD Operations leveraging Delta Lake using Pyspark for Data Engineering Applications or Pipelines
Setting up development environment to develop Data Engineering applications using Databricks
Building Data Engineering Pipelines using Spark Structured Streaming on Databricks Clusters
Incremental File Processing using Spark Structured Streaming leveraging Databricks Auto Loader cloudFiles
Overview of Auto Loader cloudFiles File Discovery Modes - Directory Listing and File Notifications
Differences between Auto Loader cloudFiles File Discovery Modes - Directory Listing and File Notifications
Differences between traditional Spark Structured Streaming and leveraging Databricks Auto Loader cloudFiles for incremental file processing.
Requirements
Programming experience using Python
Data Engineering experience using Spark
Ability to write and interpret SQL Queries
This course is ideal for experienced data engineers to add Databricks as one of the key skill as part of the profile
Description
As part of this course, you will learn all the Data Engineering using cloud platform-agnostic technology called Databricks.
About Data Engineering
Data Engineering is nothing but processing the data depending upon our downstream needs. We need to build different pipelines such as Batch Pipelines, Streaming Pipelines, etc as part of Data Engineering. All roles related to Data Processing are consolidated under Data Engineering. Conventionally, they are known as ETL Development, Data Warehouse Development, etc.
About Databricks
Databricks is the most popular cloud platform-agnostic data engineering tech stack. They are the committers of the Apache Spark project. Databricks run time provide Spark leveraging the elasticity of the cloud. With Databricks, you pay for what you use. Over a period of time, they came up with an idea of Lakehouse by providing all the features that are required for traditional BI as well as AI & ML. Here are some of the core features of Databricks.
Spark - Distributed Computing
Delta Lake - Perform CRUD Operations. It is primarily used to build capabilities such as inserting, updating, and deleting the data from files in Data Lake.
cloudFiles - Get the files in an incremental fashion in the most efficient way leveraging cloud features.
Databricks SQL - A Photon-based interface that is fine-tuned for running queries submitted for reporting and visualization by reporting tools. It is also used for Ad-hoc Analysis.
Course Details
As part of this course, you will be learning Data Engineering using Databricks.
Getting Started with Databricks
Setup Local Development Environment to develop Data Engineering Applications using Databricks
Using Databricks CLI to manage files, jobs, clusters, etc related to Data Engineering Applications
Spark Application Development Cycle to build Data Engineering Applications
Databricks Jobs and Clusters
Deploy and Run Data Engineering Jobs on Databricks Job Clusters as Python Application
Deploy and Run Data Engineering Jobs on Job Cluster using Notebooks
Deep Dive into Delta Lake using Dataframes
Deep Dive into Delta Lake using Spark SQL
Building Data Engineering Pipelines using Spark Structured Streaming on Databricks Clusters
Incremental File Processing using Spark Structured Streaming leveraging Databricks Auto Loader cloudFiles
Overview of Auto Loader cloudFiles File Discovery Modes - Directory Listing and File Notifications
Differences between Auto Loader cloudFiles File Discovery Modes - Directory Listing and File Notifications
Differences between traditional Spark Structured Streaming and leveraging Databricks Auto Loader cloudFiles for incremental file processing.
Overview of Databricks SQL for Data Analysis and reporting.
We will be adding few more modules related to Pyspark, Spark with Scala, Spark SQL, Streaming Pipelines in the coming weeks.
Desired Audience
Here is the desired audience for this advanced course.
Experienced application developers to gain expertise related to Data Engineering with prior knowledge and experience of Spark.
Experienced Data Engineers to gain enough skills to add Databricks to their profile.
Testers to improve their testing capabilities related to Data Engineering applications using Databricks.
Prerequisites
Logistics
Computer with decent configuration (At least 4 GB RAM, however 8 GB is highly desired)
Dual Core is required and Quad-Core is highly desired
Chrome Browser
High-Speed Internet
Valid AWS Account
Valid Databricks Account (free Databricks Account is not sufficient)
Experience as Data Engineer especially using Apache Spark
Knowledge about some of the cloud concepts such as storage, users, roles, etc.
Associated Costs
As part of the training, you will only get the material. You need to practice on your own or corporate cloud account and Databricks Account.
You need to take care of the associated AWS or Azure costs.
You need to take care of the associated Databricks costs.
Training Approach
Here are the details related to the training approach.
It is self-paced with reference material, code snippets, and videos provided as part of Udemy.
One needs to sign up for their own Databricks environment to practice all the core features of Databricks.
We would recommend completing 2 modules every week by spending 4 to 5 hours per week.
It is highly recommended to take care of all the tasks so that one can get real experience of Databricks.
Support will be provided through Udemy Q&A.
Who this course is for
Beginner or Intermediate Data Engineers who want to learn Databricks for Data Engineering
Intermediate Application Engineers who want to explore Data Engineering using Databricks
Data and Analytics Engineers who want to learn Data Engineering using Databricks
Testers who want to learn Databricks to test Data Engineering applications built using Databricks
Download from RapidGator
Download from NitroFlare
Data Engineering using Databricks on AWS and Azure
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.65 GB | Duration: 13h 50m
What you'll learn
Data Engineering leveraging Databricks features
Databricks CLI to manage files, Data Engineering jobs and clusters for Data Engineering Pipelines
Deploying Data Engineering applications developed using PySpark on job clusters
Deploying Data Engineering applications developed using PySpark using Notebooks on job clusters
Perform CRUD Operations leveraging Delta Lake using Spark SQL for Data Engineering Applications or Pipelines
Perform CRUD Operations leveraging Delta Lake using Pyspark for Data Engineering Applications or Pipelines
Setting up development environment to develop Data Engineering applications using Databricks
Building Data Engineering Pipelines using Spark Structured Streaming on Databricks Clusters
Incremental File Processing using Spark Structured Streaming leveraging Databricks Auto Loader cloudFiles
Overview of Auto Loader cloudFiles File Discovery Modes - Directory Listing and File Notifications
Differences between Auto Loader cloudFiles File Discovery Modes - Directory Listing and File Notifications
Differences between traditional Spark Structured Streaming and leveraging Databricks Auto Loader cloudFiles for incremental file processing.
Requirements
Programming experience using Python
Data Engineering experience using Spark
Ability to write and interpret SQL Queries
This course is ideal for experienced data engineers to add Databricks as one of the key skill as part of the profile
Description
As part of this course, you will learn all the Data Engineering using cloud platform-agnostic technology called Databricks.
About Data Engineering
Data Engineering is nothing but processing the data depending upon our downstream needs. We need to build different pipelines such as Batch Pipelines, Streaming Pipelines, etc as part of Data Engineering. All roles related to Data Processing are consolidated under Data Engineering. Conventionally, they are known as ETL Development, Data Warehouse Development, etc.
About Databricks
Databricks is the most popular cloud platform-agnostic data engineering tech stack. They are the committers of the Apache Spark project. Databricks run time provide Spark leveraging the elasticity of the cloud. With Databricks, you pay for what you use. Over a period of time, they came up with an idea of Lakehouse by providing all the features that are required for traditional BI as well as AI & ML. Here are some of the core features of Databricks.
Spark - Distributed Computing
Delta Lake - Perform CRUD Operations. It is primarily used to build capabilities such as inserting, updating, and deleting the data from files in Data Lake.
cloudFiles - Get the files in an incremental fashion in the most efficient way leveraging cloud features.
Databricks SQL - A Photon-based interface that is fine-tuned for running queries submitted for reporting and visualization by reporting tools. It is also used for Ad-hoc Analysis.
Course Details
As part of this course, you will be learning Data Engineering using Databricks.
Getting Started with Databricks
Setup Local Development Environment to develop Data Engineering Applications using Databricks
Using Databricks CLI to manage files, jobs, clusters, etc related to Data Engineering Applications
Spark Application Development Cycle to build Data Engineering Applications
Databricks Jobs and Clusters
Deploy and Run Data Engineering Jobs on Databricks Job Clusters as Python Application
Deploy and Run Data Engineering Jobs on Job Cluster using Notebooks
Deep Dive into Delta Lake using Dataframes
Deep Dive into Delta Lake using Spark SQL
Building Data Engineering Pipelines using Spark Structured Streaming on Databricks Clusters
Incremental File Processing using Spark Structured Streaming leveraging Databricks Auto Loader cloudFiles
Overview of Auto Loader cloudFiles File Discovery Modes - Directory Listing and File Notifications
Differences between Auto Loader cloudFiles File Discovery Modes - Directory Listing and File Notifications
Differences between traditional Spark Structured Streaming and leveraging Databricks Auto Loader cloudFiles for incremental file processing.
Overview of Databricks SQL for Data Analysis and reporting.
We will be adding few more modules related to Pyspark, Spark with Scala, Spark SQL, Streaming Pipelines in the coming weeks.
Desired Audience
Here is the desired audience for this advanced course.
Experienced application developers to gain expertise related to Data Engineering with prior knowledge and experience of Spark.
Experienced Data Engineers to gain enough skills to add Databricks to their profile.
Testers to improve their testing capabilities related to Data Engineering applications using Databricks.
Prerequisites
Logistics
Computer with decent configuration (At least 4 GB RAM, however 8 GB is highly desired)
Dual Core is required and Quad-Core is highly desired
Chrome Browser
High-Speed Internet
Valid AWS Account
Valid Databricks Account (free Databricks Account is not sufficient)
Experience as Data Engineer especially using Apache Spark
Knowledge about some of the cloud concepts such as storage, users, roles, etc.
Associated Costs
As part of the training, you will only get the material. You need to practice on your own or corporate cloud account and Databricks Account.
You need to take care of the associated AWS or Azure costs.
You need to take care of the associated Databricks costs.
Training Approach
Here are the details related to the training approach.
It is self-paced with reference material, code snippets, and videos provided as part of Udemy.
One needs to sign up for their own Databricks environment to practice all the core features of Databricks.
We would recommend completing 2 modules every week by spending 4 to 5 hours per week.
It is highly recommended to take care of all the tasks so that one can get real experience of Databricks.
Support will be provided through Udemy Q&A.
Who this course is for
Beginner or Intermediate Data Engineers who want to learn Databricks for Data Engineering
Intermediate Application Engineers who want to explore Data Engineering using Databricks
Data and Analytics Engineers who want to learn Data Engineering using Databricks
Testers who want to learn Databricks to test Data Engineering applications built using Databricks
Download from RapidGator
Download from NitroFlare
lease go to the third floor now. B:Okay, I understand.
