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    Data Engineering using Databricks on AWS and Azure

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    Data Engineering using Databricks on AWS and Azure - Online Course Download
    Data Engineering using Databricks on AWS and Azure - Online Course Download
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    What you'll learn :

    Data Engineering using Databricks features on AWS and Azure

    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 the 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 cloud files
    Overview of AutoLoader cloud files File Discovery Modes – Directory Listing and File Notifications
    Differences between Auto Loader cloud files File Discovery Modes – Directory Listing and File Notifications
    Differences between traditional Spark Structured Streaming and leveraging Databricks Auto Loader cloud files 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 skills 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.
    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 cloud files
    Overview of AutoLoader cloud files File Discovery Modes – Directory Listing and File Notifications
    Differences between Auto Loader cloud files File Discovery Modes – Directory Listing and File Notifications
    Differences between traditional Spark Structured Streaming and leveraging Databricks Auto Loader cloud files for incremental file processing.

    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.
    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
    Last updated 8/2021

    Course Size Details :

    10.5 hours on-demand video
    13 articles
    54 downloadable resources
    Full lifetime access
    Access on mobile and TV
    Certificate of completion

    View Demo

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