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Autor Tópico: Delta Lake with Apache Spark using Scala  (Lida 67 vezes)

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Delta Lake with Apache Spark using Scala
« em: 25 de Outubro de 2020, 09:35 »

Delta Lake with Apache Spark using Scala
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 678 MB
Genre: eLearning Video | Duration: 50 lectures (1 hour, 56 mins) | Language: English

 Delta Lake with Apache Spark using Scala on Databricks platform

What you'll learn

    You will be able to learn Delta Lake with Apache Spark in few hours
    Basics to Advance Level of Knowledge about Delta Lake
    Hands on practice with Delta Lake
    You will Learn Delta Lake with Apache Spark using Scala on DataBricks Platform
    Learn how to leverage the power of Delta Lake with a Spark Environment!
    Learn about the DataBricks Platform!

Requirements

    Apache Spark and Scala and SQL basic knowledge is necessary for this course

Description

You will Learn Delta Lake with Apache Spark using Scala on DataBricks Platform

Learn the latest Big Data Technology - Spark! And learn to use it with one of the most popular programming languages, Scala!

One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!

Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 3.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!

Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of your existing data lake and is fully compatible with Apache Spark APIs.

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

Topics Included in the Courses

    Introduction to Delta Lake

    Introduction to Data Lake

    Key Features of Delta Lake

    Introduction to Spark

    Free Account creation in Databricks

    Provisioning a Spark Cluster

    Basics about notebooks

    Dataframes

    Create a table

    Write a table

    Read a table

    Schema validation

    Update table schema

    Table Metadata

    Delete from a table

    Update a Table

    Vacuum

    History

    Concurrency Control

    Optimistic concurrency control

    Migrate Workloads to Delta Lake

    Optimize Performance with File Management

    Auto Optimize

    Optimize Performance with Caching

    Delta and Apache Spark caching

    Cache a subset of the data

    Isolation Levels

    Best Practices

    Frequently Asked Question in Interview

About Databricks:

Databricks lets you start writing Spark code instantly so you can focus on your data problems.

Who this course is for:

    Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist, Data Analyst, Analyst

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