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Deep-Dive in DeltaLake using PySpark in Databricks

  • Development
  • Jan 08, 2025
SynopsisDeep-Dive in DeltaLake using PySpark in Databricks, available...
Deep-Dive in DeltaLake using PySpark Databricks  No.1

Deep-Dive in DeltaLake using PySpark in Databricks, available at $19.99, has an average rating of 4.17, with 21 lectures, based on 6 reviews, and has 97 subscribers.

You will learn about Understand the power of Delta table in Apache Spark Build Delta Lakehouse using Databricks Explore advanced features of Delta Lake, such as schema evolution and time travel Learn to use Databricks effectively for data processing and analysis Understand End to End use of Delta tables in Apache Spark Hands on practice with Delta Lake Learn how to leverage the power of Delta Lake with a Spark Environment! This course is ideal for individuals who are Data Engineer who wants to switch to Azure Big Data Engineer or Beginner Apache Spark Developer or Data Analyst It is particularly useful for Data Engineer who wants to switch to Azure Big Data Engineer or Beginner Apache Spark Developer or Data Analyst.

Enroll now: Deep-Dive in DeltaLake using PySpark in Databricks

Summary

Title: Deep-Dive in DeltaLake using PySpark in Databricks

Price: $19.99

Average Rating: 4.17

Number of Lectures: 21

Number of Published Lectures: 21

Number of Curriculum Items: 21

Number of Published Curriculum Objects: 21

Original Price: ?799

Quality Status: approved

Status: Live

What You Will Learn

  • Understand the power of Delta table in Apache Spark
  • Build Delta Lakehouse using Databricks
  • Explore advanced features of Delta Lake, such as schema evolution and time travel
  • Learn to use Databricks effectively for data processing and analysis
  • Understand End to End use of Delta tables in Apache Spark
  • Hands on practice with Delta Lake
  • Learn how to leverage the power of Delta Lake with a Spark Environment!
  • Who Should Attend

  • Data Engineer who wants to switch to Azure Big Data Engineer
  • Beginner Apache Spark Developer
  • Data Analyst
  • Target Audiences

  • Data Engineer who wants to switch to Azure Big Data Engineer
  • Beginner Apache Spark Developer
  • Data Analyst
  • This is an immersive course that provides a comprehensive understanding of Delta Lake, a powerful open-source storage layer for big data processing, and how to leverage it using Databricks. With hands-on experience and a step-by-step approach, this course explores the core concepts, architecture, and best practices of Delta Lake. Throughout the course, you will gain valuable insights into data lakes, data ingestion, data management, and data quality. You will learn the advanced capabilities of Delta Lake, including schema evolution, transactional writes, asset management, and time travel. Moreover, this course covers how to integrate Delta Lake with Databricks, a cloud-based platform for data engineering and analytics. You will witness the seamless integration of Delta Lake with Databricks, empowering you to perform analytics, data engineering, and machine learning projects efficiently using these technologies. To enhance your learning experience, this course also includes an end-to-end project where you will apply the acquired knowledge to build a real-world data solution. You will design a data pipeline, perform data ingestion, transform data using Delta Lake, conduct analytics, and visualize the results. This hands-on project will solidify your understanding and provide you with practical skills applicable to various data-driven projects. By the end of this course, you will be equipped with the expertise to leverage the power of Delta Lake using Databricks and successfully implement scalable and reliable data solutions. Whether you are a data engineer, data scientist, or data analyst, this course offers immense value in advancing your big data skills and accelerating your career in the field of data engineering and analytics.

    Course Curriculum

    Chapter 1: Introduction

    Lecture 1: Introduction and Syllabus

    Lecture 2: Architecture and Introduction of Delta-Lake

    Lecture 3: How Delta table differs from Normal tables

    Lecture 4: Create a Delta table

    Lecture 5: Generate Column in Delta table

    Lecture 6: Read a Delta table

    Lecture 7: Write to a Delta table

    Lecture 8: ReplaceWhere while writing to Delta table

    Lecture 9: Delete a Delta table

    Lecture 10: Update a Delta table

    Lecture 11: Upsert or Merge Statment in Delta tables

    Lecture 12: Understand transactional logs in _delta_log folder

    Lecture 13: How to go to time travel in Delta table using History

    Lecture 14: Restore Delta table to previous version

    Lecture 15: How to add constraint in Delta table

    Lecture 16: How to add user meta data information in a Delta table

    Lecture 17: Schema evolution and enforcement in Delta table

    Lecture 18: Shallow and Deep Clone of Delta table

    Lecture 19: Addition on Deep and Shallow Clone of Delta table

    Lecture 20: How to enable Change Data Feed in Delta table

    Lecture 21: Reduce small file issue using optimize

    Instructors

  • Deep-Dive in DeltaLake using PySpark Databricks  No.2
    Sagar Prajapati
    Instructor at Udemy | Mentor at GeekCoders Platform
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  • 2 stars: 0 votes
  • 3 stars: 2 votes
  • 4 stars: 2 votes
  • 5 stars: 2 votes
  • Frequently Asked Questions

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