Delta Lake with Apache Spark using Scala
Delta Lake with Apache Spark using Scala, available at $39.99, has an average rating of 2.95, with 53 lectures, based on 46 reviews, and has 2285 subscribers.
You will learn about 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! This course is ideal for individuals who are Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist, Data Analyst, Analyst It is particularly useful for Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist, Data Analyst, Analyst.
Enroll now: Delta Lake with Apache Spark using Scala
Summary
Title: Delta Lake with Apache Spark using Scala
Price: $39.99
Average Rating: 2.95
Number of Lectures: 53
Number of Published Lectures: 53
Number of Curriculum Items: 53
Number of Published Curriculum Objects: 53
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will 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!
Who Should Attend
- Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist, Data Analyst, Analyst
Target Audiences
- Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist, Data Analyst, Analyst
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 Sparkto 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 DataFrameframework 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
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Introduction to Delta Lake
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Introduction to Data Lake
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Key Features of Delta Lake
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Introduction to Spark
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Free Account creation in Databricks
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Provisioning a Spark Cluster
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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.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Course Introduction
Lecture 2: Introduction to Delta Lake
Lecture 3: Introduction to Data Lake
Lecture 4: Key Features of Delta Lake
Lecture 5: Elements of Delta Lake
Lecture 6: Introduction to Spark
Lecture 7: (Old) Free Account creation in Databricks
Lecture 8: (New) Free Account creation in Databricks
Lecture 9: Tips to Improve Your Course Taking Experience
Lecture 10: Provisioning a Spark Cluster
Lecture 11: Basics about notebooks
Lecture 12: Dataframes
Lecture 13: (Hands On) Create a Delta table
Lecture 14: (Hands On) Write into a Delta table
Lecture 15: (Hands On) Read a table
Lecture 16: Schema validation
Lecture 17: (Hands On) Update table schema
Lecture 18: Table Metadata
Lecture 19: Delete from a table
Lecture 20: Update a Table
Lecture 21: Vacuum
Lecture 22: History
Lecture 23: Concurrency Control
Lecture 24: Optimistic concurrency control
Lecture 25: Migrate Workloads to Delta Lake
Lecture 26: Optimize Performance with File Management
Lecture 27: Auto Optimize
Lecture 28: Optimize Performance with Caching
Lecture 29: Delta and Apache Spark caching
Lecture 30: Cache a subset of the data
Lecture 31: Isolation Levels
Lecture 32: Best Practices
Lecture 33: FAQ (Interview Question on Optimization) 1
Lecture 34: FAQ (Interview Question on Optimization) 2
Lecture 35: FAQ (Interview Question on Optimization) 3
Lecture 36: FAQ (Interview Question on Auto Optimize) 4
Lecture 37: FAQ (Interview Question on Auto Optimize) 5
Lecture 38: FAQ (Interview Question) 6
Lecture 39: FAQ (Interview Question) 7
Lecture 40: FAQ (Interview Question) 8
Lecture 41: FAQ (Interview Question) 9
Lecture 42: FAQ (Interview Question) 10
Lecture 43: FAQ (Interview Question) 11
Lecture 44: FAQ (Interview Question) 12
Lecture 45: FAQ (Interview Question) 13
Lecture 46: FAQ (Interview Question) 14
Lecture 47: FAQ (Interview Question) 15
Lecture 48: FAQ (Interview Question) 16
Lecture 49: FAQ (Interview Question) 17
Lecture 50: FAQ (Interview Question) 18
Lecture 51: FAQ (Interview Question) 19
Lecture 52: Important Lecture
Lecture 53: Bonus Lecture
Instructors
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Bigdata Engineer
Bigdata Engineer
Rating Distribution
- 1 stars: 11 votes
- 2 stars: 10 votes
- 3 stars: 13 votes
- 4 stars: 8 votes
- 5 stars: 4 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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