1

Step 1

I. Gentle Overview of Big Data and Spark

2

Step 2

II. Structured APIs—DataFrames, SQL, and Datasets

3

Step 3

III. Low-Level APIs

4

Step 4

IV. Production Applications

5

Step 5

V. Streaming

6

Step 6

VI. Advanced Analytics and Machine Learning

7

Step 7

VII. Ecosystem

1

Step 1

I. Gentle Overview of Big Data and Spark

2

Step 2

II. Structured APIs—DataFrames, SQL, and Datasets

3

Step 3

III. Low-Level APIs

4

Step 4

IV. Production Applications

5

Step 5

V. Streaming

6

Step 6

VI. Advanced Analytics and Machine Learning

7

Step 7

VII. Ecosystem

07 June 2020
Details Briefly

Goal abandoned

The author does not write in the goal 4 years 5 months 13 days

Goal author

dzmt

Belarus, Минск

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Equipment & Technologies

Spark: The Definitive Guide

Работаю в команде поддержки проекта, написанного много лет назад при помощи Hadoop. Много чего узнал за это время, но пришло час двигаться дальше. Моя основная цель - поставить этот проект на рельсы Spark.

Это книга будет отправной точкой в повышении своей компетенции.

 Goal Accomplishment Criteria

Проработать книгу Spark: The Definitive Guide.

  1. I. Gentle Overview of Big Data and Spark

    1. What Is Apache Spark?

    2. A Gentle Introduction to Spark

    3. A Tour of Spark’s Toolset

  2. II. Structured APIs—DataFrames, SQL, and Datasets

    1. Structured API Overview

    2. Basic Structured Operations

    3. Working with Different Types of Data

    4. Aggregations

    5. Joins

    6. Data Sources

    7. Spark SQL

    8. Datasets

  3. III. Low-Level APIs

    1. Resilient Distributed Datasets (RDDs)

    2. Advanced RDDs

    3. Distributed Shared Variables

  4. IV. Production Applications

    1. How Spark Runs on a Cluster

    2. Developing Spark Applications

    3. Deploying Spark

    4. Monitoring and Debugging

    5. Performance Tuning

  5. V. Streaming

    1. Stream Processing Fundamentals

    2. Structured Streaming Basics

    3. Event-Time and Stateful Processing

    4. Structured Streaming in Production

  6. VI. Advanced Analytics and Machine Learning

    1. Advanced Analytics and Machine Learning Overview

    2. Preprocessing and Feature Engineering

    3. Classification

    4. Regression

    5. Recommendation

    6. Unsupervised Learning

    7. Graph Analytics

    8. Deep Learning

  7. VII. Ecosystem

    1. Language Specifics: Python (PySpark) and R (SparkR and sparklyr)

    2. Ecosystem and Community

  • 731
  • 07 June 2020, 17:13

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