Training Apache Spark

Apache Spark adalah analytics engine yang powerful di desain untuk pemrosesan data skala besar, memilki kemampuan untuk mendukung batch processing, real-time streaming, SQL queries, machine learning, dan graph processing. untuk mencapai potensial penuh dari Apache Spark, program training yang bagus bisa membantu pemahaman user dan meningkatkan skillnya secara signifikan

Tujuan Training

  1. Memahami Arsitektur Apache Spark: mendapatkan pemahaman penuh mengenai komponen dan arsitektur Apache Spark.
  2. Mendapatkan Pengalaman Langsung menggunakan Apache Spark: dengan menerapkan apache Spark di dalam skenario real life dengan training secara langsung menggunakan apache spark
  3. Advanced Analytics: belajar melakukan data analysis yang complex menggunakan apache spark built-in library.
  4. Integration with Ecosystems: Understand how Spark integrates with various data storage and processing systems.

Syllabus Training 3 Hari

Day 1: Perkenalan dengan Apache Spark dan Konsep intinya

Sesi Pagi hari:

  • Sambutan dan Pengantar
    • Pengenalan Big Data dan Fungsi Apache Spark
    • Tujuan dari Training Apache Spark
  • Dasar Apache Apache Spark
    • Apa itu Apache Spark?
    • Sejarah dan Evolusi Apache Spark
    • Spark vs. Hadoop MapReduce
  • Arsitektur Apache Spark
    • Komponen Apache Spark: Driver, Executors, Cluster Manager
    • Resilient Distributed Datasets (RDDs)
    • Transformations and Actions

Sesi Siang hari:

  • Konfigurasi Apache Spark
    • Instalasi dan Konfigurasi
    • Running Apache Spark di local mode
    • Pengeenalan dengan Spark Shell
  • Pengenalan dengan RDDs
    • Creating RDDs
    • RDD operations: Transformations and Actions
    • RDD persistence
  • Latihan Hands-on Lab
    • Setting up Spark environment
    • Creating and manipulating RDDs

Day 2: Advanced Spark Programming and SQL

Morning Session:

  • Spark SQL and DataFrames
    • Introduction to Spark SQL
    • DataFrames and Datasets
    • Working with Spark SQL: Queries and DataFrames API
  • Spark SQL Internals
    • Catalyst optimizer
    • Tungsten execution engine

Afternoon Session:

  • Hands-on Lab
    • Querying data with Spark SQL
    • Working with DataFrames and Datasets
  • Spark Streaming
    • Introduction to Spark Streaming
    • DStreams and operations
    • Stateful transformations and window operations
  • Hands-on Lab
    • Setting up Spark Streaming
    • Processing real-time data streams

Day 3: Machine Learning, Graph Processing, and Integration

Morning Session:

  • Machine Learning with Spark
    • Introduction to MLlib
    • Common algorithms: Classification, Regression, Clustering
    • Building and evaluating machine learning models
  • Graph Processing with Spark
    • Introduction to GraphX
    • Creating and manipulating graphs
    • Graph algorithms

Afternoon Session:

  • Hands-on Lab
    • Building and evaluating a machine learning model
    • Graph processing tasks with GraphX
  • Integration with Ecosystems
    • Connecting Spark to Hadoop, HDFS, and Hive
    • Working with external data sources: S3, Cassandra, HBase
  • Performance Tuning and Best Practices
    • Spark configuration and tuning
    • Monitoring and debugging
    • Best practices for production deployment
  • Final Project and Q&A
    • Real-world case study
    • Applying Spark to a comprehensive project
    • Open Q&A session


This 3-day Apache Spark training program is designed to provide a comprehensive understanding of Spark’s capabilities and applications. By the end of the training, participants will have the knowledge and hands-on experience needed to apply Spark to various data processing and analytics tasks in real-world scenarios. This course is suitable for data engineers, data scientists, and big data developers who want to deepen their expertise in Spark.

Further Resources

These resources can provide additional learning materials and certifications to further enhance your understanding and skills in Apache Spark.


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  •  Training Spark 2024
  •  Training Big Data
  •  Training Spark Programming