Apache Spark Training
Level
BeginnerDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedApache Spark Training
The Apache Spark Training is an intensive two-day course focused on the practical application of this popular framework for processing large datasets. The training program is designed so that 80% of the time is dedicated to hands-on workshops, and 20% to theory. Participants will gain solid theoretical foundations and practical skills in using Apache Spark, working with real data and solving practical problems.
What You Will Learn
- How to install and configure Apache Spark in various environments.
- How to process and analyze data using RDDs, DataFrames, and Spark SQL.
- How to optimize queries and manage resources in Apache Spark.
- How to deploy Apache Spark applications in a production environment.
Required Technical Skills
- Basic knowledge of programming in Python or Scala.
- Basic understanding of data processing.
- Ability to work in a Unix/Linux environment.
Who is this training for?
Developers and data engineers who want to expand their skills with Apache Spark.-
Data scientists and data analysts aiming to process large datasets efficiently.
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IT and big data specialists looking to leverage Apache Spark in their projects.
Training Program
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Day 1: Introduction to Apache Spark and Data Processing Basics
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Introduction to Apache Spark
- History and development of Apache Spark
- Architecture and main components (RDD, DataFrame, Spark SQL)
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Installation and Environment Configuration
- Installing Apache Spark and dependencies
- Configuring the working environment (Standalone, Hadoop, AWS)
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Basic Data Processing in Apache Spark
- Working with files: JSON, CSV, XML, TXT, Parquet, AVRO
- Transformations and Actions (lazy evaluation)
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Day 2: Advanced Techniques and Practical Applications
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Advanced Data Processing with DataFrame and Spark SQL
- Creating and managing DataFrames
- Querying large datasets with Spark SQL
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Data Transformations
- Sorting, grouping, and filtering data
- Transformations using map, flatMap, and UDF functions
- Window and analytical functions
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Workshop: Data Processing and Analysis
- Implementing operations on DataFrames and SQL queries
- Analyzing large datasets using Spark SQL
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Optimization and Performance Tuning
- Query optimization and Spark performance techniques
- Memory management and resource allocation
- Partitioning and efficient data writing
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Deploying Apache Spark Applications
- Preparing and exporting Spark applications
- Deploying applications in production environments
