PySpark Training and Certification in Thimphu, Bhutan

PySpark training introduces learners to the power of Apache Spark for processing large-scale data using Python. It covers core concepts such as Resilient Distributed Datasets (RDDs), DataFrames, and SparkSQL, providing the foundation for data manipulation and analysis. Participants gain hands-on experience in working with PySpark for both batch and real-time data processing, leveraging its scalability and speed. The training also explores advanced topics like machine learning with MLlib, graph processing with GraphX, and performance optimization techniques. By completing this course, professionals can apply PySpark to handle complex big data tasks and build efficient data pipelines for analytics.

Introduction to PySpark and Basic Operations

• Introduction to Big Data and Apache Spark:

• What is Big Data?

• Overview of Apache Spark and its ecosystem.

• Components of Spark: SparkCore, Spark SQL, Spark Streaming, MLlib, and GraphX.

• Benefits of using Spark for big data processing.

• Hands-on Exercise

• Understanding RDDs (Resilient Distributed Datasets):

• What is an RDD?

• Creating RDDs from existing data sources.

• Basic RDD operations: map(), filter(), reduce(), and collect().

• Transformations and actions in RDDs.

• Hands-on Exercise

• Introduction to DataFrames and Spark SQL:

• What is a DataFrame?

• Differences between RDD and DataFrame.

• Loading data into Spark DataFrames.

• Performing basic operations on DataFrames (select, filter, groupBy).

• Hands-on Exercise

Data Transformation and Analysis

• Data Processing with PySpark DataFrames:

• Using DataFrame APIs for data manipulation.

• Data cleaning and preprocessing using PySpark.

• Handling missing values, filtering, and data aggregation.

• Hands-on Exercise

• Advanced DataFrame Operations:

• Joins and aggregations with DataFrames.

• Working with nested data and complex data structures (arrays, maps).

• Window functions and ranking operations.

• Hands-on Exercise

• Working with PySpark SQL:

• Writing SQL queries in PySpark using the spark.sql() method.

• Understanding and working with temporary views and permanent tables.

• Using SQL functions in PySpark.

• Hands-on Exercise

Spark Jobs, Performance Optimization, and Advanced Topics

• Running Spark Jobs:

• Spark job execution model: stages, tasks, and DAG (Directed Acyclic Graph).

• Running Spark jobs locally vs on a cluster.

• Submitting jobs to a Spark cluster using Spark-submit.

• Hands-on Exercise

• Performance Tuning and Optimization in PySpark:

• Understanding Spark job execution and performance bottlenecks.

• Optimizing Spark jobs using caching, partitioning, and parallelism.

• Tuning Spark configurations (e.g., memory, shuffle partitions).

• Hands-on Exercise

• Introduction to Spark Streaming:

• What is Spark Streaming?

• Processing real-time data streams with PySpark.

• Working with DStreams and window operations.

• Hands-on Exercise

• Integrating PySpark with Other Big Data Tools:

• Using PySpark with Hadoop and HDFS.

• Integration with Hive and other big data tools.

• Hands-on Exercise