Apache Spark Training and Certification in Astana

Apache Spark Training and Certification in Astana, Kazakhstan

It covers essential concepts such as RDDs, DataFrames, and SparkSQL for efficient data manipulation and querying.

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Apache Spark Training and Certification is a professional training program delivered by ProgNXT in Astana, Kazakhstan. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Apache Spark Training and Certification course with hands-on, instructor-led sessions. Apache Spark training provides a deep understanding of big data processing and analytics using the Spark framework. It covers essential concepts such...

Expert Panel

Designed and delivered by the ProgNXT Big Data Expert Panel — a vetted team of industry professionals with 14+ years of hands-on experience in Hadoop, Apache Spark, Kafka, and distributed data processing

ProgNXT Big Data Expert Panel

Course Code: PRQ96
21 Hrs
Last Updated: April 2026

Course Rating5/5

Apache Spark training provides a deep understanding of big data processing and analytics using the Spark framework. It covers essential concepts such as RDDs, DataFrames, and SparkSQL for efficient data manipulation and querying. Participants gain hands-on experience with Spark's machine learning library (MLlib) and real-time stream processing capabilities through Spark Streaming. The training emphasizes optimizing Spark jobs, managing clusters, and integrating Spark with other big data tools like Hadoop. By completing the course, professionals can harness the full power of Spark to process large-scale data and perform advanced analytics in distributed environments.

Welcome to the official Apache Spark Training and Certification certification program in Astana, Kazakhstan. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Basic knowledge of programming (preferably Python, Java, or Scala).
Understanding of data processing and big data concepts.
Familiarity with distributed systems and Hadoop ecosystem (optional but beneficial).

Upon completion of this course, participants will:

Understand the core concepts of Apache Spark and its ecosystem.
Learn to process large datasets using Spark’s RDDs and DataFrames.
Be able to perform data transformations and actions using Spark SQL.
Gain experience in optimizing and tuning Apache Spark applications.
Learn to use Spark for real-time stream processing.

Spark Fundamentals: Understanding the core components of Apache Spark, such as RDDs, DataFrames, and Spark SQL.
Big Data Processing: Ability to process and analyze large datasets using Spark.
Data Transformation: Skill in performing data transformations using Spark’s API.
Real-Time Stream Processing: Knowledge of using Spark Streaming for real-time data processing.
Spark Optimization: Ability to optimize and fine-tune Spark applications for better performance.

Participants will be assessed on:

Spark RDD and DataFrame Operations: Understanding how to create and manipulate RDDs and DataFrames.
Spark SQL: Ability to perform SQL queries on Spark DataFrames and work with Spark SQL.
Data Transformation and Actions: Knowledge of Spark operations like map(), reduce(), and filter().
Real-Time Processing: Ability to implement Spark Streaming applications.
Spark Optimization: Knowledge of techniques for optimizing Spark jobs, such as caching and partitioning.

ProgNXT Certificate of Completion will be awarded to participants who successfully complete the course and assessments.

Introduction to Apache Spark and Core Concepts

Introduction to Apache Spark:

What is Apache Spark and its advantages over Hadoop.
Spark components: Spark Core, Spark SQL, Spark Streaming, MLlib,
Understanding Spark’s architecture: Driver, Executors, Cluster Manager.
Hands-on Exercise

Spark RDDs (Resilient Distributed Datasets):

Understanding RDDs: Creation, transformations, and actions.
Working with RDDs in Spark (e.g., map(), filter(), reduce(), flatMap()).
Hands-on Exercise

Spark DataFrames and Datasets:

Introduction to DataFrames and Datasets in Spark.
Benefits of DataFrames over RDDs.
Creating DataFrames from RDDs, JSON, and CSV files.
Hands-on Exercise

Spark SQL and Data Processing

Spark SQL:

Introduction to Spark SQL and its integration with DataFrames.
Running SQL queries on Spark DataFrames.
Creating and querying temporary views in Spark SQL.
Hands-on Exercise

DataFrame Transformations and Actions:

Using various DataFrame transformations: select(), groupBy(), join(), and agg().
Common actions on DataFrames: collect(), show(), count(), save().
Hands-on Exercise

Data Sources and Formats in Spark:

Reading from and writing to various data formats: JSON, CSV, Parquet, JDBC, HDFS.
Connecting to different data sources (e.g., relational databases, HDFS).
Hands-on Exercise

Spark Streaming and Optimization Techniques

Spark Streaming:

Introduction to real-time stream processing with Spark Streaming.
Working with DStreams and Spark Streaming transformations.
Handling time windows and sliding windows in streaming data.
Hands-on Exercise

Spark Machine Learning (MLlib):

Introduction to MLlib for building machine learning models in Spark.
Basic machine learning algorithms: classification, regression, clustering.
Hands-on Exercise

Spark Optimization and Performance Tuning:

Understanding Spark’s execution plan and how to optimize jobs.
Techniques for performance tuning: partitioning, caching, broadcasting, and serialization.
Hands-on Exercise

Final Project and Review:

Using all the concepts learned to solve a real-world big data problem.
Reviewing key topics and answering participant questions.
Hands-on Exercise

Apache Spark official documentation and user guides.
Sample datasets for practical exercises.
Best practices for working with Spark, SQL, and Streaming.

Evaluation based on participation in practical exercises and course assessments.
ProgNXT Certificate of Completion will be awarded to participants who successfully complete the course and assessments.

Apache Spark Training and Certification Events in astana

Online Events

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More Upcoming Session Dates

November 2026

📍 Astana, Kazakhstan

5th Nov 2026→7th Nov 2026

🌐 Online Instructor-Led

10th Nov 2026→12th Nov 2026

🌐 Online Instructor-Led

20th Nov 2026→22nd Nov 2026

📍 Astana, Kazakhstan

25th Nov 2026→27th Nov 2026

December 2026

📍 Astana, Kazakhstan

2nd Dec 2026→4th Dec 2026

🌐 Online Instructor-Led

7th Dec 2026→9th Dec 2026

🌐 Online Instructor-Led

21st Dec 2026→23rd Dec 2026

📍 Astana, Kazakhstan

26th Dec 2026→28th Dec 2026

January 2027

🌐 Online Instructor-Led

2nd Jan 2027→4th Jan 2027

📍 Astana, Kazakhstan

9th Jan 2027→11th Jan 2027

📍 Astana, Kazakhstan

22nd Jan 2027→24th Jan 2027

🌐 Online Instructor-Led

28th Jan 2027→30th Jan 2027

Events for other courses in Astana, Kazakhstan

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Apache Spark Training and Certification in Astana