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Data Processing with PySpark Intermediate

The Data Processing with PySpark course equips data engineers and data scientists with hands-on skills to process large-scale datasets efficiently using Apache Spark’s Python API. It solves the growing industry challenge of slow, fragmented data workflows by teaching optimized DataFrame operations, Spark SQL, and Structured Streaming. With Apache Spark adoption rising at 33.9% CAGR through 2030, demand for PySpark proficiency is critical across Fortune 500 companies and tech giants.

This course prepares learners for the Databricks Certified Associate Developer for Apache Spark exam, featuring official vendor-authorized courseware and 30-day lab access. Graduates gain proven expertise in high-demand areas like distributed computing and real-time analytics, positioning them for roles with average salaries exceeding $120,000 and long-term career growth in data engineering.

32 Hours (4 Days)
Live Online / Classroom
20+ professionals trained

Training Formats & Pricing

1-on-1 USD 1,800
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Public Batch USD 1,400
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Self-Paced USD 199
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Course Overview

The Data Processing with PySpark course by Open Source is designed to equip data professionals with the skills needed to process and analyze large-scale datasets using PySpark, the Python API for Apache Spark. This training prepares learners for the Databricks Certified Associate Developer for Apache Spark certification exam, a widely recognized credential in the big data industry. It is ideal for data engineers, analytics engineers, and machine learning engineers who are responsible for building scalable data pipelines and performing distributed data processing. With over 212 active job postings listing PySpark as a key requirement and 89% of these roles offering remote work options, demand for PySpark expertise continues to grow across technology, finance, and healthcare sectors.

Participants will gain hands-on experience with core components of the Apache Spark ecosystem, including Spark SQL, DataFrames, Structured Streaming, Spark Core, Pandas API on Spark, and Spark Connect. The course features interactive labs conducted in a Databricks-based lab environment where students build real-world data processing solutions. A key project involves creating an end-to-end streaming pipeline using Structured Streaming to ingest, transform, and aggregate live data from multiple sources while applying watermarking for deduplication and fault tolerance. Learners will configure Spark sessions, optimize partitioning strategies, implement broadcast joins, and use Spark SQL to query both batch and streaming data, ensuring they develop production-ready skills aligned with industry best practices.

This course directly supports preparation for the Databricks Certified Associate Developer for Apache Spark certification, which validates foundational PySpark proficiency and is accepted by leading tech employers including Amazon, Microsoft, and Google. Certified professionals command a median salary of $167,900, with top earners in high-demand regions like California exceeding $194,000 annually. Koenig Solutions enhances the learning experience with Guaranteed-to-Run batches and access to official courseware, ensuring consistent scheduling and up-to-date content. Upon completion, graduates are well-positioned to advance into senior data engineering roles or transition into specialized domains such as real-time analytics and machine learning operations.

What You'll Learn

Implement high-performance I/O operations for CSV, JSON, and Parquet formats using the PySpark DataFrame API to minimize ingestion latency.
Construct robust data cleaning workflows using PySpark transformations to enforce schema validation and ensure high-fidelity datasets.
Execute advanced data imputation and deduplication strategies to scale data pipelines to petabyte-level datasets while maintaining integrity.
Analyze complex datasets using Spark SQL to reduce query latency and generate actionable insights from distributed data sources.
Perform broadcast joins and window functions within the PySpark DataFrame API to manage complex relational data and time-series analytics.
Optimize Spark execution plans by leveraging the Catalyst Optimizer and tuning shuffle partitions to accelerate processing throughput.

Skills You'll Gain

PySpark DataFrames PySpark SQL PySpark Structured Streaming PySpark RDDs PySpark MLlib PySpark Pandas API PySpark Declarative Pipelines Spark Core Spark SQL Structured Streaming MLlib Pipelines Data Processing Distributed Computing In-Memory Processing Fault-Tolerant Processing Schema Evolution Spark Connect

Prerequisites

Recommended knowledge before taking this course
  • Course Benefits
  • Data Processing with PySpark by Open Source is designed to equip learners with the skills needed to handle big data efficiently, reducing processing time and increasing productivity. This course is ideal for data professionals aiming to advance their expertise in PySpark, an Apache Software Foundation project used by over 1,000 organizations worldwide for big data analytics. Completing this training enables you to leverage PySpark’s capabilities, transforming your data processing tasks and boosting your career prospects in data engineering.
  • Prerequisites
  • [
  • "Programming experience in Python 3.10 or higher",
  • "Working knowledge of Java 17+ and JVM environment setup, including JAVA_HOME configuration",
  • "Familiarity with Apache Hadoop 3.3+ ecosystem components such as HDFS and YARN",
  • "Experience with data processing libraries like pandas and PyArrow",
  • "Understanding of distributed computing concepts and Spark architecture including executors, drivers, and RDDs",
  • "Basic proficiency in Spark SQL for structured data processing",
  • "Local development environment setup including Jupyter Notebooks or IntelliJ IDEA with appropriate plugins",
  • "Operating system compatibility with Linux, macOS, or Windows 10/11 with WSL2"
  • ]
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Certification Exam

Everything you need to know about the Data Processing with PySpark certification exam

Exam Details
Exam Name
Data Processing with PySpark
Format
Multiple choice, labs & case studies
Questions
Duration
Passing Score
Validity
Retake Policy
N/A
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Data Processing with PySpark

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Course Curriculum

Structured learning with hands-on labs and real-world scenarios

1
Day 1– Mastering Spark and PySpark Fundamentals
Core Spark architecture overview Resilient Distributed Datasets (RDDs) mechanics Spark execution models and DAGs PySpark shell and REPL environments Efficient SparkSession initialization Ingesting CSV, JSON, and Parquet Writing data with PySpark pipelines Executing basic transformations and actions
2
Day 2– Data Processing with PySpark DataFrames
Comparing DataFrames and RDD performance Defining and inferring data schemas Selecting and renaming dataset columns Advanced filtering and boolean indexing Cleaning and handling missing values Removing duplicate records efficiently Casting and converting data types Sorting and ordering large datasets
3
Day 3– Data Manipulation with Open Source Spark SQL
Running SQL queries on DataFrames Registering temporary SQL views Inner, left, and anti joins Aggregations and groupBy operations Applying built-in Spark SQL functions Window functions for complex analytics Ranking records with row_number() Partitioning with the over() clause
4
Day 4– Advanced Data Processing Techniques
Caching and persisting DataFrames Broadcast and accumulator variable usage Custom User-defined functions (UDFs) Leveraging Pandas API on Spark Interpreting complex execution plans Optimizing data partitioning strategies Resolving common data skew issues Checkpointing for robust fault tolerance
5
Day 5– ETL Pipelines and Performance Optimization
Building scalable ETL pipelines Reading from external JDBC sources Writing data to external databases Optimizing Spark job performance Tuning cluster memory and cores Monitoring jobs via Spark UI Processing massive enterprise datasets Best practices for production deployments

What's Included in Your Training

Every enrollment comes packed with resources to maximise your learning and exam success

Career Outcomes

82%

of Data Processing with PySpark certified professionals report career advancement within 6 months

Salary Impact

+24%

Average salary increase reported after obtaining the Data Processing with PySpark certification

Typical Salary Range (Global)
Entry$90,000–$115,000
Mid$115,000–$145,000
Senior$145,000–$180,000

*Source: Glassdoor / LinkedIn 2025

Job Roles

6
  • Data Engineer
  • Big Data Engineer
  • PySpark Developer
  • Data Pipeline Engineer
  • Spark Data Engineer
  • Cloud Data Engineer

Companies Hiring

5,000+
Amazon Microsoft Google Accenture Deloitte Infosys Razorpay Nubank SafeGraph Halodoc

and 5,000+ organizations worldwide seeking Data Processing with PySpark certified professionals

Real Transformations

Course Student Reviews

Real results from IT professionals who trained with Koenig — rated 4.9/5 from 18,400+ verified reviews.

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    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

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    Engineering Manager

    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

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    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

    James T.

    Head of L&D, UK Enterprise

    100+ Learners Trained ✓ Verified
  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

    James T.

    Head of L&D, UK Enterprise

    100+ Learners Trained ✓ Verified
  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified

Frequently Asked Questions

Everything you need to know about the Data Processing with PySpark training course

Is the Data Processing with PySpark certification exam included in the course fee, and what is the cost of the exam?
The Data Processing with PySpark certification exam is not included in the Koenig Solutions course fee. You must purchase the Databricks Certified Associate Developer for Apache Spark exam separately for $200 directly from Databricks. Each attempt costs $200 with no refunds for failed attempts. Candidates must register via Webassessor to schedule their proctored exam.
What training formats are available for the Data Processing with PySpark course, and is it Guaranteed-to-Run?
Koenig Solutions offers live online 1-on-1, public instructor-led, and self-paced Flexi training for Data Processing with PySpark. All formats are Guaranteed-to-Run (GTR). This ensures your scheduled batch proceeds regardless of enrollment, allowing you to book with confidence. Flexi and live sessions provide global access with flexible timing options.
How long is lab access provided, and what type of environment is used for hands-on practice?
Lab access is provided for 6 months for the Data Processing with PySpark Flexi course. You will use cloud-based sandbox environments via Microsoft Learn. These labs integrate with Koenig's Learning Enhancement Tool (LET) for real-world practice. You will perform PySpark data processing, DataFrame operations, and Spark SQL queries in a secure, browser-accessible platform.
What is the rescheduling and cancellation policy for the Data Processing with PySpark course?
Koenig allows free rescheduling to another Guaranteed-to-Run batch if requested before training starts under our Happiness Guarantee. If you cancel or reschedule within 10 days of the start date, a 50% fee applies. The same training session cannot be rescheduled more than once, ensuring consistent learning schedules for all participants.
What is the format, number of questions, passing score, and time limit for the PySpark certification exam?
The Databricks Certified Associate Developer for Apache Spark exam features 45 multiple-choice questions with a 90-minute limit. No test aids are allowed. The passing score is estimated at 70%. The exam includes unscored questions for analysis, covering Spark architecture, DataFrames, Spark SQL, and Structured Streaming to validate your PySpark expertise.
How long is the PySpark certification valid, and what is the renewal process and cost?
Your certification is valid for 2 years. To maintain your status, you must retake the current version of the exam through Databricks for $200. There is no grace period or free renewal. Passing the updated exam ensures your skills remain aligned with the latest Spark features and industry best practices.
What post-training support does Koenig provide after completing the Data Processing with PySpark course?
Koenig provides 6 hours of free consultation with trainers and access to Qubits for self-assessment. Flexi learners receive a course completion certificate. Students enjoy 30-day post-training access to materials and are eligible for revision classes. Our Happiness Guarantee allows a full refund or retake if you are dissatisfied with your training experience.
What are the prerequisites or prior experience needed to succeed in the Data Processing with PySpark course?
To succeed in Data Processing with PySpark, learners should have Python 3.7+ proficiency, basic SQL knowledge, and data manipulation experience using pandas. While no formal prerequisites exist, Databricks recommends 6+ months of hands-on experience with Apache Spark for exam success. Understanding Java 11+ is also beneficial for environment setup and execution.
What is the average salary impact for professionals with PySpark skills, and how does certification influence earnings?
Data Engineers with PySpark skills earn an average of $112,041 annually in the U.S., with senior roles exceeding $150,000. Certification enhances your professional credibility, particularly in enterprise sectors. Demand for Spark expertise in data pipelines and distributed processing drives premium compensation and significant career advancement for certified professionals.
How does formal training in Data Processing with PySpark compare to self-study in terms of exam success and skill mastery?
Formal training yields a 72% first-attempt pass rate compared to 64% for self-study, with hybrid learners achieving 84%. Koenig's structured curriculum, live labs, and expert instruction accelerate your mastery of Spark architecture and DataFrame APIs. This approach reduces your preparation time and increases your confidence for the proctored certification exam.
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