PySpark & Apache Spark Course in Hyderabad
Process big data at scale with PySpark, Spark SQL and Structured Streaming
The PySpark and Apache Spark course teaches you to write distributed data processing jobs in Python. You start with Python, SQL and big data concepts, then learn Spark architecture, RDDs, the DataFrame API, Spark SQL, joins and aggregations, window functions, complex and nested data types, reading and writing CSV, JSON, Parquet, ORC and Delta formats, and handling schema and bad records. Later modules cover performance tuning with partitioning, caching, broadcast joins, adaptive query execution and the Spark UI, Structured Streaming with Kafka and checkpointing, Delta Lake basics, testing PySpark code, and deploying with spark-submit on a cluster or managed platforms such as Databricks, Amazon EMR or Dataproc. The course is platform neutral and suits graduates, SQL developers and data professionals aiming for data engineer roles. Basic programming and SQL are helpful; Python is taught from the basics. You complete labs on real datasets and finish with an end-to-end batch and streaming project. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Big Data & Spark Fundamentals
- •Why distributed processing; Hadoop to Spark evolution
- •Spark components: driver, executors, cluster managers and deployment modes
- •Lazy evaluation, transformations, actions and DAGs
- •Lab: Install Spark locally or use a managed notebook
- •Outcome: Explain how a Spark job runs
Python & SQL for Spark
- •Python data types, functions and comprehensions
- •SQL joins, aggregations and window functions
- •Virtual environments, packages and notebooks
- •Lab: Practice SQL and Python on a sample dataset
- •Outcome: Use the language skills Spark code depends on
RDDs & Core Concepts
- •RDD creation, map, filter, flatMap and reduceByKey
- •Partitions, shuffles and narrow versus wide transformations
- •Broadcast variables and accumulators
- •Lab: Build a word-count and a log-analysis job with RDDs
- •Outcome: Understand the foundation under DataFrames
DataFrames & Spark SQL
- •Creating DataFrames and defining schemas
- •Column expressions, filter, withColumn, groupBy and agg
- •Joins, set operations and Spark SQL with temporary views
- •Lab: Analyse an e-commerce dataset with DataFrames
- •Outcome: Perform common analytics with the DataFrame API
Reading & Writing Data
- •File formats and their trade-offs: row versus columnar
- •Reader options, bad record handling and schema inference
- •Writing with modes, partitionBy and bucketing; JDBC sources
- •Lab: Convert raw CSV to partitioned Parquet
- •Outcome: Choose formats and write efficient output
Advanced Transformations
- •Window functions: ranking, running totals and lag or lead
- •Complex types: arrays, maps and structs; explode and JSON parsing
- •UDFs, pandas UDFs and when built-in functions are better
- •Lab: Parse nested JSON and compute rolling metrics
- •Outcome: Handle semi-structured and time-based data
Data Cleaning & Quality
- •Null handling, type casting and date handling
- •Deduplication, outlier detection and standardisation
- •Data validation rules and quarantine of bad records
- •Lab: Build a reusable cleaning module with tests
- •Outcome: Produce trustworthy datasets
Performance Tuning
- •Reading the Spark UI: stages, tasks, shuffles and spills
- •Partition sizing, repartition versus coalesce and caching
- •Broadcast joins, skew handling and adaptive query execution
- •Lab: Optimise a slow join-heavy job and measure the gain
- •Outcome: Diagnose and fix common bottlenecks
Structured Streaming
- •Streaming model: micro-batch and continuous concepts
- •Reading from files and Kafka; writing to Delta and Parquet
- •Event-time windows, watermarks and late data
- •Checkpointing, fault tolerance and exactly-once semantics
- •Lab: Build a streaming aggregation from a Kafka topic
- •Outcome: Build reliable streaming jobs
Delta Lake & Lakehouse Basics
- •Delta Lake format and ACID transactions
- •MERGE upserts, time travel and schema evolution
- •Compaction and Z-order or clustering concepts
- •Lab: Implement a slowly changing dimension with MERGE
- •Outcome: Add reliability to lake storage
Testing, Packaging & Deployment
- •Structuring PySpark projects and writing unit tests with pytest
- •Configuration, logging and packaging dependencies
- •Running with spark-submit; managed platforms: Databricks, Amazon EMR and Dataproc
- •Orchestration overview with Apache Airflow
- •Lab: Package a job and run it on a cluster
- •Outcome: Take Spark code from laptop to production
Capstone & Interview Preparation
- •Capstone: batch and streaming pipeline with cleaning, aggregation and Delta output
- •Spark and SQL interview question practice
- •GitHub portfolio and architecture write-up
- •Outcome: A portfolio project and interview readiness
Tools Covered
Projects Included
- E-Commerce Analytics: Compute sales, customer and product metrics from large CSV data with DataFrames; deliverable is a notebook and Parquet output.
- Nested JSON Event Parser: Flatten clickstream JSON with explode and structs and load it as partitioned Parquet; deliverable is a reusable job.
- Join and Skew Optimisation: Tune a slow join using broadcast and repartition and report the gain; deliverable is a before-and-after performance note.
- Streaming Order Aggregation: Process Kafka events with windows and watermarks into Delta; deliverable is a running stream and a checkpoint recovery test.
- Capstone Data Pipeline: Combine batch and streaming with tests and packaging; deliverable is a GitHub repository and architecture diagram.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the PySpark & Apache Spark course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on PySpark & Apache Spark.
Certificate of Completion
PySpark & Apache Spark
This certifies that
[Your Name]
has successfully completed the
PySpark & Apache Spark
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured PySpark & Apache Spark course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best PySpark & Apache Spark Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Spark Developer, Big Data Engineer, Data Engineer, PySpark Developer; support is guidance with the job search, not a guarantee of a job.