Apache Spark Training
Master Apache Spark 3.5, PySpark, and Scala for big data processing. Build real-time data pipelines with hands-on projects at COSS Hyderabad.
Course Overview
Alright, so you're looking into Apache Spark, right? Good call, because this technology is essential for handling massive datasets with speed. Forget those slow batch jobs; with Spark, you're talking real-time analytics and lightning-fast computations. In this course, I'm [TRAINER_NAME], and I’ll personally walk you through everything, starting from the basics of Big Data right up to building complex data pipelines. You'll work with Apache Spark 3.5, mastering PySpark and Scala to process big data efficiently. We'll dive deep into Spark SQL, DataFrames, and how to use them effectively for data transformation. By week 4, you'll be tackling Spark Streaming and Structured Streaming, making sense of data as it arrives. Does that sound like a challenge you’re ready for? We don't just talk theory here; you'll complete 10 hands-on projects, deploying your Spark applications on real cluster environments. You'll also integrate with tools like Hadoop 3.3, Hive, and Kafka. The demand for skilled Spark developers is huge, especially here in Hyderabad. Companies like Amazon Hyderabad and Microsoft IDC in HITEC City are constantly hiring for roles that need strong Spark expertise. Expect starting salaries for freshers trained in Spark to be in the 6 to 10 LPA range, with experienced pros earning significantly more. We even offer weekend batches at our Dilsukhnagar and Ameerpet centers, perfect if you're working already. We've placed over [STUDENT_COUNT] students in data engineering roles – are you next?
What You Will Learn
- ✓ Master Apache Spark 3.5 Core APIs (RDDs)
- ✓ Build data pipelines using PySpark and Scala
- ✓ Work with Spark SQL, DataFrames, and Dataset API
- ✓ Implement real-time processing with Spark Streaming
- ✓ Perform 10+ hands-on projects on live data
- ✓ Learn Spark performance tuning and deployment strategies
- ✓ Prepare for Databricks and Azure DP-203 certifications
- ✓ Personalized mentorship from [TRAINER_NAME]
Tools & Technologies
Syllabus
1Module 1: Big Data & Hadoop Essentials+
- Introduction to Big Data concepts and challenges
- HDFS (Hadoop Distributed File System) architecture
- YARN (Yet Another Resource Negotiator) resource management
- MapReduce paradigm overview
- Hadoop 3.3 cluster setup and basic operations
2Module 2: Spark Core Fundamentals+
- Introduction to Apache Spark 3.5 and its ecosystem
- Spark Architecture: Driver, Executors, Cluster Managers
- Resilient Distributed Datasets (RDDs) operations and transformations
- Spark Shell for interactive data analysis (Scala & PySpark)
- Monitoring Spark applications with Spark UI
3Module 3: Spark SQL & DataFrames+
- Introduction to DataFrames and SparkSession
- Creating DataFrames from various data sources (CSV, JSON, Parquet)
- DataFrame transformations (select, filter, groupBy) and actions (show, collect)
- Spark SQL syntax and integration with existing Hive tables
- Understanding the Catalyst Optimizer
4Module 4: PySpark and Scala for Spark+
- Essential PySpark API for data manipulation
- Scala programming basics for Spark developers
- Developing User-Defined Functions (UDFs) in PySpark and Scala
- Integrating external Python/Scala libraries with Spark
- Performance considerations for language choice
5Module 5: Spark Streaming & Structured Streaming+
- DStreams architecture and batch interval processing
- Real-time data processing concepts
- Integrating Spark Streaming with Apache Kafka
- Structured Streaming fundamentals and fault tolerance
- Implementing watermarking for late data handling
6Module 6: Advanced Spark & Performance Tuning+
- Spark deployment modes (Local, YARN, Standalone, Kubernetes)
- Optimizing Spark applications: partitioning, caching, persistence
- Understanding broadcast variables and accumulators
- Identifying and resolving common performance bottlenecks
- Introduction to Delta Lake for data reliability
7Module 7: Real-world Projects & Certification Prep+
- Building an end-to-end data lake pipeline (AWS S3/Azure ADLS)
- Developing a real-time IoT data processing application
- Workshop on Databricks Certified Associate Developer exam objectives
- Review of concepts for Azure Data Engineer Associate (DP-203)
- Capstone project: Design and deploy a scalable Spark solution