Learn Spark SQL, DataFrames, and Machine Learning. Build scalable data pipelines and master distributed computing.
π₯ 676 students
π January 2026 update
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- Course Overview
- Navigate the complexities of modern big data architecture by mastering the integration of Python and Apache Spark, the industry standard for high-performance distributed computing.
- Explore the evolution of data processing from traditional single-node systems to multi-node clusters, understanding how the Spark JVM backend manages Pythonic logic.
- Gain a deep architectural understanding of the Spark Driver and Worker nodes, ensuring you can visualize how tasks are serialized and distributed across a network.
- Dive into the core concept of Lazy Evaluation, learning how the Spark engine builds a Directed Acyclic Graph (DAG) to optimize execution plans before a single byte of data is moved.
- Compare and contrast Resilient Distributed Datasets (RDDs) with DataFrames and Datasets, identifying the specific scenarios where low-level API control is necessary versus high-level optimization.
- Learn the nuances of the Catalyst Optimizer and Tungsten Execution Engine, which allow PySpark to rival the performance of native Scala implementations in 2026.
- Understand the role of Cluster Managers such as Kubernetes, YARN, and Mesos in orchestrating resource allocation for massive enterprise workloads.
- Requirements / Prerequisites
- A foundational grasp of Python programming, including familiarity with common data structures like lists, dictionaries, and basic functional programming concepts like lambda functions.
- Fundamental knowledge of Structured Query Language (SQL), particularly joining tables, aggregating results, and using window functions to manipulate relational data.
- Basic understanding of Command Line Interfaces (CLI) for navigating directory structures and executing shell scripts during the environment setup phase.
- A computer system with at least 8GB of RAM (16GB recommended) to support local Docker containers or standalone Spark instances for testing and development.
- An introductory awareness of what “Big Data” entails, specifically the challenges of Volume, Velocity, and Variety that traditional databases fail to address.
- No prior experience with Distributed Systems or Scala is required, as the course builds these complex concepts from the ground up using Python.
- Skills Covered / Tools Used
- Master the PySpark SQL Module to execute complex analytical queries on massive datasets using both programmatic syntax and standard SQL strings.
- Implement advanced Data Engineering patterns, including Schema-on-read, Data Partitioning, and Bucketing to drastically reduce query latency and I/O overhead.
- Utilize Spark MLlib to build scalable machine learning workflows, covering Feature Vectors, Transformers, Estimators, and the Pipeline API.
- Handle real-time data ingestion challenges using Spark Structured Streaming, connecting to sources like Apache Kafka to process live data feeds with micro-batching.
- Execute sophisticated data transformations using User Defined Functions (UDFs) and learn how to optimize them using Vectorized UDFs (Pandas UDFs) to avoid serialization bottlenecks.
- Connect PySpark to diverse storage layers, including Amazon S3, Azure Data Lake Storage (ADLS), HDFS, and modern Lakehouse formats like Delta Lake or Apache Iceberg.
- Master the art of Performance Tuning by managing Shuffle Partitions, Broadcast Joins, and identifying Data Skew through the Spark UI.
- Benefits / Outcomes
- Transition from a local Data Scientist to a Big Data Engineer capable of handling petabyte-scale datasets that exceed the memory limits of local machines.
- Develop the ability to design End-to-End Scalable Data Pipelines that can be deployed in production cloud environments such as Databricks, AWS EMR, or Google Cloud Dataproc.
- Gain a competitive edge in the 2026 job market by mastering MLlib for distributed model training, a skill highly sought after by enterprise AI divisions.
- Achieve professional-level proficiency in Troubleshooting and Debugging distributed applications, significantly reducing downtime and computational costs for your organization.
- Build a robust portfolio project featuring a complete ETL (Extract, Transform, Load) process followed by a predictive analytics model built on top of distributed data.
- Learn to write Dry and Modular Code that is easily maintainable and adheres to modern software engineering best practices within a data context.
- PROS
- Features the January 2026 Update, ensuring all library versions and syntax recommendations are aligned with the latest Spark 4.x or 5.x releases.
- Includes a heavy emphasis on Industry Use-Cases, moving beyond simple tutorials to address real-world data corruption and schema evolution problems.
- The course provides a Cloud-Agnostic approach, teaching you principles that apply whether you are working on-premise or in a multi-cloud environment.
- CONS
- The conceptual overhead of Distributed Computing can be steep for absolute beginners, requiring significant mental shifts regarding how data is stored and accessed.
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