Spark Tutorials

Apache Spark Tutorials Roadmap

Section 1: Introduction to Big Data and Apache Spark Basics

  • What is Big Data?
    • Understanding the challenges of processing large and complex datasets (Volume, Velocity, Variety).
    • Traditional data processing limitations.
  • What is Apache Spark?
    • An open-source unified analytics engine for large-scale data processing.
    • Known for its speed and ease of use compared to traditional MapReduce.
    • Provides APIs in Java, Scala, Python, and R.
  • Why Learn Spark?
    • Faster data processing (in-memory computation).
    • Supports various workloads (batch processing, interactive queries, streaming, machine learning, graph processing).
    • Unified platform.
    • Large and active community.
    • Widely adopted in the industry.
  • Spark Ecosystem Overview:
    • Spark Core.
    • Spark SQL.
    • Spark Streaming (or Structured Streaming).
    • MLlib (Machine Learning Library).
    • GraphX.
    • Cluster Managers (YARN, Mesos, standalone).
  • Setting up Your Development Environment:
    • Installing Java.
    • Installing Scala (optional, but good for understanding Spark internals).
    • Installing Python and PySpark.
    • Setting up a local Spark environment (standalone mode).
    • Using IDEs (IntelliJ IDEA with Scala/PySpark plugins, VS Code with Python/Scala extensions).
    • Using Jupyter Notebooks with PySpark.
  • Your First Spark Program (Local Mode):
    • Creating a SparkSession.
    • Loading data (e.g., from a local file).
    • Performing a simple transformation (e.g., counting lines).
    • Performing an action (e.g., collecting results).

Section 2: Spark Core and Resilient Distributed Datasets (RDDs) - Foundational Concepts

  • Understanding RDDs:
    • Immutable, distributed collection of objects.
    • Fault tolerance.
    • Lazy evaluation.
  • Spark Transformations:
    • Understanding lazy evaluation.
    • Common transformations: map, filter, flatMap, distinct, union, intersection, groupByKey, reduceByKey, sortByKey, join.
    • Working with key-value RDDs.
  • Spark Actions:
    • Triggering computation.
    • Common actions: collect, count, first, take, reduce, saveAsTextFile.
  • Understanding the Spark Execution Model:
    • Directed Acyclic Graphs (DAGs).
    • Stages and Tasks.
    • Shuffle operations.
  • Caching and Persistence:
    • Understanding when and how to cache RDDs (cache(), persist()).
    • Storage levels.
  • Shared Variables:
    • Broadcast variables.
    • Accumulators.

Section 3: Spark SQL and DataFrames - The Modern Spark API

  • Introduction to Spark SQL:
    • Processing structured and semi-structured data.
    • Integration with traditional databases and data warehouses.
  • Understanding DataFrames:
    • Distributed collection of data organized into named columns.
    • Provides a higher-level abstraction than RDDs.
    • Optimized execution engine (Catalyst).
  • Creating DataFrames:
    • From RDDs.
    • From various data sources (CSV, JSON, Parquet, ORC, JDBC).
    • Using SparkSession.
  • DataFrame Transformations (using the DataFrame API):
    • Selecting columns (select).
    • Filtering rows (filter, where).
    • Adding/dropping columns (withColumn, drop).
    • Renaming columns (withColumnRenamed).
    • Grouping and aggregation (groupBy, agg).
    • Joining DataFrames (join).
    • Sorting (orderBy, sort).
    • Handling missing values (na.drop, na.fill).
  • DataFrame Actions:
    • show, collect, count, first, take, write.
  • Using Spark SQL Queries:
    • Registering DataFrames as temporary views.
    • Executing SQL queries using spark.sql().
  • User-Defined Functions (UDFs):
    • Creating custom functions for DataFrames (Python and Scala).

Section 4: Working with Different Data Sources

  • Reading and Writing Data:
    • CSV files.
    • JSON files.
    • Parquet files (columnar format, highly recommended).
    • ORC files.
    • Reading from and writing to databases using JDBC.
    • Working with other formats (e.g., Avro).
  • Schema Inference and Specifying Schemas.

Section 5: Spark Streaming (or Structured Streaming) - Real-time Data Processing

  • Introduction to Streaming Data.
  • Understanding Spark Streaming (DStreams - older API):
    • Discretized Streams.
    • Transformations and Actions on DStreams.
    • Integrating with sources (Kafka, Kinesis, files).
  • Understanding Structured Streaming (newer API):
    • Treating streaming data as an unbounded table.
    • Using the DataFrame/Dataset API for streaming.
    • Input sources (Kafka, files, sockets).
    • Output sinks (console, memory, files, Kafka, databases).
    • Watermarking for handling late data.
    • Windowing operations.
  • Choosing Between Spark Streaming and Structured Streaming.

Section 6: MLlib - Machine Learning with Spark

  • Introduction to Distributed Machine Learning.
  • MLlib Pipelines:
    • Building end-to-end ML workflows.
    • Transformers and Estimators.
  • Common ML Algorithms in MLlib:
    • Classification (Logistic Regression, Decision Trees, Random Forests, Gradient Boosted Trees).
    • Regression (Linear Regression, Decision Trees, Random Forests, Gradient Boosted Trees).
    • Clustering (K-Means).
    • Dimensionality Reduction (PCA).
  • Feature Engineering:
    • Vector Assembler.
    • Scaling and Normalization.
    • One-Hot Encoding.
  • Model Evaluation and Tuning:
    • Cross-validation.
    • Hyperparameter tuning (Grid Search).

Section 7: Spark Deployment and Cluster Management

  • Understanding Spark Cluster Modes:
    • Standalone mode.
    • Spark on YARN.
    • Spark on Mesos.
    • Spark on Kubernetes.
  • Submitting Spark Applications:
    • Using spark-submit.
    • Understanding deployment modes (client, cluster).
  • Monitoring Spark Applications:
    • Using the Spark Web UI.
    • Understanding stages, tasks, and executors.
  • Configuration and Tuning:
    • Memory tuning.
    • CPU configuration.
    • Shuffle tuning.

Section 8: Advanced Topics (Optional)

  • Spark GraphX (Graph Processing).
  • Integrating Spark with other Big Data Tools (e.g., Hive, HDFS).
  • Optimizing Spark Performance (advanced tuning, code optimization).
  • Understanding the Catalyst Optimizer and Tungsten Execution Engine.
  • Working with Spark on Cloud Platforms (AWS EMR, Google Cloud Dataproc, Azure Synapse Analytics).

Section 9: Case Studies and Practice

  • Working through end-to-end Big Data processing and analytics projects using Spark.
  • Applying learned concepts to real-world datasets.
  • Building data pipelines.

Section 10: Further Learning and Community

  • Official Apache Spark Documentation (spark.apache.org/docs/).
  • Spark Programming Guides (for Scala, Java, Python, R).
  • Online Courses and Specializations in Big Data and Apache Spark (Coursera, edX, Udacity, DataCamp, etc.).
  • Books on Apache Spark.
  • Participating in Community Forums (Stack Overflow, Reddit r/apachespark, Spark mailing lists).
  • Attending Conferences and Meetups (Spark Summit).
  • Exploring Open-Source Spark Projects on GitHub.
  • Contributing to Apache Spark.