Big Data with Apache Spark 3 and Python: From Zero to Expert

Discover Apache Spark, a fast big data solution with in-memory caching and a rich functional model that outperforms MapReduce, enabling parallel processing for machine learning and data science.

Learn how Apache Spark executes on a cluster, coordinating a driver and workers via a Spark session, with one task per partition processed, cached for iterations, and results saved.

Apache Spark ecosystem, including Spark Core API, RDDs, Spark SQL and DataFrames, streaming, MLlib, and GraphX, with Python and SQL, plus official documentation at spark.apache.org.

Explore PySpark, the Python API for Apache Spark, enabling distributed, parallel Python applications. Learn architecture, master-slave design, and cluster managers like standalone, Mesos, Yarn, and Kubernetes, with local development.

Install Apache Spark locally by downloading a release, unzipping, and setting log4j root category to ERROR; then install Anaconda and the Windows Hadoop binary, ensuring version alignment with Spark.

Install java jdk 8, unzip spark on the c drive, and set hadoop_home, spark_home, and java_home with path updates for spark bin and java bin.

Validate the Spark installation by launching Spark from Anaconda prompt and from Jupyter Notebook. Install and initialize findspark, then run a Spark context (sc) to confirm Spark is ready.

Explore Apache Spark RDD fundamentals as a fault-tolerant, distributed, immutable abstract data layer. Learn how lazy transformations produce new RDDs and how actions trigger DAG execution.

Initialize a Spark session and SparkContext in notebooks using the PySpark API, with master, partitions, and an app name. Create an RDD from a list with parallelize and collect it.

Explore lazy transformations on RDDs with map, filter, flatMap, and distinct. Map doubles elements, filter keeps evens, flatMap returns multiple outputs, and distinct yields unique items when collect runs.

Discover how to process key-value RDDs with reduceByKey, groupByKey, and sortByKey in Apache Spark, using a lambda to sum values, collect results, and explore Spark documentation.

Explore Spark RDD actions like count, first, collect, and take, which execute immediately and return data, with cautions about using collect on large datasets.

Initialize Spark session and context to work with RDDs. Create an RDD from a list, then map, filter, distinct, and perform key-based reductions and sorts, finally counting and multiplying elements.

Explore dataframes in Apache Spark as tabular structures with metadata, enabling efficient processing, schema-based optimization, and missing-value imputation across formats and languages like Python, R, Scala, and Java.

Explore the characteristics of dataframes—distributed, lazily evaluated, and immutable with fault tolerance—and learn to create dataframes from sources such as csv, json, xml, parquet files, RDDs, Hive, and Cassandra.

Master creating dataframes in Apache Spark with PySpark by initializing a Spark session, building dataframes from data collections and Hive tables using the create dataframe function.

Learn core PySpark DataFrame operations with Spark and Python, including count, select, filter, drop, group by, aggregates, sort, and with column expression for new features.

Explore the main dataframe join types in spark, including inner, left outer, right outer, full outer, and left/right anti joins, with practical syntax using df.join and depth and id.

Register a data frame as a temporary view in Spark and run SQL-like queries with spark.sql to analyze and transform data.

Learn how to load and export data with PySpark: create data frames from Hive tables and CSV files, and read from or save to relational databases via JDBC.

Master PySpark functions and performance optimizations for data transformations and distributed processing. Use cache or persist, partitions, UDFs, and the Catalyst query optimizer to boost DataFrame performance and fault-tolerant execution.

Master broadcast join and caching in Spark to boost performance, while imputing missing values and using user defined functions, creating temporary views, and persisting data to Hive tables.

Explore generating SQL expressions and user defined functions in Spark, using expr, selectExpr, and UDF to categorize salaries into low, mid, and high, with reusable queries.

Identify null values with isnull, filter, and show to view missing data, then replace them with fillna and remove rows with dropna, controlling behavior with how and subset arguments.

Learn partitions control in spark, including default 200 and changes to 6 or 500, and explore the catalog of APIs with list column, list function, current database, and cache operations.

Explore advanced analytics with spark by performing statistical analyses and transformations in preprocessing, managing schemas and missing values, and visualizing results with spark sql and PySpark with Python libraries.

Initialize a spark session, load stock price data from a csv in a Jupyter notebook, define or infer a schema, and rename columns to avoid future errors.

Inspect data in PySpark using schema, dtypes, printSchema, and describe to reveal the data schema and statistics, then use head, show, and first for samples.

Learn how to perform PySpark column transformations by adding a new date column, renaming an existing column, and dropping unnecessary columns to reshape a data frame.

Master null value handling in PySpark by identifying nulls with isnull and filtering results. Replace or drop missing data using fillna and dropna, exploring how, any, and subset options.

Explore data selection and querying with PySpark and PySpark SQL, using select, filter, between, like, group by, and aggregation, plus SQL API for advanced analytics.

Learn how to visualize spark data using matplotlib and pandas, convert to pandas dataframes, aggregate for memory efficiency, and generate bar and time series charts by sector and industry.

Learn to read and save data in CSV, JSON, and Parquet formats, persisting changes to large datasets so you can resume work later; use select to save specific columns.

Load stock price data with a schema in Apache Spark, clean nulls and drop market cap; compute averages for open, close, adjusted by industry, convert to pandas, and save parquet.

Explore koalas, the open source tool that substitutes pandas and eases the transition from pandas to PySpark by translating pandas APIs to spark sql execution plan.

Learn how to perform feature engineering with Koalas, including encoding categorical variables with getdummies and computing temporal differences in seconds, using Koalas with Spark.

Learn to create and manipulate koalas dataframes, a pandas API for spark, by building from lists and dictionaries, converting from pandas and spark dataframes, and inspecting dtypes.

Use Koalas to manipulate data in Spark dataframes, using index and iloc for access, convert to NumPy, and apply Pandas functions for describe, transpose, and sort by index or values.

Learn to handle missing data in koalas using numpy nan values, convert from Pandas to a koalas dataframe, and apply drop na and fillna to clean and fill missing values.

Visualize data with koalas by generating two data series (random and dates) and plot line charts using Matplotlib, Plotly, or Bokeh, with automatic legends and multi-variable labeling.

Learn to import and export data with Koalas, saving and reading CSV files and using Parquet for efficient data storage. Use Spark IO to access ORC and other data sources.

Apply Koalas to transform Pandas data, build series and dataframes, describe, sort, transpose, and group by, then visualize with Matplotlib and convert to a Spark dataframe.

Explore machine learning with Spark, covering supervised, unsupervised, and reinforcement learning, and show how Spark ML with DataFrames enables ETL-driven end-to-end pipelines from exploration to production.

Discover Apache Spark machine learning components, including algorithms for classification, regression, clustering, and collaborative filtering, data characterization and transformation, pipelines, feature selection, and model persistence for end-to-end development.

Develop spark-based machine learning from ETL and data prep to feature engineering and vector assembler-driven preprocessing, then train, tune, and deploy models (classification, regression, unsupervised) with MLlib.

Explore Spark's machine learning library, initialize a Spark session, and load the Titanic dataset from CSV to perform EDA and preprocessing with schema definitions and pandas profiling.

Import and explore heart data with PySpark by initializing a spark session, loading the CSV with a schema, and performing initial exploratory analysis for a binary classification model.

Build a PySpark data preprocessing pipeline by selecting useful variables, casting data types, inferring schema, handling nulls, using string indexer and vector assembler to create features for model training.

Continue with minimal preprocessing by using a vector assembler to create a single feature vector from input columns, preparing for the binary classification model.

Train and evaluate a binary classifier in PySpark using an 80/20 train-test split, pipelines with indexers, and a random forest (with other models) to predict survive.

Evaluate the trained model on the test data using the multiclass classification evaluator to compute accuracy and compare predictions to the survived label.

Train a logistic regression classifier in PySpark on preprocessed data, split 70% train and 30% test, and evaluate with the area under the curve to assess readiness for production.

Demonstrates a Spark structured streaming word count from a TCP socket, counting words with split and explode, and displaying complete results in the console.

Explore spark streaming output modes: complete, append, and update, and how each writes results to external storage, while covering replication, checkpointing, and stateless versus stateful transformations, including sliding windows.

Learn spark streaming window operations, including window length and sliding interval, to compute 30-second word counts every 10 seconds using reduced by key and window.

Explore spark streaming capabilities, ingest data from Kafka and Kinesis, process with spark sql and dataframes, train real-time models with mllib, and store results in Cassandra or Parquet files.

Demonstrate real-time bank fraud detection using spark streaming on the Kaggle fraud dataset, load data in spark, batch by step, count transactions, and save stepwise partitions to csv.

Program a real-time streaming pipeline for bank fraud detection using Spark read stream, one file per trigger, with a defined schema and grouped recipient counts.