Apache Spark In-Depth (Spark with Scala)

Outline the data engineering career path through learning tracks like Hadoop, Spark with Scala, Flume, and optional cloud options such as Azure Data Fundamentals.

Learn how Apache Spark evolved as a unified, in-memory computing engine for batch, streaming, SQL, and machine learning on clusters, complementing and evolving Hadoop ecosystems.

Explore Spark's evolution from RDD to high level data frames and datasets, learn lazy transformations and actions, and master partitions, executors, and streaming with structured streaming.

Install Spark on a Linux VM, choosing a ready-made Cloudera QuickStart VM or a manual setup; learn to import appliances, enable virtualization, and configure Ubuntu 20.04 with Oracle VirtualBox.

Explore the why and how of RDD in Spark with Scala, including immutability, partitioning, and the transformation and action model, plus legacy code and tight data control.

Day 5 extends RDD Day 2, applying map, flatMap, and randomSplit to create training and test data, with word count and basic machine learning workflows in Spark with Scala.

Explore advanced rdds in spark with scala, including map and map partitions transformations, cache and persist strategies, checkpointing, and key-value rdds with mapvalues, keys, and values.

Explore Spark RDD Day 4 concepts, including key-value pairs, map and flatMap values, lookups, grouping by key, reduce by key, aggregate by key, and joins.

Explore rdd day 5 essentials: zip transformations, coalescing versus repartition, and how partitioning, including hash and range, and custom partitioning affect shuffling and performance.

Explore the evolution of Spark data structures from RBD to data frame and dataset, compare fixed schema and schema inference, and learn about logical and physical query plans.

Master dataframe operations in Spark with Scala by loading and viewing data, defining and casting schemas, selecting and aliasing columns, and creating views to run sql queries.

Learn to load a car dataset with Spark dataframes, infer schema, create views, and perform SQL-like queries, including derived mileage columns and basic data cleansing techniques.

Master working with complex dataframes in Spark using Scala: define and inspect schemas, handle nested structures and arrays, explode fields, and implement user-defined functions for custom transformations.

Explore Spark dataframe joins, from inner and outer joins to semi, anti, natural, and cross joins, with memory, shuffle, and broadcast considerations.

Explore reading and converting data from CSV, JSON, and Parquet using Spark dataframes, compare row-based and columnar storage, and optimize with partitioning, bucketing, and predicate pushdown.

Explore spark sql and dataframes, bridging hive and sql concepts, with practical steps to create databases, tables, views, and temporary and global views, and manage metadata.

Explains Spark datasets versus dataframes, detailing compile-time schema checks in Scala and Java, Python absence, and slower yet safer validation for datasets.

Learn spark application development and deployment, from writing jobs with spark session, driver and executors, to transformations and actions, deployed via spark-submit in local or cluster mode.

Master Spark performance tuning with Scala by optimizing memory, shuffles, joins, caching, repartitioning, and garbage collection; compare data frames and data sets and Kryo vs Java serialization for throughput.

Explore common Apache Spark errors and debugging techniques across the application lifecycle, including driver and executor roles, memory and shuffle issues, partitions, joins, and logs from the spark UI.

Explore Spark streaming and structured streaming concepts, including event time vs processing time, micro-batches versus continuous processing, and challenges like out-of-order data, exactly-once processing, and joins.

Explore building streaming pipelines with Spark structured streaming, reading from sources like Kafka or socket, applying transformations, and writing to DFS or console with exactly-once semantics.

Master Spark streaming checkpoints and automatic state management, apply windowed and watermarking techniques for stateful processing, handle late data and deduplicate events via event-time concepts.

Design a capstone data ingestion and transformation pipeline using a framework, driven by configuration and a readme, integrating sources like Cassandra, Elasticsearch, and Kafka, exploring batch and streaming modes.

Learn four steps to secure Spark with Scala job support: study documentation, build and document a data project, review examples, and record a configuration demonstration video.

Explore a data engineering career with Hadoop, Spark with Scala, Kafka, and Java basics; learn optional cloud tools and receive personalized projects, resume help, and interview guidance.