Databricks Certified Associate Developer for Apache Spark -(DCAD)

Databricks Certified Associate Developer for Apache Spark (DCAD) Study Guide

The Databricks Certified Associate Developer for Apache Spark (DCAD) certification is designed to validate your skills in building and optimizing Spark applications. To help you prepare for the exam, follow this structured study guide to cover all the key topics and ensure a comprehensive understanding of Apache Spark.

1. Spark Basics and Architecture (25%)

Understanding Spark Architecture:

Learn the core components of Apache Spark (Driver, Executor, Cluster Manager).

Understand the Spark execution model and the role of the SparkContext.

Study how Spark uses Resilient Distributed Datasets (RDDs) and DataFrames for distributed processing.

Setting Up Spark:

Familiarize yourself with the Databricks platform for Spark-based development.

Study the configuration and initialization of a Spark session.

2. Spark SQL (25%)

Working with Spark DataFrames:

Understand how to create and manipulate DataFrames.

Practice reading and writing data from various sources (CSV, JSON, Parquet).

Focus on performing transformations like select(), filter(), groupBy(), and join().

SQL Queries in Spark:

Learn to execute SQL queries using the Spark SQL API.

Understand the integration of SQL queries with Spark DataFrames.

Practice optimization techniques like query caching and partition pruning.

3. Data Processing with Spark (25%)

Data Wrangling and Transformation:

Study how to transform data using Spark functions.

Learn about Spark’s higher-order functions, such as map(), flatMap(), reduceByKey(), etc.

Understand working with complex data types (structs, arrays, maps).

Data Formats and Delta Lake:

Understand the usage of Parquet, JSON, and Delta Lake formats.

Practice reading and writing data in different formats.

Learn the benefits of Delta Lake for ACID transactions and time travel.

4. Spark Performance Tuning (15%)

Optimizing Spark Jobs:

Learn how to tune Spark jobs for better performance using Spark UI.

Study techniques like partitioning, broadcast joins, and caching.

Familiarize yourself with cluster configuration and how it impacts performance (e.g., memory management, shuffle operations).

Cluster Management and Resources:

Understand the importance of executors, partitions, and memory in Spark job execution.

Learn how to effectively manage distributed resources on the Databricks platform.

5. Data Pipelines and Integration (10%)

Building Data Pipelines:

Study how to design scalable data pipelines using Spark.

Learn how to integrate Spark with other tools, like Apache Kafka for real-time streaming and Apache Hive for data warehousing.

Connecting to External Systems:

Familiarize yourself with Spark’s integration with cloud data storage (e.g., AWS S3, Azure Blob Storage).

Understand how to work with external databases (e.g., MySQL, PostgreSQL) and streaming services.