Data Engineering and Analytics with Apache Spark 3 and Python Practice Exam
Data Engineering and Analytics with Apache Spark 3 and Python Practice Exam
About the Data Engineering and Analytics with Apache Spark 3 and Python Exam
The Data Engineering and Analytics with Apache Spark 3 and Python exam assesses the skills and knowledge required to leverage Apache Spark 3 in conjunction with Python to process and analyze large-scale data. Candidates will be tested on their ability to use Spark’s powerful features for data engineering tasks, including data ingestion, transformation, and storage, and performing advanced analytics on distributed datasets.
Key Concepts Covered
- The exam covers key areas such as Spark architecture, RDDs, DataFrames, and Datasets, along with Python libraries like PySpark for data processing, machine learning, and optimization.
- Additionally, candidates will be evaluated on their understanding of Spark SQL, streaming data processing, and integration with other big data tools and technologies.
- Successful candidates will demonstrate a deep understanding of building scalable, high-performance data pipelines and analytics applications using Apache Spark and Python.
Skills Required
To succeed in the Data Engineering and Analytics with Apache Spark 3 and Python exam, candidates should possess the following skills:
- Understanding Python syntax and libraries such as PySpark, Pandas, and NumPy for data processing and manipulation.
- Knowledge of Spark’s core components, including RDDs (Resilient Distributed Datasets), DataFrames, Datasets, and Spark SQL.
- Ability to ingest data from various sources (e.g., HDFS, S3, databases), transform and clean data, and work with different file formats like Parquet, JSON, and CSV.
- Skills in performing complex data transformations, aggregations, and analytics using Spark SQL and Python.
- Familiarity with using MLlib in Spark for scalable machine learning algorithms and understanding how to implement predictive models using Spark.
- Experience with handling real-time data using Spark Streaming and structured streaming techniques.
- Knowledge of integrating Spark with other big data tools such as Hadoop, Kafka, and Hive.
- Understanding of performance tuning, memory management, and optimizations within the Spark framework for efficient data processing.
- Solid grasp of the concepts behind distributed computing, fault tolerance, and resource management in Spark clusters.
- Ability to work with databases and data storage solutions, such as HDFS, Amazon S3, and relational databases, for efficient data handling and querying.
Who should take the Exam?
The Data Engineering and Analytics with Apache Spark 3 and Python exam is ideal for:
- Data Engineers
- Data Analysts
- Data Scientists
- Software Engineers
- Machine Learning Engineers
- Big Data Professionals
- IT Professionals and Developers
Course Outline
The Data Engineering and Analytics with Apache Spark 3 and Python Exam covers the following topics -
Domain 1 - Introduction to Spark and Installation
- Overview of Spark Architecture and Unified Stack
- Installation of Java, Hadoop, Python, and PySpark
- Installation of Microsoft Build Tools and Jupyter Notebooks
- Installation steps for MacOS: Java, Python, and PySpark
- Verifying Spark Installation on MacOS
- Exploring the Spark Web UI
Domain 2 - Spark Execution Concepts
- Introduction to Spark Applications and Sessions
- Understanding Spark Transformations and Actions (Parts 1 & 2)
- Visualizing the Directed Acyclic Graph (DAG)
Domain 3 - RDD Crash Course
- Introduction to Resilient Distributed Datasets (RDDs)
- Data Preparation and Transformations: Distinct, Filter, Map, FlatMap, and SortByKey
- RDD Actions
- Challenges: Converting Fahrenheit to Centigrade and XYZ Research (Parts 1 & 2)
Domain 4 - Structured API - Spark DataFrame
- Introduction to Structured APIs
- Preparing the Project Folder for DataFrames
- Understanding PySpark DataFrame, Schema, and DataTypes
- Reading and Writing DataFrames
- Working with Structured Operations and Performance Management
- Handling Missing or Bad Data, User-Defined Functions, and Aggregations
- Challenge Part 1 & 2: Data Preparation, Removing Null Rows, and Writing Partitioned DataFrame to Parquet
- Challenge Part 3: Aggregations, Grouping, and Analyzing Sales Data
Domain 5 - Introduction to Spark SQL and Databricks
- Introduction to Databricks and Spark SQL
- Registering for Databricks, Creating Clusters, and Notebooks
- Reading CSV Files into DataFrames and Creating Databases and Tables
- Inserting, Cleaning, and Analyzing Sales Data
- Creating a Dashboard to Visualize Sales Insights
