About the course
As businesses capture ever-increasing volumes of data, deriving vital insights is crucial for maintaining a competitive edge. Traditional data processing and warehousing approaches often prove both costly and difficult to scale.
Our Apache Spark Training Course is designed for Data Scientists, Data Analysts, Software Developers, and Architects seeking to gain practical, hands-on experience in building advanced Big Data solutions. This course focuses on leveraging Apache Spark for powerful real-time data stream analysis, large-scale machine learning, and efficient data processing.
You'll engage with extensive hands-on exercises, guided by an industry expert with first-hand experience in designing and implementing commercial-scale Big Data solutions. We encourage you to bring your own laptop to create a familiar learning environment and to take away all your work for immediate application in your projects or portfolio.
Instructor-led online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the World.
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- Understand Big Data & Spark Fundamentals: Grasp core Big Data challenges and articulate Apache Spark's architecture and role in modern data processing.
- Set Up Spark Development Environment: Configure a Spark development project (e.g., in IntelliJ/VS Code) and deploy/debug applications locally and in cloud environments.
- Master Spark Core APIs (RDDs & DataFrames): Effectively utilise Spark's Resilient Distributed Datasets (RDDs) and DataFrames for distributed data manipulation.
- Perform Data Analysis with Spark SQL: Write and optimise Spark SQL queries for large-scale data analysis and transformation.
- Implement Spark Streaming Applications: Develop real-time data stream processing applications, including integration with technologies like Kafka.
- Apply Machine Learning with Spark MLlib: Utilise Spark MLlib to build and deploy scalable machine learning models for large datasets.
- Optimise Spark Performance & Architecture: Design and implement strategies for optimising Spark application performance and cluster architecture.
- Integrate Spark with Modern Data Ecosystems: Connect Spark with various data sources and sinks, including cloud storage (e.g., AWS S3) and NoSQL databases.
- Develop Spark Solutions in Key Languages: Program effectively with Apache Spark using popular languages such as Scala and Python (PySpark).
- Deploy & Debug Spark Applications: Successfully build, deploy, and debug Spark applications across various environments, including cloud platforms.
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This course is ideal for professionals seeking to build or enhance their skills in scalable data processing and analytics. This includes:
Data Scientists
Data Analysts
Software Developers
Data Architects
Big Data Engineers
DevOps and SRE Professionals
Anyone looking to leverage Spark for real-time analytics, machine learning, or large-scale data processing.
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You don't need to be a Spark expert to join this course, but some foundational knowledge is beneficial.
Programming proficiency: Experience with at least one programming language (e.g., Python, Scala, Java).
SQL familiarity: A basic understanding of SQL concepts is beneficial for working with Spark SQL.
Data concepts: Familiarity with fundamental data processing or data warehousing concepts.
Laptop requirement: Attendees are encouraged to bring their own laptop for hands-on exercises.
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This Apache Spark course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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Apache Spark Fundamentals
Big Data Challenges & Spark's Role: Addressing modern data processing hurdles and Apache Spark's position in the Big Data ecosystem.
Spark Architecture Deep Dive: Understanding core components (Driver, Executor, Cluster Manager), execution model, and fault tolerance mechanisms.
Setting Up Your Spark Environment:
Creating and managing Spark projects in common IDEs (e.g., IntelliJ, VS Code).
Running and debugging Spark applications locally.
Building and deploying Spark projects with common build tools (e.g., SBT for Scala).
Introduction to installing and running Spark in the Cloud (e.g., on AWS).
Spark Core APIs & Data Transformation
Resilient Distributed Datasets (RDDs): Introduction to RDDs, understanding transformations and actions for immutable, distributed collections.
Spark SQL & DataFrames:
Introduction to DataFrames as a powerful, structured API for data manipulation.
Performing large-scale data analysis and transformation using Spark SQL queries.
Working effectively with the DataFrames API (examples in Scala/Python).
Integrating Spark with Diverse Data Sources: Connecting Spark applications to various data formats (e.g., Parquet, ORC, CSV, JSON) and storage systems (e.g., Amazon S3, relational databases, NoSQL databases).
Real-time Streaming & Machine Learning
Spark Streaming & Structured Streaming:
Introduction to real-time data processing concepts.
Building robust, fault-tolerant data pipelines with Spark Structured Streaming.
Seamless integration with popular messaging systems like Apache Kafka.
Spark Machine Learning (MLlib):
Overview of MLlib components and available algorithms for various machine learning tasks.
Building, training, and deploying scalable machine learning models on large datasets using Spark.
Real-time Analytics with Spark: Applying Spark for instant insights and advanced analytical functions, including concepts like dynamic sampling, distinct count estimation (e.g., HyperLogLog), and moving averages.
Deployment, Optimisation & Best Practices
Spark Application Optimisation:
Strategies for tuning Spark configurations for optimal performance.
Understanding performance considerations for various transformations and actions.
Debugging and troubleshooting common issues in Spark applications.
Spark Deployment Strategies: Exploring different ways to run Spark applications on various cluster managers (e.g., YARN, Kubernetes, standalone mode) and cloud-managed services (e.g., AWS EMR, Databricks).
Design Patterns & Best Practices:
Introduction to Test-Driven Development (TDD) principles as applied to Spark applications.
Identifying common patterns and anti-patterns in Spark development for robust and efficient code.
Integration considerations with third-party applications and specific programming languages (e.g., Python (PySpark), Scala).
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Official Apache Spark Documentation: The primary and most authoritative source for all Spark features and APIs - https://spark.apache.org/docs/latest/
Databricks Documentation: As a major contributor to Spark, Databricks offers excellent resources and tutorials - https://docs.databricks.com/
Apache Kafka Documentation: For deeper understanding of messaging systems used in streaming - https://kafka.apache.org/documentation/
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