Big Data Analytics With Apache Spark: Unlock Insights From Massive Datasets

Introduction:

In today's data-driven world, the ability to process and analyze large datasets is crucial for gaining valuable insights. This course on Big Data Analytics with Apache Spark equips participants with the specialized knowledge and skills to handle massive data volumes. Participants will learn how to leverage Spark's distributed processing capabilities, utilize its core APIs, and build scalable data pipelines. This course bridges the gap between traditional data analysis and big data processing, empowering professionals to extract meaningful information from vast datasets.

Target Audience:

This course is designed for data professionals seeking to analyze big data with Apache Spark, including:

• Data Scientists
• Data Engineers
• Data Analysts
• Big Data Developers
• Software Engineers
• Anyone involved in processing and analyzing large datasets

Course Objectives:

Upon completion of this Big Data Analytics with Apache Spark course, participants will be able to:

• Understand the fundamentals of big data and Apache Spark.
• Set up and configure a Spark environment.
• Utilize Spark's core APIs (RDDs, DataFrames, Datasets).
• Implement data processing and transformation using Spark.
• Perform data analysis and visualization with Spark.
• Understand Spark's distributed processing capabilities.
• Implement Spark SQL for querying structured data.
• Utilize Spark Streaming for real-time data processing.
• Optimize Spark applications for performance and scalability.
• Understand Spark's machine learning capabilities (MLlib).
• Enhance their ability to process and analyze large datasets.
• Improve their organization's big data analytics capabilities.
• Contribute to improved data-driven decision-making.
• Stay up-to-date with the latest trends and best practices in big data analytics with Spark.
• Become a more knowledgeable and effective big data professional.
• Understand ethical considerations in big data analytics.
• Learn how to use Spark tools and platforms effectively.

DURATION

10 Days

COURSE CONTENT

Module 1: Introduction to Big Data and Apache Spark

• Understanding the challenges of big data and the need for distributed processing.
• Overview of Apache Spark architecture and components.
• Understanding Spark's core concepts (RDDs, DataFrames, Datasets).
• Setting up a Spark development environment (local, cluster).
• Understanding Spark's ecosystem and use cases.

Module 2: Spark Core: Resilient Distributed Datasets (RDDs)

• Understanding RDDs and their characteristics.
• Creating and manipulating RDDs using transformations and actions.
• Understanding RDD persistence and caching.
• Implementing RDD partitioning and data locality.
• Utilizing RDDs for low-level data processing.

Module 3: Spark SQL and DataFrames

• Understanding Spark SQL and DataFrames.
• Creating DataFrames from various data sources (CSV, JSON, Parquet).
• Performing data transformations and aggregations using DataFrames.
• Utilizing Spark SQL for querying DataFrames.
• Understanding DataFrame schemas and data types.

Module 4: Spark Datasets and Type-Safe Operations

• Understanding Datasets and their benefits.
• Creating Datasets from various data sources.
• Performing type-safe operations on Datasets.
• Understanding Encoders and serialization.
• Utilizing Datasets for structured data processing.

Module 5: Spark Data Sources and Formats

• Understanding different data sources and formats (CSV, JSON, Parquet, Avro).
• Reading and writing data from various data sources.
• Utilizing custom data sources and formats.
• Understanding data partitioning and bucketing.
• Implementing data compression and optimization.

Module 6: Spark Transformations and Actions

• Understanding different types of Spark transformations (map, filter, reduce, join).
• Understanding different types of Spark actions (collect, count, save).
• Implementing complex data transformations and aggregations.
• Understanding lazy evaluation and execution plans.
• Utilizing Spark's functional programming capabilities.

Module 7: Spark SQL and Data Warehousing

• Understanding Spark SQL for data warehousing and analytics.
• Creating and managing tables and views.
• Utilizing Spark SQL for complex queries and aggregations.
• Understanding Spark SQL optimization techniques.
• Implementing data warehousing use cases with Spark SQL.

Module 8: Spark Streaming and Real-Time Data Processing

• Understanding Spark Streaming and its applications.
• Creating and managing Spark Streaming applications.
• Utilizing different streaming sources (Kafka, Flume, TCP sockets).
• Implementing windowing and stateful operations.
• Understanding micro-batching and fault tolerance.

Module 9: Spark Structured Streaming

• Understanding Structured Streaming and its benefits.
• Creating and managing Structured Streaming applications.
• Utilizing different streaming sources and sinks.
• Implementing event-time processing and watermarking.
• Understanding continuous processing and triggers.

Module 10: Spark Machine Learning Library (MLlib)

• Understanding Spark MLlib and its capabilities.
• Utilizing MLlib for classification, regression, and clustering.
• Implementing feature engineering and model evaluation.
• Understanding MLlib pipelines and transformers.
• Utilizing MLlib for recommendation systems.

Module 11: Spark Performance Tuning and Optimization

• Understanding Spark performance tuning techniques.
• Optimizing Spark configurations and resource allocation.
• Utilizing Spark caching and persistence effectively.
• Understanding data partitioning and shuffling.
• Monitoring and troubleshooting Spark applications.

Module 12: Spark Deployment and Cluster Management

• Understanding different Spark deployment modes (local, standalone, YARN, Mesos).
• Deploying Spark applications on clusters.
• Utilizing cluster management tools and services.
• Understanding resource management and scheduling.
• Implementing Spark application monitoring and logging.

Module 13: Advanced Spark Concepts and Techniques

• Understanding Spark's internal architecture and execution model.
• Implementing custom Spark transformations and actions.
• Utilizing Spark's advanced features (accumulator, broadcast variables).
• Understanding Spark's graph processing capabilities (GraphX).
• Utilizing Spark for advanced data analytics and machine learning.

Module 14: Big Data Analytics Use Cases and Applications

• Exploring real-world big data analytics use cases.
• Analyzing case studies of Spark adoption.
• Understanding the benefits and challenges of big data analytics.
• Developing strategies for implementing big data analytics projects.
• Understanding the future of big data analytics.

Module 15: Spark Best Practices and Future Trends

• Understanding Spark best practices for large-scale deployments.
• Implementing data governance and compliance in Spark applications.
• Exploring emerging Spark technologies and trends.
• Understanding the impact of Spark on data science and engineering.
• Continuous learning and professional development in Spark.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 5 working days before commencement of the training.