Apache Spark in the Cloud Training Course

This instructor-led live training in Malaysia (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics.

By the end of this training, participants will be able to:

• Set up a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process vast datasets efficiently by applying distributed computing principles.

This instructor-led, live training in Malaysia (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark systems for processing very large amounts of data.

By the end of this training, participants will be able to:

• Install and configure Apache Spark.
• Quickly process and analyze very large datasets.
• Understand the differences between Apache Spark and Hadoop MapReduce and when to use each.
• Integrate Apache Spark with other machine learning tools.

This instructor-led live training in Malaysia (online or onsite) is designed for beginner to intermediate system administrators seeking to deploy, maintain, and optimize Spark clusters.

By the end of this training, participants will be able to:

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

OBJECTIVE:

This course provides an introduction to Apache Spark. Participants will gain insights into how Spark integrates within the Big Data ecosystem and learn to leverage it for data analysis. The curriculum encompasses the Spark shell for interactive data exploration, Spark internals, Spark APIs, Spark SQL, Spark Streaming, as well as machine learning and GraphX capabilities.

AUDIENCE:

Developers and Data Analysts

This instructor-led, live training in Malaysia (online or onsite) is designed for data scientists and developers who wish to leverage Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.

Upon completion of this training, participants will be able to:

• Configure the necessary development environment to begin constructing NLP pipelines with Spark NLP.
• Gain an understanding of the features, architecture, and advantages of employing Spark NLP.
• Utilize pre-trained models available in Spark NLP to execute text processing tasks.
• Learn how to build, train, and scale Spark NLP models for production-grade projects.
• Apply classification, inference, and sentiment analysis techniques to real-world scenarios (such as clinical data and customer behavior insights).

In this instructor-led live training in Malaysia, participants will learn how to use Python and Spark together to analyze big data while engaging in hands-on exercises.

By the end of this training, participants will be able to:

• Learn how to use Spark with Python to analyze Big Data.
• Work on exercises that mimic real world cases.
• Use different tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Malaysia (online or on-site) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyse, and transform large and complex datasets.

By the end of this training, participants will be able to:

• Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
• Understand the features, core components, and architecture of Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for big data processing.
• Explore the tools in the Spark ecosystem (Spark MLlib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
• Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.

Spark SQL serves as Apache Spark's dedicated module for processing both structured and unstructured data. It offers visibility into data structures and the computations being executed, enabling performance optimizations. The primary applications of Spark SQL include executing SQL queries and accessing data from existing Hive installations.

Through this instructor-led live training (available onsite or remotely), participants will gain the skills to analyse diverse datasets using Spark SQL.

Upon completion of this training, participants will be capable of:

• Installing and configuring Spark SQL.
• Conducting data analysis with Spark SQL.
• Querying datasets in various formats.
• Visualizing data and query results.

Course Format

• Interactive lectures and discussions.
• Ample exercises and practice sessions.
• Practical implementation within a live laboratory environment.

Course Customization Options

• To arrange customized training for this course, please contact us.

Stratio is a unified, data-centric platform that consolidates big data management, artificial intelligence, and governance. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics tailored for enterprise settings.

This instructor-led live training, available online or on-site, is designed for intermediate data professionals looking to master the Rocket and Intelligence modules within Stratio using PySpark. Key areas of focus include looping structures, user-defined functions (UDFs), and implementing advanced data logic.

Upon completing this training, participants will be capable of:

• Effectively navigating and utilizing the Stratio platform through its Rocket and Intelligence modules.
• Applying PySpark for data ingestion, transformation, and analytical tasks.
• Employing loops and conditional logic to orchestrate data workflows and execute feature engineering.
• Developing and managing user-defined functions (UDFs) to enable reusable data operations within PySpark.

Course Format

• Engaging lectures accompanied by interactive discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live laboratory environment.

Customization Options

• To arrange customized training for this course, please get in touch with us to discuss your specific requirements.