Apache Spark in the Cloud Training Course

This instructor-led live training in Czech Republic (online or onsite) targets intermediate-level data scientists and engineers who wish to employ 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.

Big data analytics encompasses the methodology of reviewing extensive and diverse datasets to identify correlations, uncover latent patterns, and extract actionable insights.

The healthcare sector generates vast volumes of complex and heterogeneous medical and clinical data. Leveraging big data analytics within this domain holds immense potential for deriving insights that can enhance healthcare delivery. Nevertheless, the sheer scale of these datasets presents significant challenges for analysis and practical implementation in clinical settings.

In this instructor-led, live online training, participants will learn how to execute big data analytics in healthcare by progressing through a series of hands-on laboratory exercises.

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

• Install and configure big data analytics tools, including Hadoop MapReduce and Spark
• Comprehend the characteristics of medical data
• Apply big data techniques to manage and analyze medical data
• Examine big data systems and algorithms within the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course

• A combination of lectures, discussions, exercises, and intensive hands-on practice.

Note

• To request customized training for this course, please contact us to make arrangements.

This instructor-led, live training in Czech Republic (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organization.

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

• Install and configure Apache Hadoop.
• Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.  
• Set up HDFS to operate as storage engine for on-premise Spark deployments.
• Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
• Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.

In this instructor-led, live training in Czech Republic (onsite or remote), participants will learn how to set up and integrate various Stream Processing frameworks with existing big data storage systems, as well as related software applications and microservices.

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

• Install and configure diverse Stream Processing frameworks, including Spark Streaming and Kafka Streaming.
• Understand and select the most suitable framework for specific use cases.
• Process data continuously, concurrently, and on a record-by-record basis.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, and more.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows with PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by leveraging distributed computing principles.

This instructor-led live training in Czech Republic (online or onsite) targets data scientists who wish to leverage the SMACK stack to build data processing platforms for big data solutions.

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

• Implement a data pipeline architecture for processing big data.
• Develop cluster infrastructure using Apache Mesos and Docker.
• Analyze data with Spark and Scala.
• Manage unstructured data with Apache Cassandra.

This instructor-led live training in Czech Republic (online or onsite) is aimed at engineers who wish to set up and deploy an Apache Spark system 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 data sets.
• Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
• Integrate Apache Spark with other machine learning tools.

This instructor-led, live training in Czech Republic (online or onsite) is designed for system administrators with beginner to intermediate skill levels who want to deploy, maintain, and optimize Spark clusters.

Upon completing this training, participants will be able to:

• Install and configure Apache Spark across 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, internal mechanisms of Spark, application programming interfaces (APIs), Spark SQL, Spark Streaming, as well as Machine Learning and GraphX functionalities.

AUDIENCE:

Developers / Data Analysts

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

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

• Set up the necessary development environment to start building NLP pipelines with Spark NLP.
• Understand the features, architecture, and benefits of using Spark NLP.
• Use the pre-trained models available in Spark NLP to implement text processing.
• Learn how to build, train, and scale Spark NLP models for production-grade projects.
• Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).

In this instructor-led, live training in Czech Republic, participants will learn how to use Python and Spark together to analyze big data as they work on 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 Czech Republic (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.

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 is the Apache Spark module dedicated to processing structured and semi-structured data. It exposes metadata regarding data structure and the computations involved, enabling the execution of performance optimizations. Spark SQL is commonly utilized for: - executing SQL queries. - accessing data from an existing Hive deployment.

In this instructor-led live training (available onsite or remotely), participants will learn how to analyze diverse datasets using Spark SQL.

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

• Install and configure Spark SQL.
• Conduct data analysis with Spark SQL.
• Query datasets in various formats.
• Visualize data and query results.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical work.
• Hands-on implementation in a live lab environment.

Customization Options

• To request a customized training program for this course, please contact us to arrange details.

Stratio is a data-centric platform that unifies big data, AI, and governance into a single solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics within enterprise environments.

This instructor-led, live training (available online or onsite) is designed for intermediate-level data professionals who want to effectively leverage the Rocket and Intelligence modules in Stratio with PySpark. The focus is on mastering looping structures, user-defined functions, and implementing advanced data logic.

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

• Navigate and work efficiently within the Stratio platform using its Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis tasks.
• Utilize loops and conditional logic to manage data workflows and feature engineering processes.
• Create and manage user-defined functions (UDFs) to enable reusable data operations in PySpark.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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