Apache Spark in the Cloud Training Course

This instructor-led live training in Sweden (online or onsite) targets intermediate-level data scientists and engineers who intend to utilise 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 entails the examination of extensive and diverse datasets to identify correlations, hidden patterns, and other valuable insights.

The healthcare sector manages vast volumes of complex, heterogeneous medical and clinical data. Leveraging big data analytics on health records offers significant potential for deriving insights that enhance healthcare delivery. However, the sheer scale of these datasets presents substantial challenges for analysis and practical implementation within clinical settings.

In this instructor-led, remote live training, participants will learn how to conduct big data analytics in healthcare by progressing through a series of practical, live laboratory exercises.

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

• Install and configure big data analytics tools, including Hadoop MapReduce and Spark
• Understand the specific characteristics of medical data
• Apply big data techniques to manage and analyze medical data
• Study big data systems and algorithms within the context of healthcare applications

Audience

• Developers
• Data Scientists

Format of the Course

• A mix of lectures, discussions, exercises, and extensive hands-on practice.

Note

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

This instructor-led, live training in Sweden (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 Sweden (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.

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

• Install and configure various Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand and select the most suitable framework for the task at hand.
• Process data continuously, concurrently, and in a record-by-record manner.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

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

This instructor-led live training in Sweden (online or onsite) is designed for data scientists seeking to use the SMACK stack to develop 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.
• Perform data analysis with Spark and Scala.
• Manage unstructured data using Apache Cassandra.

This instructor-led, live training in Sweden (online or onsite) is aimed at engineers who wish to set up and deploy 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 Sweden (online or onsite) targets beginner to intermediate system administrators who wish to deploy, maintain, and optimize Spark clusters.

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

• Install and configure Apache Spark in diverse 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 an understanding of how Spark integrates into the Big Data ecosystem and learn to utilize it for data analysis. The curriculum covers the Spark shell for interactive analysis, 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 Sweden (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, participants will learn how to utilize Python and Spark together to analyze big data through 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 Sweden (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 serves as Apache Spark's dedicated module for handling both structured and unstructured data. It provides insights into the data's structure as well as the computations being executed, enabling the system to apply various optimizations. Spark SQL is commonly utilized for two primary purposes:
- Executing SQL queries.
- Accessing data from an existing Hive installation.

Through this instructor-led live training (available onsite or remotely), participants will gain the skills to analyze diverse types of data sets using Spark SQL.

Upon completing this training, participants will be capable of:

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

Course Format

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

Course Customization Options

• For requests regarding customized training for this course, please contact us to arrange details.

Stratio is a unified platform centered on data that combines big data capabilities, artificial intelligence, and governance into one cohesive solution. The Rocket and Intelligence modules within Stratio facilitate swift data exploration, transformation, and sophisticated analytics in enterprise settings.

This instructor-led live training (available online or onsite) is designed for intermediate-level data professionals looking to leverage the Rocket and Intelligence modules in Stratio effectively with PySpark. The curriculum emphasizes looping structures, user-defined functions, and complex data logic.

Upon completing this training, participants will be able to:

• Navigate and utilize the Stratio platform through the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis.
• Utilize loops and conditional logic to manage data workflows and execute feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to enable reusable data operations in PySpark.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

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