Apache Spark in the Cloud Training Course

This instructor-led live training in Mexico (online or onsite) is tailored for intermediate-level data scientists and engineers looking to utilize Google Colab and Apache Spark for big data processing and analytics.

By the conclusion 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 refers to the process of scrutinizing vast volumes of diverse data sets to uncover correlations, hidden patterns, and other valuable insights.

The healthcare sector generates enormous amounts of complex, heterogeneous medical and clinical data. Applying big data analytics to this information holds significant potential for deriving insights that can improve healthcare delivery. However, the sheer scale of these datasets presents substantial challenges for analysis and practical application in clinical environments.

Through this instructor-led, live remote training, participants will learn how to conduct big data analytics in healthcare by completing a series of hands-on, live-lab exercises.

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

• Install and configure big data analytics tools, such as Hadoop MapReduce and Spark
• Understand the characteristics of medical data
• Apply big data techniques to manage medical data
• Study big data systems and algorithms within the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course

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

Note

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

This instructor-led, live training in Mexico (online or onsite) targets 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.
• Grasp the four key components of the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Leverage the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
• Configure HDFS to function as the storage engine for on-premise Spark deployments.
• Configure Spark to access alternative storage solutions like Amazon S3 and NoSQL databases such as Redis, Elasticsearch, Couchbase, Aerospike, and others.
• Perform administrative tasks including provisioning, management, monitoring, and securing an Apache Hadoop cluster.

In this instructor-led, live training in Mexico (onsite or remote), participants will learn how to set up and integrate various 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 different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand and select the most appropriate framework for the task.
• Process data continuously, concurrently, and on a record-by-record basis.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
• Integrate the most suitable stream processing library with enterprise applications and microservices.

This training offers a hands-on introduction to creating scalable data processing and Machine Learning workflows using PySpark. Attendees will gain insights into how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently handle large datasets by applying distributed computing principles.

This instructor-led, live training in Mexico (online or onsite) is designed for data scientists aiming to use 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 a cluster infrastructure with Apache Mesos and Docker.
• Analyze data with Spark and Scala.
• Manage unstructured data with Apache Cassandra.

This instructor-led, live training in Mexico (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 Mexico (online or onsite) is designed for beginner to intermediate-level 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 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 introduces Apache Spark, teaching students how Spark integrates into the Big Data ecosystem and how to leverage it for data analysis. Topics include the Spark shell for interactive analysis, Spark internals, APIs, Spark SQL, Spark Streaming, as well as machine learning and GraphX.

AUDIENCE :

Developers and Data Analysts

This instructor-led live training in Mexico (online or onsite) is targeted 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 Mexico, 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 Mexico (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 Apache Spark's module for working with structured and unstructured data. Spark SQL provides information about the structure of the data as well as the computation being performed. This information can be used to perform optimizations. Two common uses for Spark SQL are:
- to execute SQL queries.
- to read data from an existing Hive installation.

In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL.

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

• Install and configure Spark SQL.
• Perform data analysis using Spark SQL.
• Query data sets in different formats.
• Visualize data and query results.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Stratio is a comprehensive, data-focused platform that unifies big data capabilities, artificial intelligence, and governance into one cohesive solution. Its Rocket and Intelligence modules facilitate swift data exploration, transformation, and sophisticated analytics tailored for enterprise needs.

This live training, led by an instructor and available either online or in-person, is designed for data professionals with an intermediate skill level. The goal is to help participants effectively leverage the Rocket and Intelligence modules in Stratio using PySpark, with a specific focus on loop structures, user-defined functions, and complex data logic.

Upon completing this training, participants will be able to:

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

Course Format

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

Customization Options

• For those interested in a customized version of this course, please reach out to us to arrange details.