Big Data Analytics in Health Training Course

Target Audience:

This course is designed for IT professionals seeking solutions for storing and processing large-scale datasets within a distributed system environment.

Course Objective:

To provide in-depth expertise in Apache Hadoop cluster administration.

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

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

• Configure 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 instructor-led live training in Slovakia (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 core components of the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Utilize the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
• Configure HDFS to serve as a storage engine for on-premise Spark deployments.
• Set up Spark to access alternative storage solutions, such as Amazon S3, as well as NoSQL database systems like 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 at Slovakia (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 different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand and select the most appropriate framework for the job.
• Process data continuously, concurrently, and in a record-by-record fashion.
• 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 training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows with PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

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

Upon completion, participants will be able to:

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

While Apache Spark has a steep initial learning curve that demands significant effort before yielding results, this course is designed to help learners quickly overcome that initial hurdle. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with both Python and Scala APIs. They will also gain a solid understanding of executors, tasks, and other core concepts. Aligned with industry best practices, the course places a strong emphasis on cloud deployment, specifically focusing on Databricks and AWS. Additionally, students will learn to differentiate between AWS EMR and AWS Glue, with special attention to AWS Glue as one of the latest Spark-based services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

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 techniques for performing data analysis using Spark. Key topics include the Spark shell for interactive analysis, internal mechanisms of Spark, its APIs, Spark SQL, Spark Streaming, as well as Machine Learning and GraphX capabilities.

AUDIENCE :

Developers / Data Analysts

This instructor-led live training in Slovakia (online or onsite) targets 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 Slovakia, 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 Slovakia (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 the component within Apache Spark designed for handling both structured and unstructured data. It exposes details regarding the data's structure alongside the computations being executed, enabling various performance optimizations. Spark SQL is commonly utilized for:
- running SQL queries.
- accessing data from an existing Hive deployment.

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

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

• Installing and configuring Spark SQL.
• Conducting data analysis with Spark SQL.
• Executing queries on datasets in various formats.
• Visualizing data and the outcomes of queries.

Course Format

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

Customization Options

• For customized training requests, please reach out to us to make arrangements.

Stratio is a data-centric platform that seamlessly integrates big data, artificial intelligence, and governance into a unified solution. Its Rocket and Intelligence modules empower organizations to conduct rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led live training (available online or on-site) targets intermediate-level data professionals who want to leverage the Rocket and Intelligence modules in Stratio effectively using 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 operate 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 execute feature engineering.
• Develop and manage user-defined functions (UDFs) to enable reusable data operations in PySpark.

Course Format

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

Course Customization Options

• For customized training requirements for this course, please contact us to arrange your schedule.