Big Data Analytics in Health Training Course

Target Audience:

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

Objective:

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

This instructor-led live training in Turkey (online or onsite) is tailored for intermediate-level data scientists and engineers who want to utilize 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 instructor-led live training in Turkey (online or on-site) is designed for system administrators who want to learn how to set up, deploy, and manage Hadoop clusters within their organizations.

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 Turkey (onsite or remote), participants will learn how to set up and integrate various Stream Processing frameworks with existing big data storage systems, related software applications, and microservices.

Upon completion 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 suitable framework for specific requirements.
• Process data continuously, concurrently, and on a record-by-record basis.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, and other systems.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will explore how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently process extensive datasets using distributed computing principles.

This instructor-led, live training in Turkey (online or onsite) is designed for data scientists who wish to utilize 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 Turkey (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 Turkey (online or onsite) targets beginner to intermediate system administrators seeking to deploy, maintain, and optimize Spark clusters.

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

• Install and configure Apache Spark across diverse environments.
• Manage cluster resources and oversee Spark applications.
• Enhance the performance of Spark clusters.
• Implement security protocols and ensure high availability.
• Debug and resolve common Spark issues.

Although the initial learning curve for Apache Spark can be steep, requiring significant effort to see results, this course is designed to help you navigate that challenging phase quickly. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and master both the Python and Scala APIs. You will also gain a deep understanding of executors, tasks, and other core concepts. Aligned with industry best practices, the curriculum places strong emphasis on cloud deployment, particularly within Databricks and AWS environments. Students will learn to differentiate between AWS EMR and AWS Glue, one of AWS’s latest Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course provides an introduction to Apache Spark. Participants will explore how Spark integrates into the Big Data ecosystem and gain skills in using Spark for data analysis. Key topics include using the Spark shell for interactive analysis, understanding Spark internals, working with Spark APIs, leveraging Spark SQL, implementing Spark Streaming, and applying machine learning and GraphX capabilities.

AUDIENCE :

Developers / Data Analysts

This instructor-led live training in Turkey (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 begin building NLP pipelines with Spark NLP.
• Understand the features, architecture, and benefits of using Spark NLP.
• Utilize 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 to real-world use cases (clinical data, customer behavior insights, etc.).

In this instructor-led, live training in Turkey, 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 Turkey (online or onsite) is designed for developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex datasets.

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

• Configure the necessary environment to begin processing big data using Spark, Hadoop, and Python.
• Gain a comprehensive understanding of the features, core components, and architecture of Spark and Hadoop.
• Master the integration of Spark, Hadoop, and Python for efficient big data processing.
• Explore key tools within the Spark ecosystem, including Spark MLlib, Spark Streaming, Kafka, Sqoop, and Flume.
• Develop collaborative filtering recommendation systems akin to those used by Netflix, YouTube, Amazon, Spotify, and Google.
• Utilize Apache Mahout to scale machine learning algorithms.

Spark SQL serves as Apache Spark's module designed for handling both structured and unstructured data. It offers insights into the data's structure alongside the computations being executed, enabling the system to apply performance optimizations. The primary applications of Spark SQL include:
- Executing SQL queries.
- Reading data from an existing Hive installation.

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

Upon completing 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 outcomes.

Course Format

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

Course Customization Options

• For customized training requests, please contact us to arrange accordingly.

Stratio is a comprehensive, data-centric platform that unifies big data capabilities, artificial intelligence, and governance into a single cohesive solution. Its Rocket and Intelligence modules empower organizations to perform rapid data exploration, transformation, and advanced analytics within enterprise environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals aiming to master the Rocket and Intelligence modules in Stratio using PySpark. The curriculum emphasizes looping structures, user-defined functions, and sophisticated data logic.

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

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

Course Format

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

Customization Options

• For organizations seeking customized training for this course, please contact us to arrange a tailored program.