Spark for Developers Training Course

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use 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 refers to the process of analysing large and diverse datasets to uncover correlations, hidden patterns, and other valuable insights.

The healthcare sector contains vast quantities of complex, heterogeneous medical and clinical data. Applying big data analytics to health data offers significant potential for generating insights that can improve healthcare delivery. However, the sheer size and complexity of these datasets present considerable challenges in terms of analysis and practical implementation within clinical environments.

In this instructor-led, live remote training, participants will learn how to conduct big data analytics in the health domain by progressing through 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 key characteristics of medical data
• Apply big data techniques to manage and analyse medical data
• Examine big data systems and algorithms in the context of healthcare applications

Audience

• Developers
• Data Scientists

Course Format

• A combination of lecture, discussion, exercises, and extensive hands-on practice.

Note

• To request a customised training session for this course, please contact us to make arrangements.

This instructor-led, live training in Uzbekistan (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 session in Uzbekistan (onsite or remote), participants will learn how to set up and integrate various Stream Processing frameworks with existing big data storage systems, 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 suitable framework for specific tasks.
• 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.

Renowned for its clear syntax and code readability, Python is a high-level programming language. Spark serves as a powerful engine for processing big data, enabling efficient querying, analysis, and transformation. PySpark bridges the two, allowing users to interface Spark with Python.

Target Audience: This course is designed for intermediate-level banking professionals who are already familiar with Python and Spark and wish to enhance their expertise in big data processing and machine learning.

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

This instructor-led live training in Uzbekistan (online or onsite) is designed for data scientists who aim 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 Uzbekistan (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 Uzbekistan (online or onsite) is aimed at beginner-level 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.

The initial learning curve for Apache Spark can be steep, requiring significant effort before yielding results. This course is designed to help you navigate that challenging early stage. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and gain proficiency in both Python and Scala APIs. Learners will also develop a solid understanding of executors, tasks, and other core concepts. Aligned with industry best practices, the course places strong emphasis on cloud deployment strategies, specifically within Databricks and AWS environments. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS’s most recent Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

This instructor-led live training in Uzbekistan (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 Uzbekistan, participants will learn how to leverage Python and Spark together to analyze big data while completing hands-on exercises.

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

• Master the use of Spark with Python to analyze Big Data.
• Complete exercises that simulate real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

This instructor-led live training in Uzbekistan (available online or on-site) is designed for developers who want 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 designed for working with both structured and unstructured data. It provides insights into the data's structure as well as the computations being executed, enabling performance optimizations. Two of the most common use cases for Spark SQL include:
- Running SQL queries.
- Reading data from an existing Hive installation.

In this instructor-led, live training (available onsite or remotely), participants will learn how to analyze diverse 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 various formats.
• Visualize data and query results.

Course Format

• Interactive lectures and discussions.
• Abundant exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Course Customization Options

• To request a customized version of this course, please contact us to arrange.

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

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to use the Rocket and Intelligence modules in Stratio effectively with PySpark, focusing on looping structures, user-defined functions, and advanced data logic.

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

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

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.