Hadoop Training in Tashkent

This instructor-led live training in Tashkent (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.

Audience:

This course is designed for IT specialists seeking solutions to store and process large datasets within a distributed system environment.

Goal:

To develop in-depth expertise in Hadoop cluster administration.

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.

Apache Hadoop is the most widely adopted framework for processing Big Data across clusters of servers. In this three-day course (optionally extended to four days), participants will explore the business benefits and real-world use cases of Hadoop and its ecosystem. They will learn how to plan cluster deployment and growth, as well as how to install, maintain, monitor, troubleshoot, and optimize Hadoop environments. The course also includes hands-on practice with bulk data loading, familiarisation with various Hadoop distributions, and managing tools within the Hadoop ecosystem. The programme concludes with a discussion on securing the cluster using Kerberos.

“…The materials were exceptionally well-prepared and comprehensively covered. The lab sessions were highly beneficial and meticulously organised.”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

Lectures and hands-on labs, with an approximate balance of 60% lectures and 40% labs.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This course provides developers with an introduction to the key components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase. These advanced programming techniques will be beneficial to experienced Hadoop developers.

Audience: developers

Duration: three days

Format: lectures (50%) and hands-on labs (50%).

Target Audience:

This course aims to simplify big data and Hadoop technologies, demonstrating that they are accessible and easy to understand.

This instructor-led, live training in Tashkent (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.

This course provides an introduction to HBase, a NoSQL database built on top of Hadoop. It is designed for developers who intend to build applications using HBase, as well as administrators responsible for managing HBase clusters.

The course guides developers through HBase architecture, data modeling, and application development. It also covers the integration of MapReduce with HBase and addresses key administrative topics related to performance optimization. The training is highly practical, featuring numerous lab exercises.

Duration: 3 days

Audience: Developers & Administrators

Informatica with Big Data (BDM) is a program designed to equip data professionals with the skills to develop, manage, and analyze large datasets, utilizing the latest technologies and architectures in the Big Data field. The course covers the entire data lifecycle, from ingestion, integration, cleansing, and curation to analytics and the exposure and consumption of big data services.
In this course, participants will explore solutions that process large datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. The course also provides hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric to understand big data technologies such as MapReduce and Hadoop. By the end of the course, learners will be capable of creating end-to-end data solutions using Informatica and its associated Big Data offerings.

Apache NiFi is an open-source, flow-based data integration and event-processing platform. It enables automated, real-time data routing, transformation, and system mediation between disparate systems, with a web-based UI and fine-grained control.

This instructor-led, live training (onsite or remote) is aimed at intermediate-level administrators and engineers who wish to deploy, manage, secure, and optimize NiFi dataflows in production environments.

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

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows from varied sources and sinks.
• Implement flow automation, routing, and transformation logic.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lecture with real-world architecture discussion.
• Hands-on labs: building, deploying, and managing flows.
• Scenario-based exercises in a live-lab environment.

Course Customization Options

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

In this instructor-led, live training in Tashkent, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.

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

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.