Infomatica with Big Data (BDM) Training Course

Audience:

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

Goal:

To provide in-depth knowledge regarding the administration of Hadoop clusters.

Big data analytics is the process of scrutinizing extensive and diverse datasets to reveal correlations, latent patterns, and other valuable insights.

The healthcare sector generates vast volumes of complex, heterogeneous medical and clinical data. Leveraging big data analytics within this domain holds immense potential for deriving insights that enhance healthcare delivery. However, the sheer scale of these datasets presents significant challenges for analysis and practical implementation 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.

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

• Install and configure big data analytics tools, including Hadoop MapReduce and Spark
• Comprehend the characteristics of medical data
• Apply big data techniques to manage medical data
• Explore big data systems and algorithms in the context of healthcare applications

Target Audience

• Developers
• Data Scientists

Course Format

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

Note

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

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. Over the course of three days (with an optional fourth day), participants will explore the business advantages and practical applications of Hadoop and its surrounding ecosystem. The training covers essential skills such as planning cluster deployment and scaling, installing, maintaining, monitoring, troubleshooting, and optimizing Hadoop environments. Attendees will also gain hands-on experience with bulk data loading, explore various Hadoop distributions, and learn to install and manage tools within the Hadoop ecosystem. The course concludes with a session on securing clusters using Kerberos.

“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

A combination of lectures and hands-on labs, with an approximate split of 60% instruction and 40% practical lab work.

Apache Hadoop stands out as the leading framework for processing Big Data across server clusters. This course offers developers a comprehensive introduction to key components within the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop stands out as one of the most widely adopted frameworks for managing Big Data across server clusters. This course provides an in-depth exploration of data management within HDFS, alongside advanced techniques in Pig, Hive, and HBase. These sophisticated programming methods are particularly valuable for seasoned Hadoop developers.

Audience: developers

Duration: three days

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

Target Audience:

This course is designed to demystify big data and Hadoop technologies, demonstrating that these concepts are accessible and easy to grasp.

This instructor-led live training in Serbia (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 ecosystem: 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 introduces HBase, a NoSQL database built on Hadoop. It is designed for developers who will use HBase to build applications, as well as administrators responsible for managing HBase clusters.

We will guide developers through HBase architecture, data modeling, and application development. The course also covers using MapReduce with HBase and discusses administrative topics focused on performance optimization. The training is highly practical, featuring numerous lab exercises.

Duration: 3 days

Audience: Developers and Administrators

Apache NiFi is an open-source platform designed for flow-based data integration and event processing. It facilitates automated, real-time data routing, transformation, and system mediation between diverse systems, supported by a web-based UI and granular control mechanisms.

This instructor-led training, available both onsite and remotely, targets intermediate-level administrators and engineers looking to deploy, manage, secure, and optimize NiFi dataflows within production environments.

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

• Installing, configuring, and maintaining Apache NiFi clusters.
• Designing and managing dataflows across various sources and sinks.
• Implementing logic for flow automation, routing, and data transformation.
• Optimizing performance, monitoring operations, and troubleshooting issues.

Course Format

• Interactive lectures featuring discussions on real-world architecture.
• Hands-on labs focused on building, deploying, and managing flows.
• Scenario-based exercises conducted in a live-lab environment.

Customization Options

• For customized training tailored to your needs, please contact us to arrange.

In this instructor-led live training in Serbia, 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.

This instructor-led, live training in Serbia (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.