Greenplum Architecture and Data Modeling Training Course

Audience:

This course is designed for IT professionals seeking to establish solutions for storing and processing large volumes of data within a distributed system environment.

Goal:

To provide in-depth knowledge of Hadoop cluster administration.

This instructor-led, live training in Australia (online or onsite) is designed for intermediate-level data scientists and engineers who wish to leverage Google Colab and Apache Spark for big data processing and analytics.

Upon completing this training, participants will be able to:

• Set up a big data environment using Google Colab and Spark.
• Process and analyse large datasets efficiently with Apache Spark.
• Visualise big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Big data analytics entails the examination of extensive and diverse datasets to identify correlations, uncover hidden patterns, and extract valuable insights.

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

Through this instructor-led, live remote training, participants will gain practical experience in performing big data analytics in healthcare by completing a series of hands-on 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 distinctive characteristics of medical data
• Apply big data techniques to manage and analyse medical data
• Explore big data systems and algorithms within the context of healthcare applications

Audience

• Developers
• Data Scientists

Course Format

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

Note

• To arrange customised training for this course, please contact us.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This course, spanning three days (with an optional fourth day), enables attendees to understand the business advantages and practical applications of Hadoop and its ecosystem. Participants will learn to plan for cluster deployment and expansion, as well as to install, maintain, monitor, troubleshoot, and optimise Hadoop. Hands-on practice includes bulk data loading, exploring various Hadoop distributions, and installing and managing Hadoop ecosystem tools. The course concludes with a discussion on securing clusters using Kerberos.

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

Audience

Hadoop administrators

Format

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

Apache Hadoop is the predominant framework used for processing Big Data across server clusters. This course introduces developers to the key components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop stands as one of the most widely adopted frameworks for processing Big Data across server clusters. This course provides an in-depth look at data management within HDFS, alongside advanced techniques using Pig, Hive, and HBase. These sophisticated programming strategies are designed to benefit experienced Hadoop developers.

Audience: developers

Duration: three days

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

Target Audience:

This course aims to demystify big data and Hadoop technology, demonstrating that it is accessible and not overly complex to understand.

This instructor-led live training in Australia (online or onsite) is tailored for system administrators who wish to learn how to set up, deploy, and manage Hadoop clusters within their organisation.

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 the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
• Configure HDFS to operate as a storage engine for on-premise Spark deployments.
• Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems like Redis, Elasticsearch, Couchbase, Aerospike, and others.
• Carry out administrative tasks such as provisioning, management, monitoring, and securing an Apache Hadoop cluster.

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

Participants will be guided through HBase architecture, data modelling, and application development. The course also covers the use of MapReduce with HBase and addresses key administration topics, focusing on performance optimisation. The training is highly practical, featuring numerous lab exercises.

Duration: 3 days

Audience: Developers & Administrators

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 optimise 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.
• Optimise 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 held in Australia, participants will learn the fundamentals of flow-based programming while developing a series 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 training offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by leveraging distributed computing principles.

In this instructor-led, live training in Australia, participants will learn how to use Python and Spark together to analyse 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 analyse 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 Australia (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.

Stratio serves as a data-centric platform that unifies big data, artificial intelligence, and governance within a single solution. Its Rocket and Intelligence modules empower organisations to conduct rapid data exploration, transformation, and advanced analytics in enterprise settings.

This instructor-led live training, available online or onsite, is designed for intermediate-level data professionals looking to effectively utilise the Rocket and Intelligence modules in Stratio with PySpark. The course focuses on looping structures, user-defined functions, and advanced data logic.

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

• Navigate and operate within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis.
• Employ loops and conditional logic to manage data workflows and feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations in PySpark.

Course Format

• Interactive lectures and group discussions.
• Ample exercises and practical practice.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

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