Big Data - Data Science Training Course

This instructor-led, live training in New Zealand (delivered either online or on-site) is designed for intermediate-level data scientists and engineers who wish to leverage 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 analyse large datasets efficiently with Apache Spark.
• Visualise big data within a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Big data analytics involves the process of examining large volumes of diverse datasets to uncover correlations, hidden patterns, and other valuable insights.

The health sector manages vast quantities of complex, heterogeneous medical and clinical data. Applying big data analytics to health data holds significant potential for generating insights that can enhance healthcare delivery. However, the sheer scale of these datasets presents considerable challenges for analysis and practical application in clinical settings.

In this instructor-led, live training (delivered remotely), participants will learn how to perform big data analytics in the health domain by working 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 characteristics of medical data
• Apply big data techniques to manage medical data effectively
• Examine big data systems and algorithms within the context of health applications

Audience

• Developers
• Data Scientists

Course Format

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

Note

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

This instructor-led, live training in New Zealand (online or on-site) is tailored for system administrators who want 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 core components of the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Leverage the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or even thousands of nodes.
• Configure HDFS as the storage engine for on-premises Spark deployments.
• Set up Spark to interface with alternative storage solutions such as Amazon S3 and NoSQL database systems including Redis, Elasticsearch, Couchbase, Aerospike, and others.
• Perform key administrative tasks such as provisioning, managing, monitoring, and securing an Apache Hadoop cluster.

In this instructor-led, live training in New Zealand (delivered onsite or remotely), participants will learn how to set up and integrate various Stream Processing frameworks with existing big data storage systems, related 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 appropriate framework for the task at hand.
• Process data continuously, concurrently, and on a record-by-record basis.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, and other storage systems.
• Integrate the most suitable stream processing library with enterprise applications and microservices.

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

This instructor-led, live training in New Zealand (delivered either online or on-site) is designed for data scientists who want to utilise the SMACK stack to construct 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 cluster infrastructure using Apache Mesos and Docker.
• Analyse data using Spark and Scala.
• Manage unstructured data with Apache Cassandra.

This instructor-led, live training in New Zealand (online or on-site) is intended for engineers who wish to set up and deploy the Apache Spark system to process extremely large volumes of data.

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

• Install and configure Apache Spark.
• Efficiently process and analyse very large data sets.
• Understand the differences between Apache Spark and Hadoop MapReduce, and when to use each.
• Integrate Apache Spark with other machine learning tools.

This instructor-led, live training in New Zealand (available online or on-site) is designed for system administrators at beginner to intermediate levels who wish to deploy, maintain, and optimise Spark clusters.

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

• Install and configure Apache Spark across various environments.
• Manage cluster resources and monitor Spark applications.
• Optimise the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

The learning curve for Apache Spark can be steep at the outset, requiring significant effort before seeing initial returns. This course is designed to help learners navigate that challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, gain proficiency in both Python and Scala APIs, and develop a solid understanding of executors and tasks. Emphasising best practices, the course places strong focus on cloud deployment, particularly with Databricks and AWS. Students will also explore the key differences between AWS EMR and AWS Glue, one of AWS's latest Spark services.

AUDIENCE:

Data Engineers, DevOps professionals, Data Scientists

OBJECTIVE:

This course introduces Apache Spark. Participants will learn how Spark integrates into the Big Data ecosystem and how to leverage it for data analysis. The curriculum covers the Spark shell for interactive analysis, Spark internals, Spark APIs, Spark SQL, Spark Streaming, Machine Learning with MLlib, and graph processing with GraphX.

AUDIENCE:

Developers and Data Analysts

This instructor-led, live training in New Zealand (available online or on-site) is tailored for data scientists and developers who wish to leverage Spark NLP, built atop 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.
• Leverage pre-trained models available in Spark NLP to implement text processing workflows.
• Learn how to build, train, and scale Spark NLP models for production-grade projects.
• Apply classification, inference, and sentiment analysis to real-world scenarios (clinical data, customer behaviour insights, etc.).

In this instructor-led, live training in New Zealand, participants will learn how to combine Python and Spark to analyse big data through 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 mirror real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

This instructor-led, live training in New Zealand (available online or on-site) is tailored for developers seeking to use and integrate Spark, Hadoop, and Python to process, analyse, and transform large and complex data sets.

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

• Set up the necessary environment to begin processing big data using Spark, Hadoop, and Python.
• Understand the key features, core components, and architecture of both Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for efficient big data processing.
• Explore tools within the Spark ecosystem, including Spark MLlib, Spark Streaming, Kafka, Sqoop, and Flume.
• Build collaborative filtering recommendation systems similar to those used by Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.

Spark SQL is Apache Spark's module for working with structured and unstructured data. Spark SQL provides information about the structure of the data as well as the computation being performed. This information can be used to perform optimisations. Two common uses for Spark SQL are:
- to execute SQL queries.
- to read data from an existing Hive installation.

In this instructor-led, live training (onsite or remote), participants will learn how to analyse various types of 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 different formats.
• Visualise data and query results.

Format of the Course

• Interactive lecture and discussion.
• Plenty of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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

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

This instructor-led, live training (available online or on-site) is designed for intermediate-level data professionals who want to effectively use the Rocket and Intelligence modules in Stratio with PySpark, with a focus 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 the 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.

Course Format

• Interactive lectures and discussions.
• Plenty of exercises and hands-on practice.
• Live-lab implementation in a real-world environment.

Course Customisation Options

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