35 Data Processing courses delivered Live Online

Duration 1 Days 6 CPD hours This course is intended for The primary audience for this course is data professionals who are familiar with data modeling, extraction, and analytics. It is designed for professionals who are interested in gaining knowledge about Lakehouse architecture, the Microsoft Fabric platform, and how to enable end-to-end analytics using these technologies. Job role: Data Analyst, Data Engineer, Data Scientist Overview Describe end-to-end analytics in Microsoft Fabric Describe core features and capabilities of lakehouses in Microsoft Fabric Create a lakehouse Ingest data into files and tables in a lakehouse Query lakehouse tables with SQL Configure Spark in a Microsoft Fabric workspace Identify suitable scenarios for Spark notebooks and Spark jobs Use Spark dataframes to analyze and transform data Use Spark SQL to query data in tables and views Visualize data in a Spark notebook Understand Delta Lake and delta tables in Microsoft Fabric Create and manage delta tables using Spark Use Spark to query and transform data in delta tables Use delta tables with Spark structured streaming Describe Dataflow (Gen2) capabilities in Microsoft Fabric Create Dataflow (Gen2) solutions to ingest and transform data Include a Dataflow (Gen2) in a pipeline This course is designed to build your foundational skills in data engineering on Microsoft Fabric, focusing on the Lakehouse concept. This course will explore the powerful capabilities of Apache Spark for distributed data processing and the essential techniques for efficient data management, versioning, and reliability by working with Delta Lake tables. This course will also explore data ingestion and orchestration using Dataflows Gen2 and Data Factory pipelines. This course includes a combination of lectures and hands-on exercises that will prepare you to work with lakehouses in Microsoft Fabric. Introduction to end-to-end analytics using Microsoft Fabric Explore end-to-end analytics with Microsoft Fabric Data teams and Microsoft Fabric Enable and use Microsoft Fabric Knowledge Check Get started with lakehouses in Microsoft Fabric Explore the Microsoft Fabric Lakehouse Work with Microsoft Fabric Lakehouses Exercise - Create and ingest data with a Microsoft Fabric Lakehouse Use Apache Spark in Microsoft Fabric Prepare to use Apache Spark Run Spark code Work with data in a Spark dataframe Work with data using Spark SQL Visualize data in a Spark notebook Exercise - Analyze data with Apache Spark Work with Delta Lake Tables in Microsoft Fabric Understand Delta Lake Create delta tables Work with delta tables in Spark Use delta tables with streaming data Exercise - Use delta tables in Apache Spark Ingest Data with DataFlows Gen2 in Microsoft Fabric Understand Dataflows (Gen2) in Microsoft Fabric Explore Dataflows (Gen2) in Microsoft Fabric Integrate Dataflows (Gen2) and Pipelines in Microsoft Fabric Exercise - Create and use a Dataflow (Gen2) in Microsoft Fabric

Duration 5 Days 30 CPD hours Overview Mining data Manipulating data Visualizing and reporting data Applying basic statistical methods Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle CompTIA Data+ is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making. CompTIA Data+ gives you the confidence to bring data analysis to life. As the importance for data analytics grows, more job roles are required to set context and better communicate vital business intelligence. Collecting, analyzing, and reporting on data can drive priorities and lead business decision-making. 1 - Identifying Basic Concepts of Data Schemas Identify Relational and Non-Relational Databases Understand the Way We Use Tables, Primary Keys, and Normalization 2 - Understanding Different Data Systems Describe Types of Data Processing and Storage Systems Explain How Data Changes 3 - Understanding Types and Characteristics of Data Understand Types of Data Break Down the Field Data Types 4 - Comparing and Contrasting Different Data Structures, Formats, and Markup Languages Differentiate between Structured Data and Unstructured Data Recognize Different File Formats Understand the Different Code Languages Used for Data 5 - Explaining Data Integration and Collection Methods Understand the Processes of Extracting, Transforming, and Loading Data Explain API/Web Scraping and Other Collection Methods Collect and Use Public and Publicly-Available Data Use and Collect Survey Data 6 - Identifying Common Reasons for Cleansing and Profiling Data Learn to Profile Data Address Redundant, Duplicated, and Unnecessary Data Work with Missing Value Address Invalid Data Convert Data to Meet Specifications 7 - Executing Different Data Manipulation Techniques Manipulate Field Data and Create Variables Transpose and Append Data Query Data 8 - Explaining Common Techniques for Data Manipulation and Optimization Use Functions to Manipulate Data Use Common Techniques for Query Optimization 9 - Applying Descriptive Statistical Methods Use Measures of Central Tendency Use Measures of Dispersion Use Frequency and Percentages 10 - Describing Key Analysis Techniques Get Started with Analysis Recognize Types of Analysis 11 - Understanding the Use of Different Statistical Methods Understand the Importance of Statistical Tests Break Down the Hypothesis Test Understand Tests and Methods to Determine Relationships Between Variables 12 - Using the Appropriate Type of Visualization Use Basic Visuals Build Advanced Visuals Build Maps with Geographical Data Use Visuals to Tell a Story 13 - Expressing Business Requirements in a Report Format Consider Audience Needs When Developing a Report Describe Data Source Considerations For Reporting Describe Considerations for Delivering Reports and Dashboards Develop Reports or Dashboards Understand Ways to Sort and Filter Data 14 - Designing Components for Reports and Dashboards Design Elements for Reports and Dashboards Utilize Standard Elements Creating a Narrative and Other Written Elements Understand Deployment Considerations 15 - Understand Deployment Considerations Understand How Updates and Timing Affect Reporting Differentiate Between Types of Reports 16 - Summarizing the Importance of Data Governance Define Data Governance Understand Access Requirements and Policies Understand Security Requirements Understand Entity Relationship Requirements 17 - Applying Quality Control to Data Describe Characteristics, Rules, and Metrics of Data Quality Identify Reasons to Quality Check Data and Methods of Data Validation 18 - Explaining Master Data Management Concepts Explain the Basics of Master Data Management Describe Master Data Management Processes Additional course details: Nexus Humans CompTIA Data Plus (DA0-001) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the CompTIA Data Plus (DA0-001) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Architecture and course review Post course knowledge check Architecture review Course review

Duration 4 Days 24 CPD hours This course is intended for While no prior programming or web development experience is required, target students should have good foundational computer skills. Overview Set up the tools and resources you need to perform Web Development. Create web pages in HTML, constructing valid, well-formed elements, including navigation links, sections, titles, and semantic markup. Enhance HTML content with tables, images, movies, and audio. Apply styles to HTML elements using CSS. Use CSS to format the edges, size, position, and layering of HTML elements. Apply complex style rules using advanced CSS selectors, including pseudo-class selectors, structural selectors, and pseudo-element selectors. Create complex layouts using CSS newspaper style columns, grid layouts, and flexible box layouts. Improve the quality of web content, including adaptability (different displays and devices), searchability, usability, and accessibility. Submit data through URL query strings and web forms for processing by a web application server. Write JavaScript code to make web pages more interactive, perform data processing tasks directly in the browser, and manipulate items in the current web page. Write JavaScript code to iterate through collections of elements in a page to get and set their attributes and add event listener code. Use third-party libraries and frameworks for web front-end development. Modern organizations depend heavily on the web to perform core business operations such as marketing, advertising, and selling products, providing services, and communicating with customers, partner organizations, and employees. Whatever you're creating on the web, HTML, CSS, and JavaScript? likely play an important role. These three languages provide the core toolkit for anyone looking to perform web development work. This course covers the fundamentals of web development using these three languages. Prerequisites This course assumes that students have strong experience working with computers. Previous experience programming in other languages is helpful, but not required for students to benefit from this course. Lesson 1: Setting Up Your Web Development Environment Topic A: Prepare Your Web Platform Topic B: Prepare Your Web Development Tools and Processes Topic C: Monitor the Web Request-Response Cycle Lesson 2: Creating Web Content in HTML Topic A: Create a Basic Web Page Topic B: Provide Navigation Links Between Web Pages Topic C: Improve Web Page Structure and Navigation Lesson 3: Adding Tables and Multimedia Content to a Web Page Topic A: Create a Table Topic B: Embed Images, Movies, and Audio in a Web Page Lesson 4: Applying Styles to Web Content Topic A: Apply Styles to HTML Topic B: Create a Style Sheet Topic C: Use Web Fonts Lesson 5: Controlling Edges, Size, and Position Topic A: Format Element Edges and Corners Topic B: Control an Element's Height and Width Topic C: Control an Element's Position and Layering Topic D: Normalize and Reset Browser CSS Defaults Lesson 6: Applying Complex Style Rules Topic A: Use Advanced Selectors Topic B: Manage User Interface States Topic C: Make Structure Apparent to Users Topic D: Use CSS Pseudo-Element Selectors Lesson 7: Creating Complex Layouts Topic A: Use CSS to Create Newspaper Style Columns Topic B: Use CSS to Create Grid Layouts Topic C: Use CSS to Create Flexible Box Layouts Lesson 8: Improving Web Content Topic A: Adjust the Layout for a Wide Variety of Devices Topic B: Perform Basic Search Engine Optimization Topic C: Test Your Website Lesson 9: Submitting Data to a Web Server for Processing Topic A: Submit Data Through a URL Topic B: Submit Data Through a Web Form Lesson 10: Writing JavaScript Code Topic A: Add JavaScript to a Web Page Topic B: Perform Operations on Data Topic C: Program Repetitive Tasks Topic D: Manipulate DOM Objects Lesson 11: Enumerating and Processing Collections of Elements Topic A: Enumerate Elements Topic B: Attach Events Through Code Lesson 12: Using Third-Party Libraries and Frameworks Topic A: Use a Third-Party JavaScript Library Topic B: Create a Web Page Based on a Third-Party Framework Additional course details: Nexus Humans Web Development with HTML5, CSS, and JavaScript (v1.0) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Web Development with HTML5, CSS, and JavaScript (v1.0) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Overview Skills gained in this training include:The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysisThe fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with HadoopHow Pig, Hive, and Impala improve productivity for typical analysis tasksJoining diverse datasets to gain valuable business insightPerforming real-time, complex queries on datasets Cloudera University?s four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data. Hadoop Fundamentals The Motivation for Hadoop Hadoop Overview Data Storage: HDFS Distributed Data Processing: YARN, MapReduce, and Spark Data Processing and Analysis: Pig, Hive, and Impala Data Integration: Sqoop Other Hadoop Data Tools Exercise Scenarios Explanation Introduction to Pig What Is Pig? Pig?s Features Pig Use Cases Interacting with Pig Basic Data Analysis with Pig Pig Latin Syntax Loading Data Simple Data Types Field Definitions Data Output Viewing the Schema Filtering and Sorting Data Commonly-Used Functions Processing Complex Data with Pig Storage Formats Complex/Nested Data Types Grouping Built-In Functions for Complex Data Iterating Grouped Data Multi-Dataset Operations with Pig Techniques for Combining Data Sets Joining Data Sets in Pig Set Operations Splitting Data Sets Pig Troubleshoot & Optimization Troubleshooting Pig Logging Using Hadoop?s Web UI Data Sampling and Debugging Performance Overview Understanding the Execution Plan Tips for Improving the Performance of Your Pig Jobs Introduction to Hive & Impala What Is Hive? What Is Impala? Schema and Data Storage Comparing Hive to Traditional Databases Hive Use Cases Querying with Hive & Impala Databases and Tables Basic Hive and Impala Query Language Syntax Data Types Differences Between Hive and Impala Query Syntax Using Hue to Execute Queries Using the Impala Shell Data Management Data Storage Creating Databases and Tables Loading Data Altering Databases and Tables Simplifying Queries with Views Storing Query Results Data Storage & Performance Partitioning Tables Choosing a File Format Managing Metadata Controlling Access to Data Relational Data Analysis with Hive & Impala Joining Datasets Common Built-In Functions Aggregation and Windowing Working with Impala How Impala Executes Queries Extending Impala with User-Defined Functions Improving Impala Performance Analyzing Text and Complex Data with Hive Complex Values in Hive Using Regular Expressions in Hive Sentiment Analysis and N-Grams Conclusion Hive Optimization Understanding Query Performance Controlling Job Execution Plan Bucketing Indexing Data Extending Hive SerDes Data Transformation with Custom Scripts User-Defined Functions Parameterized Queries Choosing the Best Tool for the Job Comparing MapReduce, Pig, Hive, Impala, and Relational Databases Which to Choose?

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake architectures. Module 1: Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Module 2: Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Module 3: Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Module 4: Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Module 5: Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Module 6: Architecture and course review Post course knowledge check Architecture review Course review Additional course details: Nexus Humans Building Data Lakes on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Building Data Lakes on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for Data Protection Officers Data Protection Lawyers Records Managers Information Officers Compliance Officers Human Resource Officers Anyone who uses, processes and maintains personal data Overview The General Data Protection Regulation (GDPR) took effect in 2018. Are you in compliance? There's a lot to know, there's a lot at stake and there's a lot of opportunity for data protection professionals with the right training and education. Achieving a CIPP/E credential shows you have the comprehensive GDPR knowledge, perspective and understanding to ensure compliance and data protection success in Europe-and to take advantage of the career opportunity this sweeping legislation represents. The Certified Information Privacy Manager (CIPM) credential provides the practical day-to-day information to lead an organisation's data protection programme. Adding the CIPM to your CIPP/E puts you at the forefront of ensuring you are ready for the GDPR. The General Data Protection Regulation (GDPR) took effect in 2018. Are you in compliance? There?s a lot to know, there?s a lot at stake and there?s a lot of opportunity for data protection professionals with the right training and education. Achieving a CIPP/E credential shows you have the comprehensive GDPR knowledge, perspective and understanding to ensure compliance and data protection success in Europe?and to take advantage of the career opportunity this sweeping legislation represents. The Certified Information Privacy Manager (CIPM) credential provides the practical day-to-day information to lead an organisation?s data protection programme. Adding the CIPM to your CIPP/E puts you at the forefront of ensuring you are ready for the GDPR. Data protection laws Key European data protection laws and regulatory bodies Evolving toward a harmonised legislative framework Personal Data Understanding and differentiating between types of data as defined by the GDPR Personal, anonymous, pseudonymous and special categories Controllers and Processors Roles and relationships of controllers and processors as defined by the GDPR Processing Personal Data Data processing and GDPR processing principles Applying the GDPR Legal grounds for processing personal data Data subject rights Data subject rights Applying rights Controller and processor obligations Information provision obligations Controller obligations for providing information about data processing activities to data subjects Supervisory authorities as set out in the GDPR Cross-border data transfers Options and obligations under the GDPR for transferring data outside the European Economic Area Adequacy decisions Safeguards and derogations Compliance considerations Applying European data protection laws Legal bases and compliance requirements for processing personal data in practice Processing employee data Surveillance Direct marketing Internet technology and communications Security of processing Considerations and duties of controllers and processors for ensuring security of personal data GDPR specifications for providing notification of data breaches Accountability Accountability requirements Data protection management systems Data protection impact assessments Data protection policies Role of the data protection officer Supervision and enforcement Role, powers and procedures of supervisory authorities Composition and tasks of the European Data Protection Board Role of the European Data Protection Supervisor Remedies, liabilities and penalties for noncompliance as set out in the GDPR Additional course details: Nexus Humans Certified Information Privacy Professional (CIPP/E) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Certified Information Privacy Professional (CIPP/E) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

Duration 4.5 Days 27 CPD hours This course is intended for This is an intermediate course intended for IBM i system administrators, data processing managers and other individuals who implement and manage security, backup and recovery, system software and problem determination. This course is not recommended for s Overview Describe and implement the components of IBM i security, such as user profile, group profile, authorization list, adopted authority and object / resource security Develop a security plan for your Power Systems with IBM i Describe the options to implement security auditing Develop a plan to audit security on your Power Systems with IBM i Describe the IBM i availability products and features and choose the option(s) that best fit your company requirements Describe how to backup and recover user, IBM and full system data on your Power Systems with IBM i Develop a backup and recovery plan for your Power Systems with IBM i Describe the system diagnostics and problem determination procedures available on your Power Systems with IBM i Learn how to plan for, implement, and manage the ongoing operations of an IBM i. Class administration and introductions . IBM i overview and concepts . Management central overview . Security concepts and overview . System values . User security . Resource security . Security auditing . Designing security . IBM i availability overview . Disk management . Backup and recovery strategy using Save/Restore . Journal management . Commitment control overview . Backup and recovery planning . Problem determination . Overview of Systems Director Navigator for i . Introduction to BRMS .

Duration 1 Days 6 CPD hours This course is intended for This course is intended for networking and security professionals involved in the day-to-day management of a FortiAnalyzer appliance and FortiGate security information.. Overview Describe key features and concepts of FortiAnalyzer Deploy an appropriate architecture Manage ADOMs on both FortiAnalyzer and the devices that log to it Configure RAID Register supported devices Encrypt log transmission (SSL / IPSec) View & analyze current and historical logs (FortiView) Monitor events Apply disk quotas to log data from devices Backup, restore, and forward log data Use content archiving (summary and full) Understand the different stages of data processing, from receiving logs to compiling reports Understand SQL queries and datasets used by FortiAnalyzer reports Design datasets, charts, and custom reports Generate reports by schedule or on demand. In this 1-day class, you will learn how to use FortiAnalyzer. You will explore setup, registering supported devices and securing communications, managing logs and archives, and configuring both predefined and customized reports. Introduction to FortiAnalyzer Key features Key concepts Different FortiAnalyzer models Configuration & Administration Deployment requirements Configuration tools Configuring network settings Backing up system configuration Configuring administrative users Configuring, enabling, and assigning ADOMs Configuring RAID Device Registration Registered and unregistered devices Device registration methods Modifying options of a registered device Methods available to secure communication Configuring SSL encryption and encryption levels Configuring an IPsec tunnel Logs & Archives Logging basics The FortiView tab Configuring log arrays and event handlers Reports Reports and functionality Relationship between reports, charts, and datasets Effect of ADOMs on report settings SQL SELECT queries and clauses SQL functions and operators FortiAnalyzer-specific functions and macros Building or customizing charts Report features--creating, cloning, configuring