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Duration 1 Days 6 CPD hours This course is intended for This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines. Completed either AWS Technical Essentials or Architecting on AWS Completed Building Data Lakes on AWS Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a data warehouse analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Using Amazon Redshift in the Data Analytics Pipeline Why Amazon Redshift for data warehousing? Overview of Amazon Redshift Module 2: Introduction to Amazon Redshift Amazon Redshift architecture Interactive Demo 1: Touring the Amazon Redshift console Amazon Redshift features Practice Lab 1: Load and query data in an Amazon Redshift cluster Module 3: Ingestion and Storage Ingestion Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API Data distribution and storage Interactive Demo 3: Analyzing semi-structured data using the SUPER data type Querying data in Amazon Redshift Practice Lab 2: Data analytics using Amazon Redshift Spectrum Module 4: Processing and Optimizing Data Data transformation Advanced querying Practice Lab 3: Data transformation and querying in Amazon Redshift Resource management Interactive Demo 4: Applying mixed workload management on Amazon Redshift Automation and optimization Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster Module 5: Security and Monitoring of Amazon Redshift Clusters Securing the Amazon Redshift cluster Monitoring and troubleshooting Amazon Redshift clusters Module 6: Designing Data Warehouse Analytics Solutions Data warehouse use case review Activity: Designing a data warehouse analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 3 Days 18 CPD hours This course is intended for This course is intended for network engineers, support personnel, reseller support, and others responsible for implementing Juniper Networks ScreenOS firewall products. Overview After successfully completing this course, you should be able to:Explain the Juniper Networks security architecture.Configure administrative access and options.Back up and restore configuration and ScreenOS files.Configure a ScreenOS device in transparent, route, Network Address Translation (NAT), and IP version 6 (IPv6) modes.Discuss the applications of multiple virtual routers.Configure the Juniper Networks firewall to permit and deny traffic based on user defined policies.Configure advanced policy options.Identify and configure network designs for various types of network address translation.Configure policy-based and route-based VPN tunnels. This course is the first in the ScreenOS curriculum. It is a course that focuses on configuration of the ScreenOS firewall/virtual private network (VPN) products in a variety of situations, including basic administrative access, routing, firewall policies and policy options, address translation, and VPN implementations. The course combines both lecture and labs, with significant time allocated for hands-on experience. Students completing this course should be confident in their ability to configure Juniper Networks firewall/VPN products in a wide range of installations. Chapter 1: Course IntroductionChapter 2: ScreenOS Concepts, Terminology, and PlatformsChapter 3: Initial Connectivity Lab 1: Initial Configuration Chapter 4: Device Management Lab 2: Device Administration Chapter 5: Layer 3 Operations Lab 3: Layer 3 Operations Chapter 6: Basic Policy Configuration Lab 4: Basic Policy Configuration Chapter 7: Policy Options Lab 5: Policy Options Chapter 8: Address Translation Lab 6: Address Translation Chapter 9: VPN ConceptsChapter 10: Policy-Based VPNs Lab 7: Policy-Based VPNs Chapter 11: Route-Based VPNs Lab 8: Route-Based VPNs Chapter 12: IPv6 Lab 9: IPv6 Appendix A: Additional FeaturesAppendix B: Transparent Mode Lab 10: Transparent Mode (Optional) Additional course details: Nexus Humans Configuring Juniper Networks Firewall/IPSec VPN Products 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 Configuring Juniper Networks Firewall/IPSec VPN Products 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 Linux system administrators, virtualization administrators, and hybrid infrastructure engineers interested in deploying large-scale virtualization solutions and managing virtual servers in their datacenters, based on the Red Hat Virtualization open virtualization management platform. Overview As a result of completing this offering, you should be able to create and deploy Red Hat Virtualization and virtual servers. Using a single, full-service management interface, Red Hat Virtualization Manager, you will be able to configure, manage, and migrate systems within the virtualization environment. In this course you will develop the skills needed to deploy, administer, and operate virtual machines in your organization using Red Hat© Virtualization. Through numerous hands-on exercises, you will demonstrate the ability to deploy and configure the Red Hat Virtualization infrastructure and use it to provision and manage virtual machines. This offering also prepares you for the Red Hat Certified Specialist in Virtualization exam.This course is based on Red Hat Enterprise Virtualization 4.3 and Red Hat Enterprise Linux© 7.6 and 8, as well as Red Hat Hyperconverged Infrastructure for Virtualization 1.6.This course covers the same material as RH318, but includes the Red Hat Certified Specialist in Virtualization exam (EX318). Red Hat Virtualization overview Explain the purpose and architecture of Red Hat Virtualization. Install and configure Red Hat Virtualization Install a minimal Red Hat Virtualization environment and use it to create a virtual machine. Create and manage datacenters and clusters Organize hypervisors into groups using datacenters and clusters. Manage user accounts and roles Configure user accounts using a central directory service, then use roles to assign access to resources based on job responsibilities. Adding physical hosts Add additional Red Hat Virtualization hosts automatically, and move and remove hosts from datacenters as needed. Scale Red Hat Virtualization infrastructure Add Red Hat Virtualization hosts automatically, configure Red Hat Enterprise Linux hosts when appropriate, and move and remove hosts from data centers as needed. Manage Red Hat Virtualization networks Separate network traffic into multiple networks on one or more interfaces to improve the performance and security of Red Hat Virtualization. Manage Red Hat Virtualization storage Create and manage data and ISO storage domains. Deploy and manage virtual machines Operate virtual machines in the Red Hat Virtualization environment. Migrate virtual machines Migrate and control automatic migration of virtual machines. Manage virtual machine images Manage virtual machine snapshots and disk images. Automating virtual machine deployment Automate deployment of virtual machines by using templates and cloud-init. Back up and upgrade Red Hat Virtualization Back up, restore, and upgrade the software in a Red Hat Virtualization environment. Explore high-availability practices Explain procedures to improve the resilience and reliability of Red Hat Virtualization by removing single points of failure and implementing high-availability features. Perform comprehensive review Demonstrate skills learned in this course by installing and configuring Red Hat Virtualization; using the platform to create and manage virtual machines; and backing up and updating components of Red Hat Virtualization.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Solutions architects, security DevOps, and security engineers Overview In this course, you will learn to: Establish a landing zone with AWS Control Tower Configure AWS Organizations to create a multi-account environment Implement identity management using AWS Single Sign-On users and groups Federate access using AWS SSO Enforce policies using prepackaged guardrails Centralize logging using AWS CloudTrail and AWS Config Enable cross-account security audits using AWS Identity and Access Management (IAM) Define workflows for provisioning accounts using AWS Service Catalog and AWS Security Hub Security is foundational to AWS. Governance at scale is a new concept for automating cloud governance that can help companies retire manual processes in account management, budget enforcement, and security and compliance. By automating common challenges, companies can scale without inhibiting agility, speed, or innovation. In addition, they can provide decision makers with the visibility, control, and governance necessary to protect sensitive data and systems.In this course, you will learn how to facilitate developer speed and agility, and incorporate preventive and detective controls. By the end of this course, you will be able to apply governance best practices. Course Introduction Instructor introduction Learning objectives Course structure and objectives Course logistics and agenda Module 1: Governance at Scale Governance at scale focal points Business and Technical Challenges Module 2: Governance Automation Multi-account strategies, guidance, and architecture Environments for agility and governance at scale Governance with AWS Control Tower Use cases for governance at scale Module 3: Preventive Controls Enterprise environment challenges for developers AWS Service Catalog Resource creation Workflows for provisioning accounts Preventive cost and security governance Self-service with existing IT service management (ITSM) tools Module 4: Detective Controls Operations aspect of governance at scale Resource monitoring Configuration rules for auditing Operational insights Remediation Clean up accounts Module 5: Resources Explore additional resources for security governance at scale Additional course details: Nexus Humans AWS Security Governance at Scale 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 AWS Security Governance at Scale 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 System administrators and security operations personnel, including analysts and managers Overview By the end of the course, you should be able to meet the following objectives: Describe the components and capabilities of the Carbon Black EDR server Identify the architecture and data flows for Carbon Black EDR communication Identify the architecture for a cluster configuration and Carbon Black EDR cluster communication Describe the Carbon Black EDR server data types and data locations Use the API to interact with the Carbon Black EDR server without using the UI Create custom threat feeds for use in the Carbon Black EDR server Perform the integration with a syslog server Use different server-side scripts for troubleshooting Troubleshoot sensor-side configurations and communication This course teaches you how to use the advanced features of the VMware Carbon Black© EDR? product. This usage includes gaining access to the Linux server for management and troubleshooting in addition to configuring integrations and using the API. This course provides an in-depth, technical understanding of the Carbon Black EDR product through comprehensive coursework and hands-on scenario-based labs. This class focuses exclusively on advanced technical topics related to the technical back-end configuration and maintenance Course Introduction Introductions and course logistics Course objectives Architecture Data flows and channels Sizing considerations Communication channels and ports Server Datastores SOLR database Storage configurations and data aging Partition states Postgres Modulestore EDR API CBAPI overview Viewing API calls in the browser Utilizing the API to access data Threat Intelligence Feeds Feed structure Report indicator types Custom threat feed creation and addition Syslog Integration SIEM support Configuration Troubleshooting Server-side scripts Server logs Sensor operations Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware Carbon Black EDR Advanced Administrator 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 VMware Carbon Black EDR Advanced Administrator 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 Ideal candidates include network professionals who are looking to build their foundational knowledge of the ClearPass product portfolio. Overview After you successfully complete this course, expect to be able to: Ability to setup ClearPass as a AAA server Demonstrate Configuration Guest, OnGurad, Onboard and Profiling features Integrate with External AD Server Understand Monitoring and Reporting Demonstrate Scaling and deployment of best practices Configure AAA services for both wired and wireless networks Demonstrate the configuration of Aruba Downloadable User Roles. Demonstrate the configuration of Dynamic Segmentation with Aruba switches. This course prepares participants with foundational skills in Network Access Control using the ClearPass product portfolio. This 5-day classroom session includes both instructional modules and labs to teach participants about the major features of the ClearPass portfolio. Participants will learn how to setup ClearPass as an AAA server, and configure the Policy Manager, Guest, OnGuard and Onboard feature sets. In addition, this course covers integration with external Active Directory servers, Monitoring and Reporting, as well as deployment best practices. The student will gain insight into configuring authentication with ClearPass on both wired and wireless networks. Intro to ClearPass BYOD High Level Overview Posture and Profiling Guest and Onboard ClearPass for AAA Policy Service Rules Authentication Authorization and Roles Enforcement Policy and Profiles Authentication and Security Concepts Authentication Types Servers Radius COA Active Directory Certificates Intro to NAD NAD Devices Adding NAD to ClearPass Network Device Groups Network Device Attributes Aruba Controller as NAD Aruba Switch Aruba Instant Monitoring and Troubleshooting Monitoring Troubleshooting Logging Policy Simulation ClearPass Insight Insight Dashboard Insight Reports Insight Alerts Insight Search Insight Administration Insight Replication Active Directory Adding AD as Auth Source Joining AD domain Using AD services External Authentication Multiple AD domains LDAP Static Host Lists SQL Database External Radius Server Guest Guest Account creation Web Login pages Guest Service configuration Self-registration pages Configuring NADS for Guest Guest Manager Deep Dive Web Login Deep Dive Sponsor Approval MAC Caching Onboard Intro to Onboard Basic Onboard Setup Onboard Deepdive Single SSID Onboarding Dual SSID Onboarding Profiling Intro to Profiling Endpoint Analysis Deep Dive Posture Intro to Posture Posture Deployment Options OnGuard Agent Health Collection OnGuard workflow 802.1x with Posture using Persistent/dissolvable agent OnGuard web Login Monitoring and Updates Operation and Admin Users Operations Admin Users Clustering and Redundancy Clustering Redundancy LAB Licensing ClearPass Licensing Base License Applications ClearPass Exchange Intro Examples General HTTP Palo Alto Firewall Configuration Case Study Objectives Discussion Advanced Labs Overview Wired Port Authentication 802.1X for access layer switch ports Profiling on Wired Network Configuration of Dynamic Segmentation Aruba Downloadable User Roles Downloadable User Role Enforcement in ClearPass Aruba Controller/Gateway configuration Aruba Switch configuration Troubleshooting

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 2 Days 12 CPD hours This course is intended for This course is designed for security experts and Check Point resellers who desire to obtain the necessary knowledge required to perform more advanced troubleshooting skills while managing their security environments. Overview Understand how to use Check Point diagnostic tools to determine the status of a network. Understand how to use network packet analyzers and packet capturing tools to evaluate network traffic.Become familiar with more advanced Linux system commands. Obtain a deeper knowledge of the Security Management architecture. Understand how the Management database is structured and how objects are represented in the database. Understand key Security Management Server processes and their debugs. Understand how GuiDBedit operates. Understand how the kernel handles traffic and how to troubleshoot issues with chain modules. Understand how to use the two main procedures for debugging the Firewall kernel and how they differ. Recognize User mode processes and how to interpret their debugs. Discuss how to enable and use core dumps. Understand the processes and components used for policy installs and processing packets in Access Control policies. Understand how to troubleshoot and debug issues that may occur with App Control and URLF. Understand how to debug HTTPS Inspection-related issues. Understand how to troubleshoot and debug Content Awareness issues. Understand how IPS works and how to manage performance issues. Understand how to troubleshoot Anti-Bot and Antivirus. Recognize how to troubleshoot and debug Site-to-Site VPN related issues. Understand how to troubleshoot and debug Remote Access VPNs. Understand how troubleshoot Mobile Access VPN issues. Recognize how to use SecureXL features and commands to enable and disable accelerated traffic. Understand how the server hardware and operating system affects the performance of Security Gateways. Understand how to evaluate hardware configurations for optimal performance. Provide advanced troubleshooting skills to investigate and resolve more complex issues that may occur while managing your Check Point Security environment. Course Outline Advanced Troubleshooting Management Database and Processes Advanced Kernel Debugging User Mode Troubleshooting Advanced Access Control Understanding Threat Prevention Advanced VPN Troubleshooting Acceleration and Performance Tuning Additional course details: Nexus Humans CCTE Check Point Troubleshooting Expert 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 CCTE Check Point Troubleshooting Expert 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 course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a batch data analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures