45689 Courses delivered Online

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

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

Introduction Making a great first impression can be a game-changer, and your self-introduction plays a crucial role in this. It’s not just about stating who you are but presenting yourself in a way that captures attention and fosters connections. Whether you're stepping into a job interview or mingling at a party, a well-structured self-introduction can make all the difference. Understanding Your Audience Identifying Your Audience Before you craft your introduction, it's essential to understand who you're speaking to. Are you addressing colleagues in a corporate setting, potential clients, or new acquaintances at a social event? Knowing your audience helps tailor your message to their interests and expectations. Tailoring Your Message to the Audience Different audiences require different approaches. For a professional audience, focus on your career achievements and skills. For a social setting, you might emphasize hobbies or personal interests. Adapting your message ensures relevance and keeps your audience engaged. Crafting Your Introduction Key Elements of a Self-Introduction A solid introduction typically includes: Name and Current Role: Start with who you are and what you do. This sets the context for your audience. Relevant Background Information: Share a bit about your background that adds value to the conversation. Unique Selling Points: Highlight what makes you unique or what you bring to the table. Structuring Your Introduction for Clarity Organize your introduction in a logical flow. Start with your name, move on to your current role, share relevant background information, and end with your unique selling points. This structure ensures your introduction is clear and impactful. The Power of a Strong Opening Creating a Memorable Hook Capture your audience's attention right from the start. Use a compelling hook, such as an intriguing fact about yourself, a brief anecdote, or a relevant quote. A strong opening piques curiosity and draws your audience in. Using Anecdotes or Quotes Incorporating a brief story or a quote can make your introduction more engaging and memorable. Choose anecdotes that reflect your personality or achievements, and quotes that resonate with your message. Building Your Narrative Crafting a Compelling Story People connect with stories. Weave a narrative around your experiences, challenges, and successes. This approach makes your introduction more relatable and interesting. Highlighting Achievements and Experiences Focus on significant achievements and experiences that align with the context of your introduction. This helps establish credibility and shows what you bring to the table. Keeping It Concise Importance of Brevity While it’s tempting to share every detail, brevity is key. Aim for a concise introduction that covers the essentials without overwhelming your audience. A brief, well-crafted introduction is often more effective than a lengthy one. Techniques for Staying on Point To stay on point, practice your introduction and focus on the most relevant information. Avoid tangents and ensure every part of your introduction adds value. Engaging with Your Audience Asking Questions Engage your audience by asking questions or inviting them to share their thoughts. This interaction not only makes your introduction more engaging but also fosters a two-way conversation. Inviting Interaction Encourage dialogue by showing interest in your audience’s responses or reactions. This approach helps build connections and makes your introduction more dynamic. Non-Verbal Communication Body Language Tips Non-verbal cues play a significant role in communication. Maintain good posture, use open gestures, and avoid crossing your arms. Your body language should complement your verbal introduction. The Role of Eye Contact Maintaining eye contact shows confidence and helps build trust with your audience. It demonstrates engagement and sincerity in your introduction. Tailoring Your Introduction for Different Settings Professional Settings In professional settings, focus on your career achievements, skills, and professional interests. Be formal and concise, and ensure your introduction aligns with the context of the meeting or event. Social Gatherings For social gatherings, highlight personal interests, hobbies, or interesting facts about yourself. This helps create a friendly atmosphere and fosters personal connections. Virtual Introductions In virtual settings, ensure your introduction is clear and engaging despite the lack of physical presence. Use visual aids if possible, and maintain a positive and professional tone. Practicing Your Delivery Rehearsing Your Introduction Practice your introduction to build confidence and ensure smooth delivery. Rehearse in front of a mirror or with friends to refine your approach and timing. Adapting to Feedback Be open to feedback and adjust your introduction based on what resonates best with your audience. Continuous improvement helps keep your introduction fresh and effective. Common Mistakes to Avoid Overloading with Information Avoid overwhelming your audience with too much information. Focus on key points and keep your introduction relevant to the context. Being Too Vague or Generic Generic introductions can be forgettable. Be specific and highlight unique aspects of your background or achievements to stand out. Using Visual Aids Enhancing Your Introduction with Slides or Props In some settings, visual aids can enhance your introduction. Use slides or props to highlight key points or add visual interest. Ensure they support your message without overshadowing it. When and How to Use Visual Aids Effectively Only use visual aids when they add value to your introduction. Keep them simple and relevant, and practice using them smoothly in your delivery. The Role of Confidence Building Self-Confidence Confidence can be built through practice and preparation. Know your material, rehearse regularly, and adopt a positive mindset to boost your confidence. Projecting Confidence Through Speech Your tone, pace, and clarity of speech reflect your confidence. Speak clearly, at a steady pace, and with enthusiasm to convey confidence effectively. Feedback and Improvement Seeking Constructive Feedback Request feedback from trusted friends or colleagues to gain insights into how your introduction is received. Use this feedback to make improvements. Continuously Refining Your Introduction Refine your introduction based on feedback and personal experiences. Regular updates ensure your introduction remains relevant and impactful. Conclusion Crafting a powerful self-introduction is both an art and a skill. By understanding your audience, structuring your message clearly, and practicing your delivery, you can make a memorable first impression. Remember to tailor your introduction for different settings, engage with your audience, and continually refine your approach. With these strategies, you'll be well-equipped to introduce yourself effectively in any situation. FAQs 1. What should be included in a professional self-introduction? Include your name, current role, key achievements, and relevant skills. Tailor it to the context of the professional setting. 2. How long should my self-introduction be? Aim for 30-60 seconds. It should be concise yet comprehensive enough to convey your key points. 3. How can I make my introduction more engaging? Use a memorable hook, incorporate personal anecdotes, and engage your audience with questions or interactive elements. 4. What are common mistakes to avoid in a self-introduction? Avoid being too vague, overloading with information, or appearing uninterested. Focus on relevance and clarity. 5. How can I improve my self-introduction over time? Seek feedback, practice regularly, and refine your introduction based on experiences and audience reactions.

Duration 2 Days 12 CPD hours This course is intended for This class is intended for network engineers and network admins that are either using Google Cloud Platform or are planning to do so. The class is also for individuals that want to be exposed to software-defined networking solutions in the cloud. Overview Configure Google VPC networks, subnets, and routers Control administrative access to VPC objects Control network access to endpoints in VPCsInterconnect networks among GCP projects Interconnect networks among GCP VPC networks and on-premises or other-cloud networks Choose among GCP load balancer and proxy options and configure them Use Cloud CDN to reduce latency and save money Optimize network spend using Network TiersConfigure Cloud NAT or Private Google Access to provide instances without public IP addresses access to other services Deploy networks declaratively using Cloud Deployment Manager or Terraform Design networks to meet common customer requirements Configure monitoring and logging to troubleshoot networks problems Learn about the broad variety of networking options on Google Cloud. This course uses lectures, demos, and hands-on labs to help you explore and deploy Google Cloud networking technologies, including Virtual Private Cloud (VPC) networks, subnets, and firewalls; interconnection among networks; load balancing; Cloud DNS; Cloud CDN; and Cloud NAT. You'll also learn about common network design patterns and automated deployment using Cloud Deployment Manager or Terraform. Google Cloud VPC Networking Fundamentals Recall that networks belong to projects. Explain the differences among default, auto, and custom networks. Create networks and subnets. Explain how IPv4 addresses are assigned to Compute Engine instances. Publish domain names using Google Cloud DNS. Create Compute Engine instances with IP aliases. Create Compute Engine instances with multiple virtual network. Controlling Access to VPC Networks Outline how IAM policies affect VPC networks. Control access to network resources using service accounts. Control access to Compute Engine instances with tag-based firewall rules. Sharing Networks across Projects Outline the overall workflow for configuring Shared VPC. Differentiate between the IAM roles that allow network resources to be managed. Configure peering between unrelated VPC Networks. Recall when to use Shared VPC and when to use VPC Network Peering. Load Balancing Recall the various load balancing services. Configure Layer 7 HTTP(S) load balancing. Whitelist and blacklist IP traffic with Cloud Armor. Cache content with Cloud CDN. Explain Layer 4 TCP or SSL proxy load balancing. Explain regional network load balancing. Configure internal load balancing. Recall the choices for enabling IPv6 Internet connectivity for Google Cloud load balancers. Determine which Google Cloud load balancer to use when. Hybrid Connectivity Recall the Google Cloud interconnect and peering services available to connect your infrastructure to Google Cloud. Explain Dedicated Interconnect and Partner Interconnect. Describe the workflow for configuring a Dedicated Interconnect. Build a connection over a VPN with Cloud Router. Determine which Google Cloud interconnect service to use when. Explain Direct Peering and Partner Peering. Determine which Google Cloud peering service to use when. Networking Pricing and Billing Recognize how networking features are charged for. Use Network Service Tiers to optimize spend. Determine which Network Service Tier to use when. Recall that labels can be used to understand networking spend. Network Design and Deployment Explain common network design patterns. Configure Private Google Access to allow access to certain Google Cloud services from VM instances with only internal IP addresses. Configure Cloud NAT to provide your instances without public IP addresses access to the internet. Automate the deployment of networks using Deployment Manager or Terraform. Launch networking solutions using Cloud Marketplace. Network Monitoring and Troubleshooting Configure uptime checks, alerting policies and charts for your network services. Use VPC Flow Logs to log and analyze network traffic behavior.

Duration 1 Days 6 CPD hours This course is intended for This basic course is for those who will be administering Information Server and its product components. Overview List Information Server functional categories and the Information Server products and components that support themList and describe the Information Server architectural tiersAccess Information Server clients, including thin clients using the Information Server Launch Pad, the Information Server Engine clients, and the Information Server Console clients including Information Analyzer and Information Services DirectorCreate and configure Information Server users and groupsManage Information Server active sessionsManage Information Server reportingWork with Information Server command-line tools including tools for session administration, user and group management, and encryptionUse the istool functionality to query, export, and import Information Server Repository assets This course gets those charged with administering Information Server v11.5 and its suite of many products and components started with the basic administrative tasks necessary to support Information Server users and developers. Information Server Technical Overview List the Information Server functional categories List the Information Server products and components that support these functional categories List the Information Server architectural tiers Working with Information Server Clients Use the Information Server Launch Pad to access Information Server thin clients including the Administrative Console, Information Governance Catalog, and Metadata Asset Manager Access Information Server Engine Clients including DataStage, QualityStage, FastTrack, and Information Server Manager Access Information Server Console Clients including Information Analyzer and Information Services Director Authentication and Suite Security Configure Suite users and groups Configure DataStage credentials for Engine users Session Management View a list of active sessions View session properties Disconnect sessions Configure global session properties Managing Reports Create and manage report folders Create a report Run a report View report results Administrative Tools Session Admin tool Directory Command tool Encrypt tool Managing Information Server Repository Assets Use istool to export and import common metadata assets Use istool to query information assets Use istool to export and import security assets Use istool to export and import reporting assets

Duration 1 Days 6 CPD hours This course is intended for This course is intended for individuals who desire to become more skilled at handling difficult customers. Overview Upon successful completion of this course, students will be able to deal with difficult customers in a way that increases productivity and customer service, and decreases unhappy customers. In this course, students will gain a valuable skill set to deal with difficult customers in various situations. 1 - GETTING STARTED Housekeeping Items Pre-Assignment Review Workshop Objectives The Parking Lot Action Plan 2 - THE RIGHT ATTITUDE STARTS WITH YOU Be Grateful Keep Your Body Healthy Focus on Positive Thoughts Invoke Inner Peace Case Study 3 - INTERNAL STRESS MANAGEMENT Irritability Unhappiness with Your Job Feeling Underappreciated Not Well-Rested Case Study 4 - EXTERNAL STRESS MANAGEMENT Office Furniture Not Ergonomically Sound High Noise Volume in the Office Rift with Co-Workers Demanding Supervisor Case Study 5 - TRANSACTIONAL ANALYSIS What is Transactional Analysis? Parent Adult Child Case Study 6 - WHY ARE SOME CUSTOMERS DIFFICULT? They Have Truly Had a Bad Experience and Want to Vent They Have Truly Had a Bad Experience and Want Someone to be Held Accountable They Have Truly Had a Bad Experience and Want Resolution They Are Generally Unhappy Case Study 7 - DEALING WITH THE CUSTOMER OVER THE PHONE Listen to the Customer?s Complaint Build Rapport Do Not Respond with Negative Words or Emotion Offer a Verbal Solution to Customer Case Study 8 - DEALING WITH THE CUSTOMER IN PERSON Listen to the Customer?s Complaint Build Rapport Responding with Positive Words and Body Language Besides Words, What to Look For? Case Study 9 - SENSITIVITY IN DEALING WITH CUSTOMERS Who are Angry Who Are Rude With Different Cultural Values Who Cannot Be Satisfied Case Study 10 - SCENARIOS OF DEALING WITH A DIFFICULT CUSTOMER Angry Customer Rude Customer Culturally Diverse Customer Impossible to Please Customer Case Study 11 - CUSTOMER ONCE YOU HAVE ADDRESSED THEIR COMPLAINT Call the Customer Send the Customer an Email Mail the Customer a Small Token Handwritten or Typed Letter Case Study 12 - WRAPPING UP Words From The Wise Review Of The Parking Lot Lessons Learned Recommended Reading Completion Of Action Plans And Evaluations

Duration 1 Days 6 CPD hours This course is intended for Leaders, Managers, Individuals who lead meetings This course is designed to help leaders run effective virtual meetings as well as managing their team virtually. We will explore communication styles and understanding their team as well as productivity. This course involves a lot of open discussion as well as teaching leaders how to manage the virtual workplace and run productive meetings. Defining the Virtual Workplace What does it look like? Tools available Communication strategies Understanding communication styles Leading different communication styles Building a Virtual Workplace Strategy Goals & agenda Check-ins Communication strategies Virtual Leadership Strategies Making connections & check ins Managing virtual meetings with team members Defining availability & creating schedules Open Discussion & Action Plan

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: Database architects Database administrators Database developers Data analysts and scientists Overview This course is designed to teach you how to: Discuss the core concepts of data warehousing, and the intersection between data warehousing and big data solutions Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3, to contribute to the data warehousing solution Architect the data warehouse Identify performance issues, optimize queries, and tune the database for better performance Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon QuickSight to perform analysis on your data Module 1: Introduction to Data Warehousing Relational databases Data warehousing concepts The intersection of data warehousing and big data Overview of data management in AWS Hands-on lab 1: Introduction to Amazon Redshift Module 2: Introduction to Amazon Redshift Conceptual overview Real-world use cases Hands-on lab 2: Launching an Amazon Redshift cluster Module 3: Launching clusters Building the cluster Connecting to the cluster Controlling access Database security Load data Hands-on lab 3: Optimizing database schemas Module 4: Designing the database schema Schemas and data types Columnar compression Data distribution styles Data sorting methods Module 5: Identifying data sources Data sources overview Amazon S3 Amazon DynamoDB Amazon EMR Amazon Kinesis Data Firehose AWS Lambda Database Loader for Amazon Redshift Hands-on lab 4: Loading real-time data into an Amazon Redshift database Module 6: Loading data Preparing Data Loading data using COPY Data Warehousing on AWS AWS Classroom Training Concurrent write operations Troubleshooting load issues Hands-on lab 5: Loading data with the COPY command Module 7: Writing queries and tuning for performance Amazon Redshift SQL User-Defined Functions (UDFs) Factors that affect query performance The EXPLAIN command and query plans Workload Management (WLM) Hands-on lab 6: Configuring workload management Module 8: Amazon Redshift Spectrum Amazon Redshift Spectrum Configuring data for Amazon Redshift Spectrum Amazon Redshift Spectrum Queries Hands-on lab 7: Using Amazon Redshift Spectrum Module 9: Maintaining clusters Audit logging Performance monitoring Events and notifications Lab 8: Auditing and monitoring clusters Resizing clusters Backing up and restoring clusters Resource tagging and limits and constraints Hands-on lab 9: Backing up, restoring and resizing clusters Module 10: Analyzing and visualizing data Power of visualizations Building dashboards Amazon QuickSight editions and feature

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

Discover how to conquer your assignment deadlines with ease. Packed with valuable tips and strategies, this article explores the importance of effective deadline management for busy students. Learn how to prioritise tasks, plan your schedule wisely, and utilise assignment help services to achieve academic success.