77 Google Cloud courses

Duration 1 Days 6 CPD hours This course is intended for This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists. Overview This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform. This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. Introducing Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products. Compute and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline. Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc. Scaling Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset. Machine Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs. Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Summary Why GCP? Where to go from here Additional Resources Additional course details: Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals 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 Google Cloud Platform Big Data and Machine Learning Fundamentals 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 3 Days 18 CPD hours This course is intended for Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform Overview This course teaches students the following skills: Derive insights from data using the analysis and visualization tools on Google Cloud Platform Interactively query datasets using Google BigQuery Load, clean, and transform data at scale Visualize data using Google Data Studio and other third-party platforms Distinguish between exploratory and explanatory analytics and when to use each approach Explore new datasets and uncover hidden insights quickly and effectively Optimizing data models and queries for price and performance Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL. Introduction to Data on the Google Cloud Platform Highlight Analytics Challenges Faced by Data Analysts Compare Big Data On-Premises vs on the Cloud Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud Navigate Google Cloud Platform Project Basics Lab: Getting started with Google Cloud Platform Big Data Tools Overview Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools Demo: Analyze 10 Billion Records with Google BigQuery Explore 9 Fundamental Google BigQuery Features Compare GCP Tools for Analysts, Data Scientists, and Data Engineers Lab: Exploring Datasets with Google BigQuery Exploring your Data with SQL Compare Common Data Exploration Techniques Learn How to Code High Quality Standard SQL Explore Google BigQuery Public Datasets Visualization Preview: Google Data Studio Lab: Troubleshoot Common SQL Errors Google BigQuery Pricing Walkthrough of a BigQuery Job Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs Optimize Queries for Cost Lab: Calculate Google BigQuery Pricing Cleaning and Transforming your Data Examine the 5 Principles of Dataset Integrity Characterize Dataset Shape and Skew Clean and Transform Data using SQL Clean and Transform Data using a new UI: Introducing Cloud Dataprep Lab: Explore and Shape Data with Cloud Dataprep Storing and Exporting Data Compare Permanent vs Temporary Tables Save and Export Query Results Performance Preview: Query Cache Lab: Creating new Permanent Tables Ingesting New Datasets into Google BigQuery Query from External Data Sources Avoid Data Ingesting Pitfalls Ingest New Data into Permanent Tables Discuss Streaming Inserts Lab: Ingesting and Querying New Datasets Data Visualization Overview of Data Visualization Principles Exploratory vs Explanatory Analysis Approaches Demo: Google Data Studio UI Connect Google Data Studio to Google BigQuery Lab: Exploring a Dataset in Google Data Studio Joining and Merging Datasets Merge Historical Data Tables with UNION Introduce Table Wildcards for Easy Merges Review Data Schemas: Linking Data Across Multiple Tables Walkthrough JOIN Examples and Pitfalls Lab: Join and Union Data from Multiple Tables Advanced Functions and Clauses Review SQL Case Statements Introduce Analytical Window Functions Safeguard Data with One-Way Field Encryption Discuss Effective Sub-query and CTE design Compare SQL and Javascript UDFs Lab: Deriving Insights with Advanced SQL Functions Schema Design and Nested Data Structures Compare Google BigQuery vs Traditional RDBMS Data Architecture Normalization vs Denormalization: Performance Tradeoffs Schema Review: The Good, The Bad, and The Ugly Arrays and Nested Data in Google BigQuery Lab: Querying Nested and Repeated Data More Visualization with Google Data Studio Create Case Statements and Calculated Fields Avoid Performance Pitfalls with Cache considerations Share Dashboards and Discuss Data Access considerations Optimizing for Performance Avoid Google BigQuery Performance Pitfalls Prevent Hotspots in your Data Diagnose Performance Issues with the Query Explanation map Lab: Optimizing and Troubleshooting Query Performance Advanced Insights Introducing Cloud Datalab Cloud Datalab Notebooks and Cells Benefits of Cloud Datalab Data Access Compare IAM and BigQuery Dataset Roles Avoid Access Pitfalls Review Members, Roles, Organizations, Account Administration, and Service Accounts

Duration 1 Days 6 CPD hours This course is intended for Software developers, system administrators, and IT professionals who are focused on Microsoft Windows Overview Configuring Microsoft Windows and Microsoft SQL Server in Google Compute Engine. Deploying ASP.NET MVC applications to Google Compute Engine. Deploying .NET Core applications to Google Compute Engine, Google Compute Engine, and Google Container Engine Learn how to create Windows virtual machines on Google Cloud so that you can deploy and run Microsoft Windows applications. In this course, you'll learn how to run SQL Server in Compute Engine, how to deploy instances across Google Cloud zones, and how to get more out of ASP.NET on Compute Engine, Google Container Engine, and App Engine. Introduction to Google Cloud Platform Scope and structure of GCP. Options for Windows deployment on GCP. GCP interfaces. Windows Workloads on Google Compute Engine Google Compute Engine virtual machine options. Integrating Active Directory with Google Compute Engine virtual machines. Options for running SQL Server in Google Compute Engine. Configuring SQL Server for high availability. Developing ASP.NET MVC applications Model-view-controller structure. Using Microsoft Visual Studio?s Web Project template to develop in ASP.NET. Deploying applications to Microsoft Internet Information Server (IIS) in GCE. Configuring Resilient Workloads Deploying instances across GCP zones. Using instance groups to create pools of virtual machines. Load balancing Windows applications. Delivering Next-Generation ASP.NET Core on GCP Understanding .NET Core and EF Core. Options for deploying ASP.NET Core applications on Google Cloud Platform. Deploying ASP.NET Core applications on Google Compute Engine. Deploying ASP.NET Core applications on Google Container Engine. Deploying ASP.NET Core applications on Google App Engine. Additional course details: Nexus Humans Develop and Deploy Windows Applications on Google Cloud Platform 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 Develop and Deploy Windows Applications on Google Cloud Platform 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 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.

Register on the Data Engineering with Google BigQuery & Google Cloud today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get an e-certificate as proof of your course completion. The Data Engineering with Google BigQuery & Google Cloud is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Data Engineering with Google BigQuery & Google Cloud Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Data Engineering with Google BigQuery & Google Cloud, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content Unit 01: Introduction Module 01: Welcome to This Course 00:01:00 Module 02: How to Get Maximum Value from This Course 00:06:00 Module 03: Course Structure & Coverage 00:02:00 Module 04: Technology in This Course 00:02:00 Unit 02: Introducing Data Warehouse & BigQuery Module 01: Data Warehouse 00:07:00 Module 02: Start With BigQuery 00:05:00 Module 03: BigQuery Web User Interface 00:04:00 Unit 03: First Exploration Module 01: First Data 00:04:00 Module 02: Basic Exploration 00:16:00 Module 03: Functions 00:14:00 Module 04: Common Data Types 00:07:00 Module 05: Different Query 00:01:00 Module 06: Exploring Common Data Types 00:25:00 Module 07: Converting Data Types 00:07:00 Unit 04: Data Flow Basic Module 01: Data Quality 00:06:00 Module 02: Clean & Transform 00:13:00 Module 03: Store Data 00:06:00 Module 04: Upgrading From Sandbox Account 00:01:00 Module 05: Clean & Transform With Dataprep 00:25:00 Module 06: Scheduled Query 00:04:00 Module 07: Analyze Data 00:05:00 Module 08: Data Visualization 00:06:00 Unit 08: Intermediate Query Module 01: Essential BigQuery 00:15:00 Module 02: Load Data into BigQuery (Part 1) - The Basic 00:36:00 Module 03: Tip: Mock Data 00:01:00 Module 04: Load Data into BigQuery (Part 2) - Handling Errors 00:22:00 Module 05: Load Data into BigQuery (Part 3) - Efficient Load 00:14:00 Module 06: Load Data into BigQuery (Part 4) - From Your Data to BigQuery 00:23:00 Module 07: Load Data into BigQuery (Part 5) - In Microservice Architecture 00:20:00 Module 08: Tip: Message Broker Overview 00:08:00 Module 09: Load Data into BigQuery (Part 6) - Recurring Load 00:12:00 Unit 05: Diving into BigQuery Module 01: BigQuery View 00:06:00 Unit 06: Virtual Data using View Module 01: What We Will Learn 00:01:00 Module 02: Google Sheets & BigQuery 00:10:00 Module 03: Google Data Studio 00:13:00 Unit 07: Data Visualization Module 01: Using Join - Theory 00:04:00 Module 02: Using Join - Hands On 00:16:00 Module 03: Union & Intersect 00:06:00 Module 04: Basic Statistical Functions 00:05:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Course Overview Aprende a usar uno de los sistemas de gestión de contenidomás confiables en los servicios de computación en la nube más popular con este curso sobre cómo alojar en Host en Google Cloud para WordPress (Spanish Course). WordPress es la mejor manera de alojar y construir sitios web personalizables para adaptarse a cualquier negocio, blog, cartera o tienda en línea. Google Cloud es el medio perfecto para esta aplicación, y con esta capacitación, se volverá hábil para instalarla y usarla en todo su potencial para sus requisitos en línea. Este curso de Google Cloud proporciona quince módulos educativos que responderán a todas las preguntas técnicas que tenga sobre cómo activar y activar WordPress en línea. Se le mostrarán detalles sobre el uso del dominio, WordPress VM (Máquina virtual), sistemas de nombres, protocolos de seguridad y mucho más. Todo para el desarrollador de sitios web en ciernes se ofrece en esta capacitación de expertos, y podrá alojar su sitio web personal en Google Cloud, ya sea que sea un desarrollador experimentado o no. Este Host en Google Cloud para WordPress (Spanish Course) más vendido ha sido desarrollado por profesionales de la industria y ya lo han completado cientos de estudiantes satisfechos. Este Host en Google Cloud para WordPress (Spanish Course) en profundidad es adecuado para cualquier persona que quiera desarrollar su conjunto de habilidades profesionales y mejorar su conocimiento experto. El Host en Google Cloud para WordPress (Spanish Course) está acreditado por CPD, por lo que puede estar seguro de que está completando un curso de capacitación de calidad que impulsará su CV y ​​mejorará su potencial profesional. El Host en Google Cloud para WordPress (Spanish Course) se compone de varios módulos llenos de información que desglosan cada tema en pequeños fragmentos para garantizar que comprenda y retenga todo lo que aprende. Después de completar con éxito el Host en Google Cloud para WordPress (Spanish Course), se le otorgará un certificado de finalización como prueba de sus nuevas habilidades. Si está buscando seguir una nueva carrera y desea desarrollar sus habilidades profesionales para sobresalir en el campo elegido, el certificado de finalización del Host en Google Cloud para WordPress (Spanish Course) lo ayudará a destacarse entre la multitud. También puede validar su certificación en nuestro sitio web. Sabemos que está ocupado y que el tiempo es valioso, por lo que hemos diseñado el Host en Google Cloud para WordPress (Spanish Course) para que lo complete a su propio ritmo, ya sea a tiempo parcial o completo. Obtenga acceso completo al curso al registrarse y acceda a los materiales del curso desde cualquier parte del mundo, en cualquier momento, desde cualquier dispositivo habilitado para Internet. Nuestros tutores experimentados están aquí para ayudarlo durante todo el proceso de aprendizaje y responder a cualquier consulta que pueda tener por correo electrónico.

Learn to design, plan, and scale cloud implementations with Google Cloud Platform's BigQuery. This course will walk you through the fundamentals of applied machine learning and BigQuery ML along with its history, architecture, and use cases.

  The comprehensive Diploma in Data Engineering with Google BigQuery & Google Cloud has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Diploma in Data Engineering with Google BigQuery & Google Cloud today, and learn from the very best the industry has to offer! This best selling Diploma in Data Engineering with Google BigQuery & Google Cloud has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Diploma in Data Engineering with Google BigQuery & Google Cloud is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Diploma in Data Engineering with Google BigQuery & Google Cloud is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Diploma in Data Engineering with Google BigQuery & Google Cloud is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Diploma in Data Engineering with Google BigQuery & Google Cloud, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Diploma in Data Engineering with Google BigQuery & Google Cloud will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Diploma in Data Engineering with Google BigQuery & Google Cloud to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

Duration 1 Days 6 CPD hours This course is intended for Business decision-makers: directors (managers of managers), managers of individual contributors (ICs) or ICs working in non-IT functions/divisions (such as finance, marketing, sales, HR, product design) interested in understanding the applications of Google?s cloud technology for business improvement opportunities and transformational project(s). Through this interactive training, you?ll learn about core cloud business drivers?specifically Google?s cloud?and gain the knowledge/skills to determine if business transformation is right for you and your team, and build short and long-term projects using the ?superpowers? of cloud accordingly. You?ll also find several templates, guides, and resource links through the supplementary student workbook to help you build a custom briefing document to share with your leadership, technical teams or partners. Why cloud technology is revolutionizing business This module introduces cloud technology as a paradigm shift and explains how it?s irrevocably transforming business globally. It defines the fundamental building blocks of cloud technology?compute power and data?and what they mean for you and your business. And finally, it reveals how these building blocks help to create five superpowers using concrete use cases of their transformative abilities for business, education, and government sectors. Foster an innovation culture Cloud is not just about a technological transformation; it's a business and cultural transformation, too. This module explores how the superpowers of the cloud are brought to life through people. It looks specifically at how to organically create and scale innovation through culture and business practices. It offers key principles, drawing examples from Google?s success and real world scenarios, that you can apply in your day-to-day operations. Define the ideal business transformation challenge Previous modules describe the journey with cloud technology to include business and cultural changes as well. This module sets the groundwork to build a transformational solution using cloud technology for your role or your business. This process starts with distinguishing between scaled improvements and transformations. Next, the module demonstrates how to write an ideal challenge question and use insights generated from a data ecosystem to address the challenge. Finally, it introduces a framework that you can use to assess and refine your challenge ideas, preparing you to build a business case in a later module. Build trust with availability, security, and compliance This module addresses common concerns about data privacy and security when migrating to the cloud. It defines key terms - privacy, security, compliance, and reliability - and reveals today?s top cybersecurity challenges and threats. It discusses how data security and compliance can be maintained when data is in the cloud, as illustrated by the Shared Responsibility Model. And finally, it uses a concrete example to explain how learners can build a high-level security program in their own organization. Build a business case for your transformation challenge This module explains how to identify the most transformative solution for your business challenge. It then breaks down steps to achieve the transformational solution through creating smaller projects and plotting them onto a transformation roadmap. It describes how to use the data ecosystem you mapped in a previous module to support your overall project. Finally, it explains step by step how to build a business case and gives tips to help you pitch your project idea to gain buy-in from leadership, teams, and technical partners. Additional course details: Nexus Humans Business Transformation with Google Cloud 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 Business Transformation with Google Cloud 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.