745 SQL courses in Coventry delivered Online

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24 Hour Flash Deal **25-in-1 Cloud Solutions Architect Diploma Mega Bundle** Cloud Solutions Architect Diploma Enrolment Gifts **FREE PDF Certificate**FREE PDF Transcript ** FREE Exam** FREE Student ID ** Lifetime Access **FREE Enrolment Letter ** Take the initial steps toward a successful long-term career by studying the Cloud Solutions Architect Diploma package online with Studyhub through our online learning platform. The Cloud Solutions Architect Diploma bundle can help you improve your CV, wow potential employers, and differentiate yourself from the mass. This Cloud Solutions Architect Diploma course provides complete 360-degree training on Cloud Solutions Architect Diploma. You'll get not one, not two, not three, but twenty-five Cloud Solutions Architect Diploma courses included in this course. 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This Cloud Solutions Architect Diploma training is suitable for - Students Recent graduates Job Seekers Individuals who are already employed in the relevant sectors and wish to enhance their knowledge and expertise in Cloud Solutions Architect Diploma Certified Cloud Professional (CCP) Requirements To participate in this Cloud Solutions Architect Diploma course, all you need is - A smart device A secure internet connection And a keen interest in Cloud Solutions Architect Diploma Career path You will be able to kickstart your Cloud Solutions Architect Diploma career because this course includes various courses as a bonus. This Cloud Solutions Architect Diploma is an excellent opportunity for you to learn multiple skills from the convenience of your own home and explore Cloud Solutions Architect Diploma career opportunities. Certificates CPD Accredited Certificate Digital certificate - Included CPD Accredited e-Certificate - Free CPD Accredited Hardcopy Certificate - Free Enrolment Letter - Free Student ID Card - Free

Learning AWS VPC would be your stepping stone into an AWS career. A good understanding of VPC and its components is a MUST going into any AWS certification. Get started with Amazon VPC and prepare for certification with the help of this comprehensive course. There are no prerequisite requirements for this course.

This is an intermediate to advanced ASP.NET Core (.NET 6) course that will take you from the basics to the advanced mode. This course is for anyone who has a basic understanding of ASP.NET Core and wants to learn how to architect and build real-world ASP.NET Core apps.

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.

This video course takes a step-by-step and systematic approach to make you familiar with the bash shell scripting. You'll learn how to use the Linux command-line and commands to write and execute shell scripts for automating repetitive computing tasks.

Course Overview This comprehensive course offers a deep dive into three essential technologies for data science: Python, JavaScript, and Microsoft SQL. Learners will gain foundational knowledge and practical skills in each of these key areas, which are crucial for handling data, creating interactive websites, and working with databases. By the end of the course, students will be proficient in writing Python code for data analysis, creating dynamic web content with JavaScript, and managing data with Microsoft SQL. The course is designed to equip learners with the technical skills needed to succeed in data science, making it a valuable investment for anyone looking to excel in this growing field. Course Description In this course, learners will explore the core principles of Python, JavaScript, and Microsoft SQL, all tailored to the needs of data science professionals. The curriculum covers Python’s data structures, functions, and libraries essential for data analysis, while JavaScript introduces students to web development skills, including client-side validation and data visualisation. The Microsoft SQL section focuses on data management, including filtering, joining, and structuring queries. Learners will develop a solid understanding of these technologies, which will enable them to manipulate data, automate processes, and design interactive applications. The course also includes real-world applications, ensuring learners are well-prepared for future opportunities in data science and web development. Course Modules: Module 01: JavaScript Getting Started Module 02: JavaScript Fundamentals Module 03: JavaScript Strings Module 04: JavaScript Operators Module 05: JavaScript Conditional Statements Module 06: JavaScript Control Flow Statements Module 07: JavaScript Functions Module 08: Data Visualization (Google Charts) Module 09: JavaScript Error Handling Module 10: JavaScript Client-Side Validations Module 11: Python Introduction Module 12: Python Basic Module 13: Python Strings Module 14: Python Operators Module 15: Python Data Structures Module 16: Python Conditional Statements Module 17: Python Control Flow Statements Module 18: Python Core Games Module 19: Python Functions Module 20: Python Args, KW Args for Data Science Module 21: Python Project Module 22: Publish Your Website for Live Module 23: MS SQL Statements Module 24: MS SQL Filtering Data Module 25: MS SQL Functions Module 26: MS SQL Joins Module 27: MS SQL Advanced Commands Module 28: MS SQL Structure and Keys Module 29: MS SQL Queries Module 30: MS SQL Structure Queries Module 31: MS SQL Constraints Module 32: MS SQL Backup and Restore (See full curriculum) Who is this course for? Individuals seeking to enhance their skills in data science. Professionals aiming to expand their knowledge in programming and database management. Beginners with an interest in Python, JavaScript, and SQL. Anyone looking to enter the field of data science or web development. Career Path Data Scientist Web Developer Database Administrator Data Analyst Front-End Developer Full Stack Developer Data Engineer

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Duration 1 Days 6 CPD hours This course is intended for Individuals responsible for articulating the technical benefits of AWS services Individuals interested in learning how to get started with using AWS SysOps Administrators, Solution Architects and Developers interested in using AWS services Overview Recognize terminology and concepts as they relate to the AWS platform and navigate the AWS Management Console. Understand the foundational services, including Amazon Elastic Compute Cloud (EC2), Amazon Virtual Private Cloud (VPC), Amazon Simple Storage Service (S3), and Amazon Elastic Block Store (EBS). Understand the security measures AWS provides and key concepts of AWS Identity and Access Management (IAM). Understand AWS database services, including Amazon DynamoDB and Amazon Relational Database Service (RDS). Understand AWS management tools, including Auto Scaling, Amazon CloudWatch, Elastic Load Balancing (ELB), and AWS Trusted Advisor. AWS Technical Essentials introduces you to AWS services, and common solutions. It provides you with fundamental knowledge to become more proficient in identifying AWS services. It helps you make informed decisions about IT solutions based on your business requirements and get started working on AWS. Prerequisites Working knowledge of distributed systems Familiarity with general networking concepts Working knowledge of multi-tier architectures Familiarity with cloud computing concepts 1 - AWS introduction and history AWS Global Infrastructure Demonstration: AWS Management Console 2 - AWS Storage Identify key AWS storage options Describe Amazon EBS Create an Amazon S3 bucket and manage associated objects 3 - Compute, network and storage services Amazon Elastic Compute Cloud (EC2) Amazon Virtual Private Cloud (VPC) Amazon Simple Storage Service (S3) Amazon Elastic Block Store (EBS) Demonstration: Amazon Simple Storage Service (S3) Hands-on lab: Build your VPC and launch a web server 4 - AWS Security, AWS Identity and Access Management (IAM) AWS Identity and Access Management (IAM) Demonstration: AWS Identity and Access Management (IAM) 5 - Compute Services & Networking Identify the different AWS compute and networking options Describe an Amazon Virtual Private Cloud (VPC) Create an Amazon EC2 instance Use Amazon EBS with Amazon EC2 6 - Managed Services & Database Describe Amazon DynamoDB Understand key aspects of Amazon RDS Launch an Amazon RDS instance 7 - Databases (RDS, DynamoDB) SQL and NoSQL databases Data storage considerations Hands-on lab: Build your database server and connect to it 8 - AWS elasticity and management tools Auto scaling Load balancing Cloud Watch Trusted Advisor Hands-on lab: Scale and load balance your architecture 9 - Deployment and Management Identify what is CloudFormation Describe Amazon CloudWatch metrics and alarms Describe Amazon Identity and Access Management (IAM)