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Description Nowadays, SQL Developers are in-demand and becoming a SQL developer can be a rewarding and fulfilling profession. This SQL Masterclass: SQL for Data Analytic is intended for absolute beginners that concentrate on giving the appropriate knowledge of Microsoft SQL Server and help to acquire the skills and abilities to become an SQL Server developer as well as offer many job opportunities in the competitive job market. In this course, you will learn how to create databases, tables, design databases and tables. You will also learn to create flat file database, confidently use SSMS Tool as well as write basic T-SQL Queries. In addition to that, learn to create constraints and views in the most dynamic way possible. Enrol right now and start exploring SQL, the most popular relational database management system. Entry Requirement: This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Assessment: At the end of the course, you will be required to sit an online multiple-choice test. Your test will be assessed automatically and immediately so that you will instantly know whether you have been successful. Before sitting for your final exam, you will have the opportunity to test your proficiency with a mock exam. Certification: After completing and passing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognized accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. SQL for Data Analytic - Beginner Level Module: 01 Introduction FREE 00:16:00 Tools for Creating Views 00:11:00 Examples 00:21:00 Updating Data Using a View 00:13:00 Columns and Indexed Views 00:18:00 Module: 02 Introduction Stored Procedures 00:18:00 Creating Stored Procedures 00:19:00 Returning Data 00:14:00 Module: 03 Testing and Debugging 00:17:00 Execute with Result Sets 00:11:00 Creating Triggers 00:26:00 Module: 04 Overview and Scalar Functions 00:20:00 Inline Table Valued Functions 00:11:00 Multi Statement 00:16:00 Views and Stored Procedures 00:18:00 Module: 05 Transaction Concepts 00:30:00 Applications and Transactions 00:13:00 Creating Explicit Transactions 00:29:00 Structured Error Handling 00:25:00 Module: 06 Introduction to SQL CLR 00:08:00 Writing SQL CLR Code 00:24:00 Using SQL CLR Code 00:22:00 SQL CLR Code 00:13:00 SQL CLR Security 00:20:00 SQL for Data Analytic - Intermediate Level Module: 07 Topic A Aggregates FREE 00:27:00 Topic B User Defined Types 00:17:00 Topic C Table and Functions 00:10:00 Topic D Managing Code Modules 00:08:00 Module: 08 Topic A Introduction Data Tools 00:19:00 Topic B Connected Database Development 00:20:00 Topic C Updating a Connected Database 00:16:00 Module: 09 Topic A Creating New Objects with TSQL 00:12:00 Topic B Comparing Database Schemas 00:10:00 Topic C Offline Database Development 00:13:00 Topic D Database Project Settings 00:18:00 Module: 10 Topic A Using APPLY 00:15:00 Topic B MERGE Statement 00:23:00 Topic C Creating Recursive Queries 00:08:00 Topic D Grouping Sets 00:12:00 Module: 11 Topic A ROLLUP and CUBE Operators 00:12:00 Topic B Creating Pivot Queries 00:14:00 Topic C Executing Dynamic SQL 00:28:00 Module: 12 Topic A Complex Query Execution 00:26:00 Topic B Using Execution Plans 00:16:00 Topic C Execution Plan Operators 00:12:00 Topic D Common Join Operators 00:16:00 Module: 13 Topic A Hierarchy ID Data Type 00:12:00 Topic B Exploring a Hierarchy 00:09:00 Topic C Sparse Columns 00:16:00 Topic D Column Sets 00:12:00 Module: 14 Topic A Filtered Indexes 00:09:00 Topic B FILESTREAM Storage 00:22:00 Topic C Adding Sequences to Queries 00:22:00 Module: 15 Topic A Introduction to Spatial Data 00:13:00 Topic B Spatial Data in SQL Server 00:23:00 Topic C Manipulating Spatial Shapes 00:10:00 Topic D Interactions Between Objects 00:09:00 SQL for Data Analytic - Advanced Level Module: 16 Topic A Partitioning FREE 00:20:00 Topic B Creating Partition Tables 00:30:00 Topic C Querying Partitions 00:14:00 Topic D Managing Partitions 00:17:00 Module: 17 Topic A Complex Querying 00:22:00 Topic B Rankings 00:15:00 Topic C SubQueries 00:21:00 Topic D Common Table Expressions 00:17:00 Module: 18 Topic A Complex Data and Structures 00:26:00 Topic B Efficient Queries 00:36:00 Topic C Complex Queries 00:17:00 Module: 19 Topic A XML Data Type 00:28:00 Topic B XML Schemas 00:17:00 Topic C Querying XML 00:20:00 Topic D Best Practices 00:08:00

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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.

Duration 1 Days 6 CPD hours This course is intended for This course is designed for data scientists with experience of Python who need to learn how to apply their data science and machine learning skills on Azure Databricks. Overview After completing this course, you will be able to: Provision an Azure Databricks workspace and cluster Use Azure Databricks to train a machine learning model Use MLflow to track experiments and manage machine learning models Integrate Azure Databricks with Azure Machine Learning Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this course, students will learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. Introduction to Azure Databricks Getting Started with Azure Databricks Working with Data in Azure Databricks Training and Evaluating Machine Learning Models Preparing Data for Machine Learning Training a Machine Learning Model Managing Experiments and Models Using MLflow to Track Experiments Managing Models Managing Experiments and Models Using MLflow to Track Experiments Managing Models Integrating Azure Databricks and Azure Machine Learning Tracking Experiments with Azure Machine Learning Deploying Models

Duration 5 Days 30 CPD hours This course is intended for Security Professionals working with Kubernetes Clusters Container Orchestration Engineers DevOps Professionals Overview In this course, students will learn and practice essential Kubernetes concepts and tasks in the following sections: Cloud Security Fundamentals Cluster Hardening System Hardening Minimize Microservice Vulnerabilities Supply Chain Security Disaster Recovery Secure Back-up and Restore This class prepares students for the Certified Kubernetes Security Specialist (CKS) exam. Kubernetes is a Cloud Orchestration Platform providing reliability, replication, and stabilitywhile maximizing resource utilization for applications and services. By the conclusion of this hands-on, vendor agnostic training you will be equipped with a thorough understanding ofcloud security fundamentals, along with the knowledge, skills and abilities to secure a Kubernetes cluster, detect threats, and properly resolve a security catastrophe. This courseincludes hands-on instruction which develops skills and knowledge for securing container-based applications and Kubernetes platforms, during build, deployment, and runtime. We prioritizecovering all objectives and concepts necessary for passing the Certified Kubernetes Security Specialist (CKS) exam. You will be provided the components necessary to assemble your ownhigh availability Kubernetes environment and harden it for your security needs. Learning Your Environment Underlying Infrastructure Using Vim Tmux Cloud Security Primer Basic Principles Threat Analysis Approach CIS Benchmarks Securing your Kubernetes Cluster Kubernetes Architecture Pods and the Control Plane Kubernetes Security Concepts Install Kubernetes using kubeadm Configure Network Plugin Requirements Kubeadm Basic Cluster Installing Kubeadm Join Node to Cluster Kubeadm Token Manage Kubeadm Tokens Kubeadm Cluster Upgrade Securing the kube-apiserver Configuring the kube-apiserver Enable Audit Logging Falco Deploy Falco to Monitor System Calls Enable Pod Security Policies Encrypt Data at Rest Encryption Configuration Benchmark Cluster with Kube-Bench Kube-Bench Securing ETCD ETCD Isolation ETCD Disaster Recovery ETCD Snapshot and Restore Purge Kubernetes Purge Kubeadm 3Purge Kubeadm Image Scanning Container Essentials Secure Containers Creating a Docker Image Scanning with Trivy Trivy Snyk Security Manually Installing Kubernetes Kubernetes the Alta3 Way Deploy Kubernetes the Alta3 Way Validate your Kubernetes Installation Sonobuoy K8s Validation Test Kubectl (Optional) Kubectl get and sorting kubectl get kubectl describe Labels (Optional) Labels Labels and Selectors Annotations Insert an Annotation Securing your Application Scan a Running Container Tracee Security Contexts for Pods Understanding Security Contexts AppArmor Profiles AppArmor Isolate Container Kernels gVisor Pod Security Pod Security Policies Deploy a PSP Pod Security Standards Enable PSS Open Policy Agent (OPA) Admission Controller Create a LimitRange Open Policy Agent Policy as Code Deploy Gatekeeper User Administration Contexts Contexts Authentication and Authorization Role Based Access Control Role Based Access Control RBAC Distributing Access Service Accounts Limit Pod Service Accounts Securing Secrets Secrets Create and Consume Secrets Hashicorp Vault Deploy Vault Securing the Network Networking Plugins NetworkPolicy Deploy a NetworkPolicy mTLS Linkerd mTLS with istio istio Threat Detection Active Threat Analysis Host Intrusion Detection Deploy OSSEC Network Intrusion Detection Deploy Suricata Physical Intrusion Detection Disaster Recovery Harsh Reality of Security Deploy a Response Plan Kasten K10 Backups Deploy K10

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Dive into the heart of Big Data Infrastructure, exploring storage systems, distributed file frameworks, and processing paradigms. This course provides a comprehensive understanding of key components like HDFS, Apache Spark, and Cassandra, offering insights into their architecture, use cases, and real-world applications. This course is a deep dive into the complex landscape of Big Data Infrastructure. From unravelling the architecture of Apache Spark to dissecting the benefits of distributed file systems, participants gain expertise in assessing, comparing, and implementing various Big Data storage and processing systems. Scalability, fault-tolerance, and industry-specific case studies add practical depth to theoretical knowledge. After the successful completion of this course, you will be able to: Understand the Components of Big Data Infrastructure, Including Storage Systems, Distributed File Systems, and Processing Frameworks. Identify the Characteristics and Benefits of Distributed File Systems Such as Hadoop Distributed File System (H.D.F.S). Describe the Architecture and Capabilities of Apache Spark and its Role in Big Data Processing. Recognise the Use Cases and Benefits of Apache Cassandra as a Distributed N..O.S.Q.L Database. Compare and Contrast Different Big Data Storage and Processing Systems Such as Hadoop, Spark, and Cassandra. Understand the Scalability and Fault-tolerance Mechanisms Used in Big Data Infrastructure, Such as Sharding and Replication. Appreciate the Challenges Associated with Deploying and Managing Big Data Infrastructure, Such as Hardware and Software Configuration and Security Considerations. Explore the intricacies of Big Data Infrastructure, from understanding storage systems to unraveling the nuances of distributed file frameworks and processing engines. Gain a comprehensive view of scalability, fault-tolerance mechanisms, and industry-specific challenges through engaging case studies. Equip yourself to navigate the dynamic landscape of Big Data with confidence and expertise. VIDEO - Course Structure and Assessment Guidelines Watch this video to gain further insight. Navigating the MSBM Study Portal Watch this video to gain further insight. Interacting with Lectures/Learning Components Watch this video to gain further insight. Big Data Infrastructure Self-paced pre-recorded learning content on this topic. Big Data Infrastructure Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details. There are no strict entry requirements for this course. Work experience will be an added advantage to understanding the content of the course. The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone who is eager to know more and get updated on current ideas in their respective field. We recommend this certificate for the following audience. Big Data Infrastructure Engineer Hadoop Administrator Spark Developer Cassandra Database Administrator Big Data Solutions Architect Data Infrastructure Manager NoSQL Database Analyst Big Data Consultant Average Completion Time 2 Weeks Accreditation 3 CPD Hours Level Advanced Start Time Anytime 100% Online Study online with ease. Unlimited Access 24/7 unlimited access with pre-recorded lectures. Low Fees Our fees are low and easy to pay online.

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