Course Offerings

What you will learn By the end of this course, participants will be able to: Understand the benefits and challenges of using Big Data as a service Gain familiarity with cloud-based Big Data platforms and services offered by leading cloud providers Learn how to store, manage, and process large volumes of data using cloud-based Big Data services Explore techniques for performing advanced analytics, machine learning, and AI on Big Data in the cloud Develop skills to optimize performance, security, and cost-efficiency in cloud-based Big Data solutions Gain insights into real-world use cases and applications of Big Data as a service across different industries and domains Beneficial for This course is suitable for: Data Engineers Data Analysts IT Professionals involved in data management and analytics Data Scientists Business Intelligence Professionals Course Pre-requisite Participants should have a basic understanding of: Basic understanding of Big Data concepts and technologies Familiarity with cloud computing concepts Knowledge of programming languages such as Python or Java (beneficial but not mandatory) Experience working with data analysis and visualization tools (beneficial but not mandatory) Course Outline Module 1: Introduction to Big Data as a Service Overview of Big Data technologies and their evolution Understanding the concept of Big Data as a service and its advantages Overview of cloud-based Big Data platforms offered by leading cloud providers Comparing and contrasting different cloud-based Big Data services Module 2: Storage and Data Management in the Cloud Storing and managing large volumes of data in the cloud using scalable storage solutions Exploring cloud-based data warehousing and data lake services Processing and analyzing Big Data at scale using cloud-based data processing services Performing data analytics, descriptive, diagnostic, predictive, and prescriptive analytics Module 3: Machine Learning and AI on Big Data Leveraging machine learning and AI technologies for advanced analytics on Big Data Exploring cloud-based machine learning and AI services for Big Data applications Module 4: Big Data Architectures in the Cloud Designing scalable and resilient Big Data architectures in the cloud Best practices for building fault-tolerant and high-performance Big Data solutions in the cloud Module 5: Performance Optimization and Cost Management Optimizing performance and cost-efficiency in cloud-based Big Data solutions Monitoring and managing resources to minimize costs and maximize performance Module 6: Real-World Use Cases and Applications Analyzing real-world use cases and applications of Big Data as a service Understanding how organizations leverage cloud-based Big Data platforms for business value

What you will learn By the end of this course, participants will be able to: Understand the benefits and challenges of using cloud-based data services Gain familiarity with various cloud computing platforms and their data service offerings Learn how to store, manage, and secure data in the cloud using scalable storage solutions Explore techniques for processing and analyzing data at scale using cloud-based analytics services Discover best practices for designing and implementing data pipelines and workflows in the cloud Develop skills to leverage cloud-based data services for real-time data processing and streaming analytics Gain insights into cost optimization strategies and performance tuning techniques for cloud-based data services Beneficial for This course is suitable for: Data Engineers Data Analysts Data Scientists Database Administrators IT Professionals involved in data management and analytics List Item Course Pre-requisite Participants should have a basic understanding of: Basic understanding of cloud computing concepts Familiarity with data storage and processing concepts Knowledge of SQL and database management systems (beneficial but not mandatory) Experience working with at least one cloud platform (e.g., AWS, Azure, GCP) (beneficial but not mandatory) Course Outline Module 1: Introduction to Cloud-Based Data Services Overview of cloud computing and its impact on data management and analytics Introduction to cloud-based data service models (IaaS, PaaS, SaaS) Module 2: Cloud Storage Services Overview of cloud storage options (object storage, block storage, file storage) Deep dive into cloud storage services provided by leading cloud providers Module 3: Cloud Database Services Introduction to cloud database options (relational, NoSQL, NewSQL) Exploring cloud-based database services for transactional and analytical workloads Module 4: Big Data and Analytics Services Overview of cloud-based big data and analytics platforms Leveraging cloud-based services for data processing, analytics, and machine learning Module 5: Data Pipelines and ETL in the Cloud Designing and implementing data pipelines using cloud-based ETL tools Best practices for orchestrating and scheduling data workflows in the cloud Module 6: Real-Time Data Processing and Streaming Analytics Introduction to real-time data processing and streaming analytics Leveraging cloud-based services for real-time data ingestion, processing, and analysis Module 7: Data Governance and Security in the Cloud Ensuring data governance and compliance in cloud-based data services Implementing security best practices for protecting data in the cloud Module 8: Cost Optimization and Performance Tuning Strategies for optimizing costs and performance in cloud-based data services Monitoring, measuring, and optimizing resource utilization in the cloud

What you will learn By the end of this course, participants will be able to: Understand the ETL process and its role in data integration and analytics Gain proficiency in designing and implementing ETL workflows using popular ETL tools Learn how to extract data from various sources, transform it to meet business requirements, and load it into target systems Explore techniques for data cleansing, validation, and enrichment during the transformation phase Learn how to handle errors, monitor ETL jobs, and troubleshoot issues effectively Gain insights into best practices for ETL design, performance optimization, and scalability Beneficial for This course is suitable for: Data Engineers ETL Developers Data Analysts Database Administrators Business Intelligence Professionals IT Professionals involved in data integration and analytics Course Pre-requisite Participants should have a basic understanding of: Basic understanding of databases and SQL Familiarity with data formats and structures (e.g., CSV, JSON) Knowledge of programming languages (e.g., Python, Java) is beneficial but not mandatory Course Outline Module 1: Introduction to ETL Overview of the ETL process and its significance Key components of ETL: Extract, Transform, Load Module 2: ETL Architecture and Tools Understanding ETL architecture models (e.g., batch processing, real-time) Overview of popular ETL tools and platforms (e.g., Informatica, Talend, Apache NiFi) Module 3: Extracting Data Extracting data from various sources (e.g., databases, files, APIs) Techniques for incremental data extraction and change data capture (CDC) Module 4: Transforming Data Data transformation techniques and best practices Performing data cleansing, validation, and enrichment Introduction to data integration patterns (e.g., aggregation, joining, deduplication) Module 5: Loading Data Loading transformed data into target systems (e.g., data warehouses, data lakes) Techniques for efficient data loading and bulk data loading Implementing error handling and logging during the loading phase Module 6: ETL Optimization and Performance Tuning Strategies for optimizing ETL workflows and job performance Techniques for parallel processing, partitioning, and data compression Monitoring and managing ETL jobs for performance optimization Module 7: ETL Best Practices and Governance Best practices for ETL design, development, and deployment Implementing data quality checks and data governance in ETL processes Compliance considerations and regulatory requirements in ETL operations Module 8: Case Studies and Real-World Applications Analyzing real-world ETL use cases and scenarios Understanding challenges and solutions in ETL implementation Best practices for ETL in different industries and domains

What you will learn By the end of this course, participants will be able to: Understand the fundamental concepts of cloud computing and AWS Gain familiarity with core AWS services, including compute, storage, networking, and databases Learn how to create and manage AWS resources using the AWS Management Console Explore best practices for security, scalability, and cost optimization on AWS Gain confidence to explore more advanced AWS topics and certifications Beneficial for This course is suitable for: Individuals new to cloud computing and AWS IT professionals exploring cloud computing opportunities Entrepreneurs and business owners interested in cloud services Course Pre-requisite Participants should have a basic understanding of: No prior knowledge or experience with AWS is required. Participants are expected to have: Basic understanding of computer systems and networking concepts Familiarity with using computers and web browsers Course Outline Module 1: Introduction to Cloud Computing and AWS Overview of cloud computing concepts and benefits Introduction to AWS and its global infrastructure Module 2: Getting Started with AWS Creating an AWS account and accessing the AWS Management Console Understanding AWS Regions, Availability Zones, and Edge Locations Module 3: Compute Services on AWS Introduction to Amazon EC2 (Elastic Compute Cloud) Launching and managing EC2 instances Module 4: Storage and Content Delivery Overview of Amazon S3 (Simple Storage Service) Storing and retrieving data in Amazon S3 buckets Introduction to Amazon CloudFront for content delivery Module 5: Networking and Security Introduction to Amazon VPC (Virtual Private Cloud) Configuring network access control with security groups and network ACLs Overview of AWS Identity and Access Management (IAM) Module 6: Databases on AWS Introduction to Amazon RDS (Relational Database Service) Deploying and managing relational databases in Amazon RDS Overview of Amazon DynamoDB for NoSQL database needs Module 7: Scaling and Monitoring Understanding auto-scaling and load balancing concepts Monitoring AWS resources using Amazon CloudWatch Module 8: Best Practices and Next Steps Best practices for security, reliability, performance, and cost optimization on AWS Next steps for further learning and exploring advanced AWS topics

What you will learn By the end of this course, participants will be able to: Understand the fundamentals of data analytics and its applications Learn various data analysis techniques, including descriptive, diagnostic, predictive, and prescriptive analytics Gain proficiency in data visualization tools and techniques Utilize statistical methods to analyze and interpret data Apply machine learning algorithms for predictive modeling and pattern recognition Learn how to clean, preprocess, and prepare data for analysis Develop the ability to communicate insights effectively through data storytelling Beneficial for This course is suitable for: Data Analysts Business Analysts Data Scientists Data Engineers IT Professionals interested in data analysis Course Pre-requisite Participants should have a basic understanding of: Basic understanding of statistics and probability Familiarity with data manipulation using spreadsheet software (e.g., Excel) Basic programming knowledge (e.g., Python, R) is beneficial but not mandatory Course Outline Module 1: Introduction to Data Analytics Overview of data analytics and its significance Key concepts and terminologies in data analytics Applications of data analytics across different industries Module 2: Data Exploration and Preparation Understanding data types and structures Data cleaning and preprocessing techniques Exploratory data analysis (EDA) using statistical methods and visualization tools Module 3: Descriptive Analytics Summarizing and visualizing data using descriptive statistics Understanding central tendency, dispersion, and distribution of data Creating visualizations such as histograms, box plots, and scatter plots Module 4: Diagnostic Analytics Identifying patterns and relationships in data Correlation and regression analysis techniques Root cause analysis and hypothesis testing Module 5: Predictive Analytics Introduction to predictive modeling Supervised and unsupervised learning algorithms Model evaluation and validation techniques Module 6: Prescriptive Analytics Optimizing decision-making using prescriptive analytics Linear and integer programming techniques Simulation and optimization methods Module 7: Data Visualization Principles of effective data visualization Tools and techniques for data visualization (e.g., Tableau, Power BI) Designing interactive dashboards and reports Module 8: Machine Learning for Data Analytics Introduction to machine learning concepts and algorithms Supervised and unsupervised learning techniques Implementing machine learning models for predictive analytics Module 9: Big Data Analytics Introduction to big data technologies (e.g., Hadoop, Spark) Processing and analyzing large-scale datasets Distributed computing and parallel processing techniques Module 10: Data Ethics and Privacy Understanding ethical considerations in data analytics Privacy regulations and compliance (e.g., GDPR, HIPAA) Best practices for ethical data handling and usage

What you will learn By the end of this course, participants will be able to: Understand the principles and importance of data governance Learn how to establish data governance frameworks and policies Implement data governance processes for data quality management Define roles and responsibilities for data stewardship and management Develop strategies for data classification, privacy, and security Ensure compliance with regulatory requirements and industry standards Implement data governance tools and technologies effectively Beneficial for This course is suitable for: Data Stewards Data Analysts Data Managers Data Architects Compliance Officers Information Governance Professionals Anyone involved in data management and governance initiatives Course Pre-requisite Participants should have a basic understanding of: Basic understanding of data management concepts Familiarity with organizational data structures and workflows Awareness of regulatory compliance requirements (e.g., GDPR, HIPAA) Proficiency in using basic data analysis tools (e.g., Excel, SQL) Course Outline Module 1: Introduction to Data Governance Understanding the fundamentals of data governance Exploring the benefits and challenges of data governance implementation Overview of key concepts, principles, and frameworks Module 2: Establishing Data Governance Frameworks Designing and implementing data governance policies and procedures Defining data governance roles and responsibilities Establishing data governance committees and oversight structures Module 3: Data Quality Management Understanding the importance of data quality in data governance Implementing data quality assessment and improvement processes Leveraging data quality tools and techniques Module 4: Data Stewardship and Management Defining data stewardship roles and responsibilities Implementing data stewardship processes and workflows Developing data stewardship metrics and performance measures Module 5: Data Classification, Privacy, and Security Classifying data assets based on sensitivity and criticality Ensuring data privacy and protection in accordance with regulations (e.g., GDPR, CCPA) Implementing data security measures and controls Module 6: Regulatory Compliance and Standards Understanding regulatory compliance requirements (e.g., GDPR, HIPAA, SOX) Ensuring adherence to industry standards and best practices Implementing controls and processes for compliance monitoring and reporting Module 7: Data Governance Tools and Technologies Overview of data governance tools and technologies Evaluating and selecting data governance solutions Implementing data governance frameworks using technology platforms Module 8: Case Studies and Best Practices Analyzing real-world data governance implementations Learning from best practices and success stories Applying lessons learned to optimize data governance initiatives

What you will learn By the end of this course, participants will be able to: Understand the fundamental principles and architecture of MDM Design and implement effective MDM data models Manage data quality and implement cleansing techniques Choose the right MDM implementation strategy based on organizational needs Establish MDM governance frameworks and ensure data security Integrate MDM with enterprise systems and handle data conflicts Gain hands-on experience with popular MDM tools (optional) Explore real-time MDM and IoT integration scenarios Stay updated on future trends in the dynamic field of Master Data Management. Beneficial for This course is suitable for: Database Administrators Data Stewards and Governance Professionals Data Architects and Modelers Business Analysts Data Quality Analysts Information Management Professionals Anyone involved in data management, governance, or IT strategy Course Pre-requisite Participants should have a basic understanding of: Basic understanding of data management concepts Familiarity with database systems and data structures Knowledge of data governance principles (beneficial but not mandatory) Basic understanding of enterprise IT environments Course Outline Module 1: Introduction to Master Data Management (MDM) Understanding the significance of MDM in data governance Key concepts and principles of MDM Overview of MDM architecture and components Module 2: MDM Data Models and Structures Designing and implementing master data models Hierarchies and relationships in master data Handling different data structures in MDM Module 3: Data Quality Management in MDM Importance of data quality in MDM Data profiling and cleansing techniques Implementing data quality rules and standards Module 4: MDM Implementation Strategies Choosing between centralized, decentralized, or hybrid MDM architectures Evaluating data consolidation and integration methods Implementing MDM for various data domains (customer, product, vendor, etc.) Module 5: MDM Governance and Security Establishing MDM governance frameworks Role-based access control and security measures Ensuring compliance with data regulations and policies Module 6: MDM Integration with Enterprise Systems Integrating MDM with ERP, CRM, and other enterprise systems Data synchronization and data sharing strategies Managing data conflicts and resolution in integrated environments Module 7: MDM Best Practices and Case Studies Best practices for successful MDM implementation Case studies of successful MDM projects Learning from industry-specific MDM success stories Module 8: MDM Tools and Technologies Overview of popular MDM tools in the market Evaluating and selecting the right MDM technology Hands-on exercises with a selected MDM tool (optional) Module 9: Real-time MDM and IoT Integration Understanding real-time MDM requirements Integrating MDM with Internet of Things (IoT) data Addressing challenges in real-time MDM environments Module 10: Future Trends in MDM Exploring emerging trends in Master Data Management The role of artificial intelligence (AI) and machine learning (ML) in MDM Continuous learning and staying updated in the evolving MDM landscape

What you will learn By the end of this course, participants will be able to: Understand the integration of Azure services in the DevOps lifecycle. Implement CI/CD pipelines using Azure DevOps Utilize Azure services for infrastructure provisioning and management Implement infrastructure as code (IaC) using Azure Resource Manager (ARM) templates or Terraform. Apply monitoring, logging, and security solutions for Azure-based applications Utilize Azure DevOps for agile project management and collaboration. Beneficial for This course is suitable for: DevOps Engineers System Administrators IT Professionals involved in software delivery and operations Cloud Architects Developers Course Pre-requisite Participants should have a basic understanding of: Basic understanding of cloud computing concepts Familiarity with DevOps principles and practices Experience with at least one programming language (e.g., PowerShell, Python) Knowledge of source code management tools (e.g., Git) Familiarity with Microsoft Azure (beneficial but not mandatory) Course Outline Module 1: Introduction to Azure for DevOps Overview of Azure services relevant to DevOps Key DevOps principles and their application on Azure Module 2: CI/CD with Azure DevOps Setting up CI/CD pipelines using Azure Pipelines Integrating CI/CD with source control repositories (e.g., Git) Module 3: Infrastructure as Code (IaC) with Azure Introduction to Azure Resource Manager (ARM) templates Implementing IaC using ARM templates or Terraform Module 4: Application Deployment and Management Deploying applications to Azure App Service Managing application deployments and configurations Module 5: Azure Monitoring and Logging Implementing monitoring solutions with Azure Monitor Configuring logging and diagnostics for Azure resources Module 6: Azure Security Best Practices Implementing security best practices for Azure resources Utilizing Azure Security Center for threat detection and response Module 7: Azure DevOps for Collaboration and Agile Project Management Utilizing Azure Boards for agile project management Implementing DevOps collaboration using Azure Repos and Azure Artifacts Module 8: Case Studies and Best Practices Real-world case studies of Azure DevOps implementations Best practices for efficient and secure DevOps on Azure

What you will learn By the end of this course, participants will be able to: Understand the integration of AWS services in the DevOps lifecycle. Implement continuous integration and continuous delivery (CI/CD) pipelines using AWS DevOps tools. Utilize AWS services for infrastructure provisioning and management. Implement infrastructure as code (IaC) using AWS CloudFormation or Terraform. Apply monitoring, logging, and security solutions for cloud-native applications on AWS. Utilize AWS DevOps tools for agile project management and collaboration. Beneficial for This course is suitable for: DevOps Engineers System Administrators IT Professionals involved in software delivery and operations Cloud Architects Developers transitioning to DevOps roles Course Pre-requisite Participants should have a basic understanding of: Basic understanding of cloud computing concepts Familiarity with DevOps principles and practices Experience with at least one programming language (e.g., Python, Java) Fundamental knowledge of source code management (e.g., Git) Course Outline Module 1: Introduction to AWS for DevOps Overview of AWS services relevant to DevOps Key DevOps principles and their application on AWS Module 2: CI/CD with AWS Setting up CI/CD pipelines using AWS CodePipeline Integrating CI/CD with source control repositories (e.g., AWS CodeCommit, GitHub) Module 3: Infrastructure as Code (IaC) with AWS Introduction to AWS CloudFormation Implementing IaC using AWS CloudFormation or Terraform Module 4: Container Orchestration with AWS ECS/EKS Introduction to Amazon Elastic Container Service (ECS) and Amazon (EKS) Deploying and managing containerized applications on ECS or EKS Module 5: Monitoring and Logging in AWS Implementing monitoring solutions with Amazon CloudWatch Configuring logging and error tracking for AWS resources Module 6: Security Best Practices in AWS Applying security best practices for AWS resources Utilizing AWS Identity and Access Management (IAM) for access control Module 7: Agile Project Management with AWS DevOps Tools Utilizing AWS CodeCommit, AWS CodeBuild, and AWS CodeDeploy for agile project  Implementing DevOps collaboration using AWS CodeStar Module 8: Case Studies and Best Practices Real-world case studies of AWS DevOps implementations Best practices for efficient and secure DevOps on AWS

What you will learn By the end of this course, participants will be able to: Understand the integration of GCP services in the DevOps lifecycle. Implement continuous integration and continuous delivery (CI/CD) pipelines on GCP. Utilize GCP tools for container orchestration and management. Implement infrastructure as code (IaC) using GCP Deployment Manager or Terraform. Apply monitoring and logging solutions for cloud-native applications on GCP. Automate and optimize resource provisioning and management. Implement security best practices in GCP DevOps workflows. Beneficial for This course is suitable for: DevOps Engineers System Administrators IT Professionals involved in software delivery and operations Cloud Architects Developers transitioning to DevOps roles Course Pre-requisite Participants should have a basic understanding of: Basic understanding of cloud computing concepts Familiarity with DevOps principles and practices Experience with at least one programming language (e.g., Python, Java) Fundamental knowledge of source code management (e.g., Git) Course Outline Module 1: Introduction to GCP for DevOps Overview of GCP services relevant to DevOps Key DevOps principles and their application on GCP Module 2: CI/CD with GCP Setting up CI/CD pipelines using Cloud Build Integrating CI/CD with version control systems Module 3: Container Orchestration with GKE Introduction to Google Kubernetes Engine (GKE) Deploying and managing containerized applications on GKE Module 4: Infrastructure as Code (IaC) Using GCP Deployment Manager for IaC Introduction to Terraform for GCP Module 5: Monitoring and Logging in GCP Implementing monitoring solutions with Stackdriver Logging and error tracking for cloud-native applications Module 6: Automation and Scaling Automating resource provisioning with Deployment Manager or Terraform Implementing auto-scaling for GCP resources Module 7: Security Best Practices Applying security principles in GCP DevOps workflows Utilizing Identity and Access Management (IAM) for access control Real-world case studies of GCP DevOps implementations Best practices for efficient and secure DevOps on GCP