27 Data Warehouse courses in Cardiff delivered Live Online

Duration 5 Days 30 CPD hours This course is intended for Data Warehouse AdministratorDatabase Administrators Overview Use the Oracle Database tuning methodology appropriate to the available toolsUtilize database advisors to proactively tune an Oracle Database InstanceUse the tools based on the Automatic Workload Repository to tune the databaseDiagnose and tune common SQL related performance problemsDiagnose and tune common Instance related performance problemsUse Enterprise Manager performance-related pages to monitor an Oracle DatabaseGain an understanding of the Oracle Database Cloud Service In the Oracle Database 12c: Performance Management and Tuning course, learn about the performance analysis and tuning tasks expected of a DBA: proactive management through built-in performance analysis features and tools, diagnosis and tuning of the Oracle Database instance components, and diagnosis and tuning of SQL-related performance issues. In this course, you will be introduced to Oracle Database Cloud Service. Introduction Course Objectives Course Organization Course Agenda Topics Not Included in the Course Who Tunes? What Does the DBA Tune? How to Tune Tuning Methodology Basic Tuning Diagnostics Performance Tuning Diagnostics Performance Tuning Tools Tuning Objectives Top Timed Events DB Time CPU and Wait Time Tuning Dimensions Time Model Dynamic Performance Views Using Automatic Workload Repository Automatic Workload Repository Overview Automatic Workload Repository Data Enterprise Manager Cloud Control and AWR Snapshots Reports Compare Periods Defining the Scope of Performance Issues Defining the Problem Limiting the Scope Setting the Priority Top SQL Reports Common Tuning Problems Tuning During the Life Cycle ADDM Tuning Session Performance Versus Business Requirements Using Metrics and Alerts Metrics and Alerts Overview Limitation of Base Statistics Benefits of Metrics Viewing Metric History Information Viewing Histograms Server-Generated Alerts Setting Thresholds Metrics and Alerts Views Using Baselines Comparative Performance Analysis with AWR Baselines Automatic Workload Repository Baselines Moving Window Baseline Baselines in Performance Page Settings Baseline Templates AWR Baseslines Creating AWR Baselines Managing Baselines with PL/SQL Using AWR-Based Tools Automatic Maintenance Tasks ADDM Performance Monitoring Using Compare Periods ADDM Active Session History New or Enhanced Automatic Workload Repository Views Emergency Monitoring Real-time ADDM Real-Time Database Operation Monitoring Overview Use Cases Defining a Database Operation Scope of a Composite Database Operation Database Operation Concepts Identifying a Database Operation Enabling Monitoring of Database Operations Identifying, Starting, and Completing a Database Operation Monitoring Applications What is a Service? Service Attributes Service Types Creating Services Managing Services in a Single-Instance Environment Where are Services Used? Using Services with Client Applications Services and Pluggable Databases Identifying Problem SQL Statements SQL Statement Processing Phases Role of the Oracle Optimizer Identifying Bad SQL Top SQL Reports SQL Monitoring What is an Execution Plan? Methods for Viewing Execution Plans Uses of Execution Plans Influencing the Optimizer Functions of the Query Optimizer Selectivity Cardinality and Cost Changing Optimizer Behavior Optimizer Statistics Extended Statistics Controlling the Behavior of the Optimizer with Parameters Enabling Query Optimizer Features Reducing the Cost of SQL Operations Reducing the Cost Index Maintenance SQL Access Advisor Table Maintenance for Performance Table Reorganization Methods Space Management Extent Management Data Storage Using SQL Performance Analyzer Real Application Testing: Overview Real Application Testing: Use Cases SQL Performance Analyzer: Process Capturing the SQL Workload Creating a SQL Performance Analyzer Task SQL Performance Analyzer: Tasks Parameter Change SQL Performance Analyzer Task Page SQL Performance Management Maintaining SQL Performance Maintaining Optimizer Statistics Automated Maintenance Tasks Statistic Gathering Options Setting Statistic Preferences Restore Statistics Deferred Statistics Publishing Automatic SQL Tuning Using Database Replay Using Database Replay The Big Picture System Architecture Capture Considerations Replay Considerations: Preparation Replay Considerations Replay Options Replay Analysis Tuning the Shared Pool Shared Pool Architecture Shared Pool Operation The Library Cache Latch and Mutex Diagnostic Tools for Tuning the Shared Pool Avoiding Hard Parses Reducing the Cost of Soft Parses Sizing the Shared Pool Tuning the Buffer Cache Oracle Database Architecture: Buffer Cache Buffer Cache: Highlights Database Buffers Buffer Hash Table for Lookups Working Sets Buffer Cache Tuning Goals and Techniques Buffer Cache Performance Symptoms Buffer Cache Performance Solutions Tuning PGA and Temporary Space SQL Memory Usage Performance Impact Automatic PGA Memory SQL Memory Manager Configuring Automatic PGA Memory Setting PGA_AGGREGATE_TARGET Initially Limiting the size of the Program Global Area (PGA) SQL Memory Usage Automatic Memory Oracle Database Architecture Dynamic SGA Granule Memory Advisories Manually Adding Granules to Components Increasing the Size of an SGA Component Automatic Shared Memory Management: Overview SGA Sizing Parameters: Overview Performance Tuning Summary with Waits Commonly Observed Wait Events Additional Statistics Top 10 Mistakes Found in Customer Systems Symptoms Oracle Database Cloud Service: Overview Database as a Service Architecture, Features and Tooling Software Editions: Included Database Options and Management Packs Accessing the Oracle Database Cloud Service Console Automated Database Provisioning Managing the Compute Node Associated With a Database Deployment Managing Network Access to Database as a Service Scaling a Database Deployment Performance Management in the Database Cloud Environment Performance Monitoring and Tuning What Can be Tuned in a DBCS Environment?

Duration 5 Days 30 CPD hours This course is intended for Data Warehouse Administrator Database Administrators Database Designers Support Engineer Technical Administrator Overview Back up, restore, and patch DBCS database deployments Describe the DBaaS and on-premises Oracle Database architectures Manage database instances Manage UNDO data Move data between databases Implement basic backup and recovery procedures Monitor and tune database performance Tune SQL to improve performance Manage resources with Oracle Database Resource Manager Create pluggable databases (PDBs) Configure the Oracle network environment Administer user security and implement auditing Create and manage tablespaces Manage storage space Create and manage Database as a Cloud Service (DBCS) database deployments Register databases and manage performance with Enterprise Manager Cloud Control The Oracle Database 12c R2: Administration Workshop Ed 3 course is designed to provide students with a firm foundation in administration of an Oracle Database. In this course students will gain a conceptual understanding of Oracle Database architecture and learn how to manage an Oracle Database in an effective and efficient manner. Exploring Oracle Database Architecture Introducing Oracle Database Relation Database Models Oracle SQL and PL/SQL Oracle Database Server Architecture Connecting to Oracle Databases Oracle Database Tools Oracle-Supplied User Accounts Querying the Oracle Data Dictionary Managing Database Instances Initialization Parameter Files Starting Up Oracle Databases Shutting Down Oracle Databases Opening and Closing PDBs Working with the Automatic Diagnostic Repository (ADR) Querying Dynamic Performance Views Creating PDBs Methods and Tools to Create PDBs Creating PDBs from Seed with SQL*Plus Cloning PDBs with SQL*Plus Unplugging and Pluggin In PDBs with SQL*Plus Dropping PDBs with SQL*Plus Configuring the Oracle Network Environment Oracle Net Services How Listeners Work Configuring Listeners for Dynamic Service Registration Configuring Listeners for Static Service Registration Configuring Local Naming for Connections Testing Oracle Net Connectivity with tnsping Configuring Communication Between Databases Dedicated Versus Shared Server Configurations Administering User Security Creating Users Granting Privileges Creating and Granting Roles Revoking Privileges and Roles Creating and Assigning Profiles Authenticating Users Assigning Quotas to Users Applying the Principal of Least Privilege Creating and Managing Tablespaces How Table Data is Stored Creating Tablespaces in SQL*Plus Altering and Dropping Tablespaces in SQL*Plus Viewing Tablespace Information in SQL*Plus Implementing Oracle Managed Files Moving and Renaming Online Data Files in SQL*Plus Managing Storage Space Oracle Database Space Management Features Block Space Management Row Chaining and Migration Free Space Management Within Segments Types of Segments Allocating Extents Understanding Deferred Segment Creation Space-Saving Features Managing UNDO Data Undo Data: Overview Transactions and Undo Data Storing Undo Information Comparing Undo Data and Redo Data Managing Undo Local Undo Mode Versus Shared Undo Mode Configuring Undo Retention Categories of Undo Moving Data Moving Data: General Architecture Oracle Data Pump: Overview Oracle Data Pump: Benefits Data Pump Export and Import Clients Data Pump Utility: Interfaces and Modes Data Pump Import: Transformations SQL*Loader Overview Loading Methods Backup and Recovery Concepts DBA Responsibilities Categories of Failure Understanding Instance Recovery Understanding Types of Backups Comparing Complete and Incomplete Recovery Oracle Data Protection Solutions Flashback Technology Monitoring and Tuning Database Performance Managing Performance Activities Performance Planning Considerations Database Maintenance Automatic Workload Repository (AWR) Automatic Database Diagnostic Monitor Performance Monitoring Performance Tuning Methodology Database Server Statistics and Metrics SQL Tuning SQL Tuning Process Oracle Optimizer Optimizer Statistics SQL Plan Directives Adaptive Execution Plans SQL Tuning Advisor SQL Access Advisor SQL Performance Analyzer Oracle Database Resource Manager Oracle Database Resource Manager: Overview Resource Manager Elements Using Resource Manager to Allocate Resources Creating a Simple Resource Plan Creating a Complex Resource Plan Using the Active Session Pool Feature Limiting CPU Utilization at the Database Level Limiting CPU Utilization at the Server Level Enterprise Manager Cloud Control Controlling the Enterprise Manager Cloud Control Framework Starting the Enterprise Manager Cloud Control Framework Stopping the Enterprise Manager Cloud Control Framework Introduction to Oracle Database Cloud Service Oracle Cloud: Overview Database Cloud Service Offerings DBCS Architecture Features and Tooling Additional Database Configuration Options Creating DBCS Database Deployments Automated Database Provisioning Creating a Database Deployment How SSH Key Pairs are Used Creating an SSH Key Pair Storage Used for Database Files Managing DBCS Database Deployments Cloud Tooling Accessing Tools and Features from the DBCS Console Managing the Compute Node Associated With a Database Deployment Managing Network Access to DBCS Enabling Access to a Compute Node Port Scaling a Database Deployment Backing Up and Restoring DBCS Database Deployments Backing Up and Recovering Databases on DBCS Backup Destination Choices Backup Configuration Creating an On-Demand Backup Customizing the Backup Configuration Performing Recovery by Using the Console Performing Recovery by Using the dbaascli Utility Patching DBCS Database Deployments Patching DBCS Using the DBCS Console to Manage Patches Using the dbaascli Utility to Manage Patches Creating Master Encryption Keys for PDBs CDB and PDB Master Encryption Keys Determining Whether You Need to Create and Activate and Encryption Key for a PDB Creating and Activating an Encryption Key Tablespace Encryption by Default Tablespace Encryption by Default in DBCS Transparent Data Encryption (TDE) Overview Components of TDE Using TDE Defining the Keystore Location Controlling Tablespace Encryption by Default Managing the Software Keystore and Master Encryption Key Managing the Keystore in CDBs and PDBs Additional course details: Nexus Humans Oracle Database 12c R2 - Administration Workshop Ed 3 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 Oracle Database 12c R2 - Administration Workshop Ed 3 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.

This BigQuery Conversion course is for users of SQL. We cover the interface and licensing differences and additional capabilities. Highlights of BigQuery. We will cover the general SQL topics ( see our intermediate outline ) and point out where the differences are. You would be able to move to this platform easily.

Duration 3 Days 18 CPD hours This course is intended for Senior Executives CIOs and CTOs Business Intelligence Executives Marketing Executives Data & Business Analytics Specialists Innovation Specialists & Entrepreneurs Academics, and other people interested in Big Data Overview More specifically, BDAW addresses advanced big data architecture topics, including, data formats, transformation, real-time, batch and machine learning processing, scalability, fault tolerance, security and privacy, minimizing the risk of an unsound architecture and technology selection. Big Data Architecture Workshop (BDAW) is a learning event that addresses advanced big data architecture topics. BDAW brings together technical contributors into a group setting to design and architect solutions to a challenging business problem. The workshop addresses big data architecture problems in general, and then applies them to the design of a challenging system. Throughout the highly interactive workshop, students apply concepts to real-world examples resulting in detailed synergistic discussions. The workshop is conducive for students to learn techniques for architecting big data systems, not only from Cloudera?s experience but also from the experiences of fellow students. Workshop Application Use Cases Oz Metropolitan Architectural questions Team activity: Analyze Metroz Application Use Cases Application Vertical Slice Definition Minimizing risk of an unsound architecture Selecting a vertical slice Team activity: Identify an initial vertical slice for Metroz Application Processing Real time, near real time processing Batch processing Data access patterns Delivery and processing guarantees Machine Learning pipelines Team activity: identify delivery and processing patterns in Metroz, characterize response time requirements, identify Machine Learning pipelines Application Data Three V?s of Big Data Data Lifecycle Data Formats Transforming Data Team activity: Metroz Data Requirements Scalable Applications Scale up, scale out, scale to X Determining if an application will scale Poll: scalable airport terminal designs Hadoop and Spark Scalability Team activity: Scaling Metroz Fault Tolerant Distributed Systems Principles Transparency Hardware vs. Software redundancy Tolerating disasters Stateless functional fault tolerance Stateful fault tolerance Replication and group consistency Fault tolerance in Spark and Map Reduce Application tolerance for failures Team activity: Identify Metroz component failures and requirements Security and Privacy Principles Privacy Threats Technologies Team activity: identify threats and security mechanisms in Metroz Deployment Cluster sizing and evolution On-premise vs. Cloud Edge computing Team activity: select deployment for Metroz Technology Selection HDFS HBase Kudu Relational Database Management Systems Map Reduce Spark, including streaming, SparkSQL and SparkML Hive Impala Cloudera Search Data Sets and Formats Team activity: technologies relevant to Metroz Software Architecture Architecture artifacts One platform or multiple, lambda architecture Team activity: produce high level architecture, selected technologies, revisit vertical slice Vertical Slice demonstration Additional course details: Nexus Humans Big Data Architecture Workshop 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 Big Data Architecture Workshop 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 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Architecture and course review Post course knowledge check Architecture review Course review

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines 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 batch data 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 learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks 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 EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake architectures. Module 1: Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Module 2: Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Module 3: Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Module 4: Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Module 5: Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Module 6: Architecture and course review Post course knowledge check Architecture review Course review Additional course details: Nexus Humans Building Data Lakes on AWS 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 Building Data Lakes on AWS 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.