1637 Database courses delivered Online

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.

Learn how to work with and connect multiple data sets to effectively analyse and report on data. Course overview Duration: 1 day (6.5 hours) Within Excel you have some powerful features to enable you to connect and analyse multiple data sources. Power Query enables you to import and manipulate your data, Power Pivot enables you to connect multiple data sources and create pivot tables and pivot charts from them. This course is an introduction to Power Query and Power Pivot in Excel to get you started on creating a powerful reporting capability. Knowledge of working with Excel workbooks and relational databases would be an advantage. Objectives By the end of the course you will be able to: Import data from multiple data sources Edit and transform data before importing Add extra columns of data Append data Merge data from other tables Create data models Build data relationships Build Pivot Tables Build Pivot Charts Use Slicers and Timeline Filters Content Importing data Data sources Importing data Transforming data Editing your data Setting data types Removing columns/rows Choosing columns to keep Setting header rows Splitting columns Appending queries Appending data from other tables Adding text Columns from example Custom columns Conditional columns Merge queries Setting up and using merge queries Merging in columns of data Creating a data model The data model Multiple data tables Connecting tables Building relationships Relationship types Building visuals from multiple tables Analysing information using pivot tables Creating and modifying a Pivot Table Recalculating the Pivot Table Filtering the Pivot Table Searching the Pivot Table Drilling down to underlying data Customising field names Changing field formatting Pivot charts, slices and timelines Creating Pivot Charts Adding and using Slicers

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Duration 2 Days 12 CPD hours Overview Understand why Blockchain is needed and where Explore the major components of BlockchainLearn about Hyperledger Fabric and the structure of the Hyperledger ArchitectureLean the features of the Fabric model including chaincode, SDKs, Ledger, Security and Membership ServicesPerform comprehensive labs on writing chaincodeExplore the architecture of Hyperledger FabricUnderstand and perform in depth labs on Bootstrapping the NetworkPerform comprehensive labs to integrate/develop an application with Hyperledger Fabric running a smart contractBuild applications on Hyperledger FabricCourse Outline: This training course has been created to walk you through Chaincode Development, Testing, and Deployment for a Hyperledger Fabric Network catering specifically toward Golang written Chaincode (Fabric?s original Chaincode Language). Additionally as an Application Developer you will learn how to write, and prepare Client Applications using the most mature Standard Development Kit in Hyperledger Fabric, NodeJS. Blockchain Basics (Overview)Hyperledger Fabric Development EnvironmentKnowing the Difference: ComposerChaincode Use CasesChaincode BasicsGolang Shim DevelopmentDatabases for the DeveloperChaincode Dev. Deployment and InteractionsClients & SDK Development: Fabric-NetworkClients & SDK Development: Fabric-Client InteractionsLogging and Monitoring

Duration 2 Days 12 CPD hours This course is intended for Application Developers Business Analysts Developer End Users Functional Implementer Java Developer System Analysts Technical Administrator Overview Create administrator-level personalizations Personalize configurable pages Utilize advanced personalization features Implement flexfields on OA Framework-based pages Create custom look and feel definitions Create user-level personalizations This course will be applicable for customers who have implemented Oracle E-Business Suite Release 12 or Oracle E-Business Suite 12.1. This course will be applicable for customers who have implemented Oracle E-Business Suite Release 12 or Oracle E-Business Suite 12.1.

Duration 5 Days 30 CPD hours This course is intended for The course provides an intensive hands-on experience for key system administration tasks with the goal of making the system administrator job-ready. Overview Administer the Oracle Solaris 11 Host on an Existing Network Administer Oracle Solaris Zones Control Access to Systems and Files Install Oracle Solaris 11 Operating System Administer User Accounts Administer System Processes and Schedule System Tasks Manage Data by Using ZFS Manage Boot and Shutdown of a System Administer Services by Using SMF Administer Software Packages by Using IPS and Manage Boot Environments Describe the use of IPS in packaging the Oracle Solaris OS Manage boot environments Updating the OS by using IPS The Oracle Solaris 11 System Administration training covers the full range of introductory system administration tasks on Oracle Solaris 11.3 OS. The topics covered range from installing the operating system on a single system, to monitoring and basic troubleshooting. The Oracle Solaris 11 System Administration course is designed to provide new system administrators, as well as enterprise system administrators who are new to the Oracle Solaris 11 Operating System, with the skills they need to perform their job tasks successfully and efficiently. Installing Oracle Solaris 11 Operating System Introduction to Oracle Solaris 11 OS Planning for Oracle Solaris 11 OS installation Installing Oracle Solaris 11 OS by Using the Live Media Installer Installing Oracle Solaris 11 OS Using the Text Installer Verifying the OS Installation Managing Boot and Shutdown of Systems Analyze the boot design and boot process Boot a SPARC-based system Boot an x86-based system Shut down a system Administering Services by Using SMF Describing SMF and its components Administering SMF Services Administering Software Packages by Using IPS and Managing Boot Environments Describing IPS, its components, and interfaces Configuring an IPS Client to Access the Local IPS Repository Managing Package Publishers Managing Software Packages Managing Signed Packages and Package Properties Describe the use of IPS in packaging the Oracle Solaris OS Manage boot environments Updating the OS by using IPS Managing Data by Using ZFS Introducing ZFS Administering ZFS Storage Pools Administering ZFS File Systems Administering ZFS Properties Administering ZFS Snapshots and Clones Administering the Network Reviewing Networking Fundamentals Administering Datalink Configuration Administering a Network Interface Administering Profile-Based Network Configuration Configuring a Virtual Network Verifying the Network Operations Managing Resources on the Virtual Network Administering Oracle Solaris Zones Introducing Oracle Solaris Zones Configuring an Oracle Solaris Zone Determining an Oracle Solaris Zone Configuration Controlling Access to Systems and Files Controlling Access to Systems Controlling Access to Files Securing Access to Remote Host Administering User Accounts Getting Started with the User Administration Setting Up User Accounts Maintaining User Accounts Configuring User Disk Quotas Managing System Processes and System Tasks Managing System Processes Scheduling System Administration Tasks

Duration 1 Days 6 CPD hours This course is intended for This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines. Completed either AWS Technical Essentials or Architecting on AWS Completed Building Data Lakes on AWS 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 data warehouse 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 build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake 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 Redshift. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Using Amazon Redshift in the Data Analytics Pipeline Why Amazon Redshift for data warehousing? Overview of Amazon Redshift Module 2: Introduction to Amazon Redshift Amazon Redshift architecture Interactive Demo 1: Touring the Amazon Redshift console Amazon Redshift features Practice Lab 1: Load and query data in an Amazon Redshift cluster Module 3: Ingestion and Storage Ingestion Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API Data distribution and storage Interactive Demo 3: Analyzing semi-structured data using the SUPER data type Querying data in Amazon Redshift Practice Lab 2: Data analytics using Amazon Redshift Spectrum Module 4: Processing and Optimizing Data Data transformation Advanced querying Practice Lab 3: Data transformation and querying in Amazon Redshift Resource management Interactive Demo 4: Applying mixed workload management on Amazon Redshift Automation and optimization Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster Module 5: Security and Monitoring of Amazon Redshift Clusters Securing the Amazon Redshift cluster Monitoring and troubleshooting Amazon Redshift clusters Module 6: Designing Data Warehouse Analytics Solutions Data warehouse use case review Activity: Designing a data warehouse analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures