52 Big Data courses delivered Live Online

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 4 Days 24 CPD hours This course is intended for This course is best suited to developers, engineers, and architects who want to use use Hadoop and related tools to solve real-world problems. Overview Skills learned in this course include:Creating a data set with Kite SDKDeveloping custom Flume components for data ingestionManaging a multi-stage workflow with OozieAnalyzing data with CrunchWriting user-defined functions for Hive and ImpalaWriting user-defined functions for Hive and ImpalaIndexing data with Cloudera Search Cloudera University?s four-day course for designing and building Big Data applications prepares you to analyze and solve real-world problems using Apache Hadoop and associated tools in the enterprise data hub (EDH). INTRODUCTION APPLICATION ARCHITECTURE * Scenario Explanation * Understanding the Development Environment * Identifying and Collecting Input Data * Selecting Tools for Data Processing and Analysis * Presenting Results to the Use DEFINING & USING DATASETS * Metadata Management * What is Apache Avro? * Avro Schemas * Avro Schema Evolution * Selecting a File Format * Performance Considerations USING THE KITE SDK DATA MODULE * What is the Kite SDK? * Fundamental Data Module Concepts * Creating New Data Sets Using the Kite SDK * Loading, Accessing, and Deleting a Data Set IMPORTING RELATIONAL DATA WITH APACHE SQOOP * What is Apache Sqoop? * Basic Imports * Limiting Results * Improving Sqoop?s Performance * Sqoop 2 CAPTURING DATA WITH APACHE FLUME * What is Apache Flume? * Basic Flume Architecture * Flume Sources * Flume Sinks * Flume Configuration * Logging Application Events to Hadoop DEVELOPING CUSTOM FLUME COMPONENTS * Flume Data Flow and Common Extension Points * Custom Flume Sources * Developing a Flume Pollable Source * Developing a Flume Event-Driven Source * Custom Flume Interceptors * Developing a Header-Modifying Flume Interceptor * Developing a Filtering Flume Interceptor * Writing Avro Objects with a Custom Flume Interceptor MANAGING WORKFLOWS WITH APACHE OOZIE * The Need for Workflow Management * What is Apache Oozie? * Defining an Oozie Workflow * Validation, Packaging, and Deployment * Running and Tracking Workflows Using the CLI * Hue UI for Oozie PROCESSING DATA PIPELINES WITH APACHE CRUNCH * What is Apache Crunch? * Understanding the Crunch Pipeline * Comparing Crunch to Java MapReduce * Working with Crunch Projects * Reading and Writing Data in Crunch * Data Collection API Functions * Utility Classes in the Crunch API WORKING WITH TABLES IN APACHE HIVE * What is Apache Hive? * Accessing Hive * Basic Query Syntax * Creating and Populating Hive Tables * How Hive Reads Data * Using the RegexSerDe in Hive DEVELOPING USER-DEFINED FUNCTIONS * What are User-Defined Functions? * Implementing a User-Defined Function * Deploying Custom Libraries in Hive * Registering a User-Defined Function in Hive EXECUTING INTERACTIVE QUERIES WITH IMPALA * What is Impala? * Comparing Hive to Impala * Running Queries in Impala * Support for User-Defined Functions * Data and Metadata Management UNDERSTANDING CLOUDERA SEARCH * What is Cloudera Search? * Search Architecture * Supported Document Formats INDEXING DATA WITH CLOUDERA SEARCH * Collection and Schema Management * Morphlines * Indexing Data in Batch Mode * Indexing Data in Near Real Time PRESENTING RESULTS TO USERS * Solr Query Syntax * Building a Search UI with Hue * Accessing Impala through JDBC * Powering a Custom Web Application with Impala and Search

Duration 3 Days 18 CPD hours This course is intended for This course is intended for Solution Architects Overview At the end of this course, you will be able to: Apply the AWS Well-Architected Framework Manage multiple AWS accounts for your organization Connect an on-premises datacenter to AWS cloud Move large data from an on-premises datacenter to AWS Design large datastores for AWS cloud Understand different architectural designs for scalability Protect your infrastructure from DDoS attack Secure your data on AWS with encryption Enhance the performance of your solutions Select the most appropriate AWS deployment mechanism Building on concepts introduced in Architecting on AWS, Advanced Architecting on AWS is intended for individuals who are experienced with designing scalable and elastic applications on the AWS platform. Building on concepts introduced in Architecting on AWS, this course covers how to build complex solutions which incorporate data services, governance, and security on AWS. This course introduces specialized AWS services, including AWS Direct Connect and AWS Storage Gateway to support Hybrid architecture. It also covers designing best practices for building scalable, elastic, secure, and highly available applications on AWS. MODULE 1: AWS ACCOUNT MANAGEMENT * Multiple accounts * Multi-account patterns * License management * Manage security and costs with multiple accounts * AWS Organizations * AWS Directory Service * Hands-on lab: Multi-VPC connectivity using a VPN MODULE 2: ADVANCED NETWORK ARCHITECTURES * Improve VPC network connections * Enhance performance for HPC workloads * VPN connections over AWS * AWS Direct Connect * AWS Transit Gateway * Amazon Route 53 * Exercise: Design a hybrid architecture MODULE 3: DEPLOYMENT MANAGEMENT ON AWS * Application lifecycle management * Application deployment using containers * AWS Elastic Beanstalk * AWS OpsWorks * AWS CloudFormation MODULE 4: DATA * Optimize Amazon S3 storage * Amazon ElastiCache * AWS Snowball * AWS Storage Gateway * AWS DataSync * Backup and archival considerations * Database migration * Designing for big data with Amazon DynamoDB * Hands-on lab: Build a failover solution with Amazon Route 53 and Amazon RDS MODULE 5: DESIGNING FOR LARGE SCALE APPLICATIONS * AWS Auto Scaling * Migrating over-provisioned resources * Blue-green deployments on AWS * Hands-on lab: Blue-green deployment with AWS MODULE 6: BUILDING RESILIENT ARCHITECTURES * DDoS attack overview * AWS Shield * AWS WAF * Amazon GuardDuty * High availability using Microsoft SQL Server and Microsoft SharePoint on AWS * High availability using MongoDB on Amazon EC2 * AWS Global Accelerator * Hands-on lab: CloudFront content delivery and automating AWS WAF rules MODULE 7: ENCRYPTION AND DATA SECURITY * Encryption primer * DIY key management in AWS * AWS Marketplace for encryption products * AWS Key Management Service (AWS KMS) * Cloud Hardware Security Module (HSM) * Comparison of key management options * Hands-on lab: AWS KMS with envelope encryption

Duration 1 Days 6 CPD hours This course is intended for This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists. Overview This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform. This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. INTRODUCING GOOGLE CLOUD PLATFORM * Google Platform Fundamentals Overview. * Google Cloud Platform Big Data Products. * COMPUTE AND STORAGE FUNDAMENTALS * CPUs on demand (Compute Engine). * A global filesystem (Cloud Storage). * CloudShell. * Lab: Set up a Ingest-Transform-Publish data processing pipeline. * DATA ANALYTICS ON THE CLOUD * Stepping-stones to the cloud. * Cloud SQL: your SQL database on the cloud. * Lab: Importing data into CloudSQL and running queries. * Spark on Dataproc. * Lab: Machine Learning Recommendations with Spark on Dataproc. * SCALING DATA ANALYSIS * Fast random access. * Datalab. * BigQuery. * Lab: Build machine learning dataset. * MACHINE LEARNING * Machine Learning with TensorFlow. * Lab: Carry out ML with TensorFlow * Pre-built models for common needs. * Lab: Employ ML APIs. * DATA PROCESSING ARCHITECTURES * Message-oriented architectures with Pub/Sub. * Creating pipelines with Dataflow. * Reference architecture for real-time and batch data processing. * SUMMARY * Why GCP? * Where to go from here * Additional Resources ADDITIONAL COURSE DETAILS: Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals 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 Google Cloud Platform Big Data and Machine Learning Fundamentals 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 2 Days 12 CPD hours Overview Identify and configure basic functions of Tableau. Connect to data sources, import data into Tableau, and save Tableau files Create views and customize data in visualizations. Manage, sort, and group data. Save and share data sources and workbooks. Filter data in views. Customize visualizations with annotations, highlights, and advanced features. Create and enhance dashboards in Tableau. Create and enhance stories in Tableau As technology progresses and becomes more interwoven with our businesses and lives, more and more data is collected about business and personal activities. This era of "big data" has exploded due to the rise of cloud computing, which provides an abundance of computational power and storage, allowing organizations of all sorts to capture and store data. Leveraging that data effectively can provide timely insights and competitive advantage. The creation of data-backed visualizations is a key way data scientists, or any professional, can explore, analyze, and report insights and trends from data. Tableau© software is designed for this purpose. Tableau was built to connect to a wide range of data sources and allows users to quickly create visualizations of connected data to gain insights, show trends, and create reports. Tableau's data connection capabilities and visualization features go far beyond those that can be found in spreadsheets, allowing users to create compelling and interactive worksheets, dashboards, and stories that bring data to life and turn data into thoughtful action. Prerequisites To ensure your success in this course, you should have experience managing data with Microsoft© Excel© or Google Sheets?. LESSON 1: TABLEAU FUNDAMENTALS * Topic A: Overview of Tableau * Topic B: Navigate and Configure Tableau LESSON 2: CONNECTING TO AND PREPARING DATA * Topic A: Connect to Data * Topic B: Build a Data Model * Topic C: Save Workbook Files * Topic D: Prepare Data for Analysis LESSON 3: EXPLORING DATA * Topic A: Create Views * Topic B: Customize Data in Visualizations LESSON 4: MANAGING, SORTING, AND GROUPING DATA * Topic A: Adjust Fields * Topic B: Sort Data * Topic C: Group Data LESSON 5: SAVING, PUBLISHING, AND SHARING DATA * Topic A: Save Data Sources * Topic B: Publish Data Sources and Visualizations * Topic C: Share Workbooks for Collaboration LESSON 6: FILTERING DATA * Topic A: Configure Worksheet Filters * Topic B: Apply Advanced Filter Options * Topic C: Create Interactive Filters LESSON 7: CUSTOMIZING VISUALIZATIONS * Topic A: Format and Annotate Views * Topic B: Emphasize Data in Visualizations * Topic C: Create Animated Workbooks * Topic D: Best Practices for Visual Design LESSON 8: CREATING DASHBOARDS IN TABLEAU * Topic A: Create Dashboards * Topic B: Enhance Dashboards with Actions * Topic C: Create Mobile Dashboards LESSON 9: CREATING STORIES IN TABLEAU * Topic A: Create Stories * Topic B: Enhance Stories with Tooltips

Duration 1 Days 6 CPD hours This course is intended for The audience for this course is individuals who want to learn the fundamentals of database concepts in a cloud environment, get basic skilling in cloud data services, and build their foundational knowledge of cloud data services within Microsoft Azure. Overview Describe core data concepts Identify considerations for relational data on Azure Describe considerations for working with non-relational data on Azure Describe an analytics workload on Azure In this course, students will gain foundational knowledge of core data concepts and related Microsoft Azure data services. Students will learn about core data concepts such as relational, non-relational, big data, and analytics, and build their foundational knowledge of cloud data services within Microsoft Azure. Students will explore fundamental relational data concepts and relational database services in Azure. They will explore Azure storage for non-relational data and the fundamentals of Azure Cosmos DB. Students will learn about large-scale data warehousing, real-time analytics, and data visualization. 1 - EXPLORE CORE DATA CONCEPTS * Identify data formats * Explore file storage * Explore databases * Explore transactional data processing * Explore analytical data processing 2 - EXPLORE DATA ROLES AND SERVICES * Explore job roles in the world of data * Identify data services 3 - EXPLORE FUNDAMENTAL RELATIONAL DATA CONCEPTS * Understand relational data * Understand normalization * Explore SQL * Describe database objects 4 - EXPLORE RELATIONAL DATABASE SERVICES IN AZURE * Describe Azure SQL services and capabilities * Describe Azure services for open-source databases 5 - EXPLORE AZURE STORAGE FOR NON-RELATIONAL DATA * Explore Azure blob storage * Explore Azure DataLake Storage Gen2 * Explore Azure Files * Explore Azure Tables 6 - EXPLORE FUNDAMENTALS OF AZURE COSMOS DB * Describe Azure Cosmos DB * Identify Azure Cosmos DB APIs 7 - EXPLORE FUNDAMENTALS OF LARGE-SCALE DATA WAREHOUSING * Describe data warehousing architecture * Explore data ingestion pipelines * Explore analytical data stores 8 - EXPLORE FUNDAMENTALS OF REAL-TIME ANALYTICS * Understand batch and stream processing * Explore common elements of stream processing architecture * Explore Azure Stream Analytics * Explore Apache Spark on Microsoft Azure 9 - EXPLORE FUNDAMENTALS OF DATA VISUALIZATION * Describe Power BI tools and workflow * Describe core concepts of data modeling * Describe considerations for data visualization

Duration 4 Days 24 CPD hours This course is intended for The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure. In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage. Prerequisites Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions. AZ-900T00 Microsoft Azure Fundamentals DP-900T00 Microsoft Azure Data Fundamentals 1 - INTRODUCTION TO DATA ENGINEERING ON AZURE * What is data engineering * Important data engineering concepts * Data engineering in Microsoft Azure 2 - INTRODUCTION TO AZURE DATA LAKE STORAGE GEN2 * Understand Azure Data Lake Storage Gen2 * Enable Azure Data Lake Storage Gen2 in Azure Storage * Compare Azure Data Lake Store to Azure Blob storage * Understand the stages for processing big data * Use Azure Data Lake Storage Gen2 in data analytics workloads 3 - INTRODUCTION TO AZURE SYNAPSE ANALYTICS * What is Azure Synapse Analytics * How Azure Synapse Analytics works * When to use Azure Synapse Analytics 4 - USE AZURE SYNAPSE SERVERLESS SQL POOL TO QUERY FILES IN A DATA LAKE * Understand Azure Synapse serverless SQL pool capabilities and use cases * Query files using a serverless SQL pool * Create external database objects 5 - USE AZURE SYNAPSE SERVERLESS SQL POOLS TO TRANSFORM DATA IN A DATA LAKE * Transform data files with the CREATE EXTERNAL TABLE AS SELECT statement * Encapsulate data transformations in a stored procedure * Include a data transformation stored procedure in a pipeline 6 - CREATE A LAKE DATABASE IN AZURE SYNAPSE ANALYTICS * Understand lake database concepts * Explore database templates * Create a lake database * Use a lake database 7 - ANALYZE DATA WITH APACHE SPARK IN AZURE SYNAPSE ANALYTICS * Get to know Apache Spark * Use Spark in Azure Synapse Analytics * Analyze data with Spark * Visualize data with Spark 8 - TRANSFORM DATA WITH SPARK IN AZURE SYNAPSE ANALYTICS * Modify and save dataframes * Partition data files * Transform data with SQL 9 - USE DELTA LAKE IN AZURE SYNAPSE ANALYTICS * Understand Delta Lake * Create Delta Lake tables * Create catalog tables * Use Delta Lake with streaming data * Use Delta Lake in a SQL pool 10 - ANALYZE DATA IN A RELATIONAL DATA WAREHOUSE * Design a data warehouse schema * Create data warehouse tables * Load data warehouse tables * Query a data warehouse 11 - LOAD DATA INTO A RELATIONAL DATA WAREHOUSE * Load staging tables * Load dimension tables * Load time dimension tables * Load slowly changing dimensions * Load fact tables * Perform post load optimization 12 - BUILD A DATA PIPELINE IN AZURE SYNAPSE ANALYTICS * Understand pipelines in Azure Synapse Analytics * Create a pipeline in Azure Synapse Studio * Define data flows * Run a pipeline 13 - USE SPARK NOTEBOOKS IN AN AZURE SYNAPSE PIPELINE * Understand Synapse Notebooks and Pipelines * Use a Synapse notebook activity in a pipeline * Use parameters in a notebook 14 - PLAN HYBRID TRANSACTIONAL AND ANALYTICAL PROCESSING USING AZURE SYNAPSE ANALYTICS * Understand hybrid transactional and analytical processing patterns * Describe Azure Synapse Link 15 - IMPLEMENT AZURE SYNAPSE LINK WITH AZURE COSMOS DB * Enable Cosmos DB account to use Azure Synapse Link * Create an analytical store enabled container * Create a linked service for Cosmos DB * Query Cosmos DB data with Spark * Query Cosmos DB with Synapse SQL 16 - IMPLEMENT AZURE SYNAPSE LINK FOR SQL * What is Azure Synapse Link for SQL? * Configure Azure Synapse Link for Azure SQL Database * Configure Azure Synapse Link for SQL Server 2022 17 - GET STARTED WITH AZURE STREAM ANALYTICS * Understand data streams * Understand event processing * Understand window functions 18 - INGEST STREAMING DATA USING AZURE STREAM ANALYTICS AND AZURE SYNAPSE ANALYTICS * Stream ingestion scenarios * Configure inputs and outputs * Define a query to select, filter, and aggregate data * Run a job to ingest data 19 - VISUALIZE REAL-TIME DATA WITH AZURE STREAM ANALYTICS AND POWER BI * Use a Power BI output in Azure Stream Analytics * Create a query for real-time visualization * Create real-time data visualizations in Power BI 20 - INTRODUCTION TO MICROSOFT PURVIEW * What is Microsoft Purview? * How Microsoft Purview works * When to use Microsoft Purview 21 - INTEGRATE MICROSOFT PURVIEW AND AZURE SYNAPSE ANALYTICS * Catalog Azure Synapse Analytics data assets in Microsoft Purview * Connect Microsoft Purview to an Azure Synapse Analytics workspace * Search a Purview catalog in Synapse Studio * Track data lineage in pipelines 22 - EXPLORE AZURE DATABRICKS * Get started with Azure Databricks * Identify Azure Databricks workloads * Understand key concepts 23 - USE APACHE SPARK IN AZURE DATABRICKS * Get to know Spark * Create a Spark cluster * Use Spark in notebooks * Use Spark to work with data files * Visualize data 24 - RUN AZURE DATABRICKS NOTEBOOKS WITH AZURE DATA FACTORY * Understand Azure Databricks notebooks and pipelines * Create a linked service for Azure Databricks * Use a Notebook activity in a pipeline * Use parameters in a notebook ADDITIONAL COURSE DETAILS: Nexus Humans DP-203T00 Data Engineering on Microsoft Azure 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 DP-203T00 Data Engineering on Microsoft Azure 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 2 Days 12 CPD hours This course is intended for This course is designed for professionals in a variety of job roles who are currently using Tableau to perform numerical or general data analysis, visualization, and reporting. They need to provide data visualizations from multiple data sources, or combine data to show comparisons, manipulate data through calculations, create interactive visualizations, or create visualizations that showcase insights from statistical analysis. This course is also designed for students who plan to obtain Tableau Desktop Certified Associate certification, which requires candidates to pass the Tableau Desktop Certified Associate exam. Overview Blend data multiple sources. Join data. Access data in PDFs. Refine visualizations with sets and parameters. Analyze data with calculations. Visualize data with advanced calculations. Perform statistical analysis and forecasting. Create geographic visualizations. Get answers with Ask and Explain The advent of cloud computing and storage has ushered in the era of "big data." With the abundance of computational power and storage, organizations and employees with many different roles and responsibilities can benefit from analyzing data to find timely insights and gain competitive advantage. Data-backed visualizations allow anyone to explore, analyze, and report insights and trends from data. Tableau© software is designed for this purpose. Tableau was built to connect to a wide range of data sources and allows users to quickly create visualizations of connected data to gain insights, show trends, and create reports. Beyond the fundamental capabilities of creating data driven visualizations, Tableau allows users to manipulate data with calculations to show insights, make visualizations interactive, and perform statistical analysis. This gives users the ability to create and share data driven insights with peers, executives, and clients. Prerequisites Tableau Desktop: Part 1 LESSON 1: BLENDING DATA FROM MULTIPLE SOURCES * Topic A: Blend Data * Topic B: Refine Blends to Visualize Key Information LESSON 2: JOINING DATA * Topic A: Create Joins * Topic B: Troubleshoot Joins * Topic C: Merge Data with Unions LESSON 3: ACCESSING DATA IN PDFS * Topic A: Connect to PDFs * Topic B: Clean Up and Organize PDF Data LESSON 4: REFINING VISUALIZATIONS WITH SETS AND PARAMETERS * Topic A: Create Sets * Topic B: Analyze Data with Sets * Topic C: Apply Parameters to Refine Visualizations * Topic D: Create Advanced Visualizations LESSON 5: ANALYZING DATA WITH CALCULATIONS * Topic A: Create Calculated Fields to Analyze Data * Topic B: Manipulate Data with Functions * Topic C: Analyze Data with Table Calculations LESSON 6: VISUALIZING DATA WITH ADVANCED CALCULATIONS * Topic A: Create Groups and Bins with Calculations * Topic B: Analyze Data with LOD Expressions LESSON 7: PERFORMING STATISTICAL ANALYSIS AND FORECASTING * Topic A: Perform Statistical Analysis * Topic B: Forecast Data Trends LESSON 8: CREATING GEOGRAPHIC VISUALIZATIONS * Topic A: Create Maps * Topic B: Customize Mapped Data LESSON 9: GETTING ANSWERS WITH ASK AND EXPLAIN * Topic A: Ask Data * Topic B: Explain Data ADDITIONAL COURSE DETAILS: Nexus Humans Tableau Desktop - Part 2 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 Tableau Desktop - Part 2 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 4 Days 24 CPD hours This course is intended for This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Overview Skills gained in this training include:The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysisThe fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with HadoopHow Pig, Hive, and Impala improve productivity for typical analysis tasksJoining diverse datasets to gain valuable business insightPerforming real-time, complex queries on datasets Cloudera University?s four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data. HADOOP FUNDAMENTALS * The Motivation for Hadoop * Hadoop Overview * Data Storage: HDFS * Distributed Data Processing: YARN, MapReduce, and Spark * Data Processing and Analysis: Pig, Hive, and Impala * Data Integration: Sqoop * Other Hadoop Data Tools * Exercise Scenarios Explanation INTRODUCTION TO PIG * What Is Pig? * Pig?s Features * Pig Use Cases * Interacting with Pig BASIC DATA ANALYSIS WITH PIG * Pig Latin Syntax * Loading Data * Simple Data Types * Field Definitions * Data Output * Viewing the Schema * Filtering and Sorting Data * Commonly-Used Functions PROCESSING COMPLEX DATA WITH PIG * Storage Formats * Complex/Nested Data Types * Grouping * Built-In Functions for Complex Data * Iterating Grouped Data MULTI-DATASET OPERATIONS WITH PIG * Techniques for Combining Data Sets * Joining Data Sets in Pig * Set Operations * Splitting Data Sets PIG TROUBLESHOOT & OPTIMIZATION * Troubleshooting Pig * Logging * Using Hadoop?s Web UI * Data Sampling and Debugging * Performance Overview * Understanding the Execution Plan * Tips for Improving the Performance of Your Pig Jobs INTRODUCTION TO HIVE & IMPALA * What Is Hive? * What Is Impala? * Schema and Data Storage * Comparing Hive to Traditional Databases * Hive Use Cases QUERYING WITH HIVE & IMPALA * Databases and Tables * Basic Hive and Impala Query Language Syntax * Data Types * Differences Between Hive and Impala Query Syntax * Using Hue to Execute Queries * Using the Impala Shell DATA MANAGEMENT * Data Storage * Creating Databases and Tables * Loading Data * Altering Databases and Tables * Simplifying Queries with Views * Storing Query Results DATA STORAGE & PERFORMANCE * Partitioning Tables * Choosing a File Format * Managing Metadata * Controlling Access to Data RELATIONAL DATA ANALYSIS WITH HIVE & IMPALA * Joining Datasets * Common Built-In Functions * Aggregation and Windowing WORKING WITH IMPALA * How Impala Executes Queries * Extending Impala with User-Defined Functions * Improving Impala Performance ANALYZING TEXT AND COMPLEX DATA WITH HIVE * Complex Values in Hive * Using Regular Expressions in Hive * Sentiment Analysis and N-Grams * Conclusion HIVE OPTIMIZATION * Understanding Query Performance * Controlling Job Execution Plan * Bucketing * Indexing Data EXTENDING HIVE * SerDes * Data Transformation with Custom Scripts * User-Defined Functions * Parameterized Queries CHOOSING THE BEST TOOL FOR THE JOB * Comparing MapReduce, Pig, Hive, Impala, and Relational Databases * Which to Choose?

INTERNET OF THINGS TRAINING COURSE DESCRIPTION A concise overview course covering The Internet of Things and the technologies involved. Particular emphasis is placed on the high level architecture of IoT and the benefits achievable. WHAT WILL YOU LEARN * Describe the structure of the IoT * List the technologies involved in IoT. * Explain how IoT works. INTERNET OF THINGS TRAINING COURSE DETAILS * Who will benefit: Non-technical staff working with IoT. * Prerequisites: None. * Duration 1 day INTERNET OF THINGS TRAINING COURSE CONTENTS * What is IoT The Internet, what is IoT? IoT and M2M, IoT technologies, IoT architecture. Wired and wireless communication. IoT applications; Smart houses, smart cities, smart cars, wearable, environment, other domain specific IoTs. * IoT architecture Physical objects, virtual objects, cloud computing, data capture, communications. Big data. * Components Hardware, sensors, actuators, chips, firmware, embedded systems. Open source platforms. Power options: Battery, solar, PoE. * IoT communication RF, ZigBee, Bluetooth, Bluetooth LE, RFID, WiFi, 802.11ah, mobile technologies. Wired. * Arduino (as an example) Microcontrollers, the platform, development, Arduino software, reading from sensors, I2C, SPI. Arduino and the Internet, HTTP, WiFi, GSM. The cloud and IoT: Pachube, nimbits, ThingSpeak * Security in IoT Authentication, Encryption, secure booting, firewalls.