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Overview This comprehensive course on SQL NoSQL Big Data and Hadoop will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This SQL NoSQL Big Data and Hadoop comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? At the end of the course there will be an online written test, which you can take either during or after the course. After successfully completing the test you will be able to order your certificate, these are included in the price. Who is This course for? There is no experience or previous qualifications required for enrolment on this SQL NoSQL Big Data and Hadoop. It is available to all students, of all academic backgrounds. Requirements Our SQL NoSQL Big Data and Hadoop is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 14 sections • 130 lectures • 22:34:00 total length •Introduction: 00:07:00 •Building a Data-driven Organization - Introduction: 00:04:00 •Data Engineering: 00:06:00 •Learning Environment & Course Material: 00:04:00 •Movielens Dataset: 00:03:00 •Introduction to Relational Databases: 00:09:00 •SQL: 00:05:00 •Movielens Relational Model: 00:15:00 •Movielens Relational Model: Normalization vs Denormalization: 00:16:00 •MySQL: 00:05:00 •Movielens in MySQL: Database import: 00:06:00 •OLTP in RDBMS: CRUD Applications: 00:17:00 •Indexes: 00:16:00 •Data Warehousing: 00:15:00 •Analytical Processing: 00:17:00 •Transaction Logs: 00:06:00 •Relational Databases - Wrap Up: 00:03:00 •Distributed Databases: 00:07:00 •CAP Theorem: 00:10:00 •BASE: 00:07:00 •Other Classifications: 00:07:00 •Introduction to KV Stores: 00:02:00 •Redis: 00:04:00 •Install Redis: 00:07:00 •Time Complexity of Algorithm: 00:05:00 •Data Structures in Redis : Key & String: 00:20:00 •Data Structures in Redis II : Hash & List: 00:18:00 •Data structures in Redis III : Set & Sorted Set: 00:21:00 •Data structures in Redis IV : Geo & HyperLogLog: 00:11:00 •Data structures in Redis V : Pubsub & Transaction: 00:08:00 •Modelling Movielens in Redis: 00:11:00 •Redis Example in Application: 00:29:00 •KV Stores: Wrap Up: 00:02:00 •Introduction to Document-Oriented Databases: 00:05:00 •MongoDB: 00:04:00 •MongoDB Installation: 00:02:00 •Movielens in MongoDB: 00:13:00 •Movielens in MongoDB: Normalization vs Denormalization: 00:11:00 •Movielens in MongoDB: Implementation: 00:10:00 •CRUD Operations in MongoDB: 00:13:00 •Indexes: 00:16:00 •MongoDB Aggregation Query - MapReduce function: 00:09:00 •MongoDB Aggregation Query - Aggregation Framework: 00:16:00 •Demo: MySQL vs MongoDB. Modeling with Spark: 00:02:00 •Document Stores: Wrap Up: 00:03:00 •Introduction to Search Engine Stores: 00:05:00 •Elasticsearch: 00:09:00 •Basic Terms Concepts and Description: 00:13:00 •Movielens in Elastisearch: 00:12:00 •CRUD in Elasticsearch: 00:15:00 •Search Queries in Elasticsearch: 00:23:00 •Aggregation Queries in Elasticsearch: 00:23:00 •The Elastic Stack (ELK): 00:12:00 •Use case: UFO Sighting in ElasticSearch: 00:29:00 •Search Engines: Wrap Up: 00:04:00 •Introduction to Columnar databases: 00:06:00 •HBase: 00:07:00 •HBase Architecture: 00:09:00 •HBase Installation: 00:09:00 •Apache Zookeeper: 00:06:00 •Movielens Data in HBase: 00:17:00 •Performing CRUD in HBase: 00:24:00 •SQL on HBase - Apache Phoenix: 00:14:00 •SQL on HBase - Apache Phoenix - Movielens: 00:10:00 •Demo : GeoLife GPS Trajectories: 00:02:00 •Wide Column Store: Wrap Up: 00:05:00 •Introduction to Time Series: 00:09:00 •InfluxDB: 00:03:00 •InfluxDB Installation: 00:07:00 •InfluxDB Data Model: 00:07:00 •Data manipulation in InfluxDB: 00:17:00 •TICK Stack I: 00:12:00 •TICK Stack II: 00:23:00 •Time Series Databases: Wrap Up: 00:04:00 •Introduction to Graph Databases: 00:05:00 •Modelling in Graph: 00:14:00 •Modelling Movielens as a Graph: 00:10:00 •Neo4J: 00:04:00 •Neo4J installation: 00:08:00 •Cypher: 00:12:00 •Cypher II: 00:19:00 •Movielens in Neo4J: Data Import: 00:17:00 •Movielens in Neo4J: Spring Application: 00:12:00 •Data Analysis in Graph Databases: 00:05:00 •Examples of Graph Algorithms in Neo4J: 00:18:00 •Graph Databases: Wrap Up: 00:07:00 •Introduction to Big Data With Apache Hadoop: 00:06:00 •Big Data Storage in Hadoop (HDFS): 00:16:00 •Big Data Processing : YARN: 00:11:00 •Installation: 00:13:00 •Data Processing in Hadoop (MapReduce): 00:14:00 •Examples in MapReduce: 00:25:00 •Data Processing in Hadoop (Pig): 00:12:00 •Examples in Pig: 00:21:00 •Data Processing in Hadoop (Spark): 00:23:00 •Examples in Spark: 00:23:00 •Data Analytics with Apache Spark: 00:09:00 •Data Compression: 00:06:00 •Data serialization and storage formats: 00:20:00 •Hadoop: Wrap Up: 00:07:00 •Introduction Big Data SQL Engines: 00:03:00 •Apache Hive: 00:10:00 •Apache Hive : Demonstration: 00:20:00 •MPP SQL-on-Hadoop: Introduction: 00:03:00 •Impala: 00:06:00 •Impala : Demonstration: 00:18:00 •PrestoDB: 00:13:00 •PrestoDB : Demonstration: 00:14:00 •SQL-on-Hadoop: Wrap Up: 00:02:00 •Data Architectures: 00:05:00 •Introduction to Distributed Commit Logs: 00:07:00 •Apache Kafka: 00:03:00 •Confluent Platform Installation: 00:10:00 •Data Modeling in Kafka I: 00:13:00 •Data Modeling in Kafka II: 00:15:00 •Data Generation for Testing: 00:09:00 •Use case: Toll fee Collection: 00:04:00 •Stream processing: 00:11:00 •Stream Processing II with Stream + Connect APIs: 00:19:00 •Example: Kafka Streams: 00:15:00 •KSQL : Streaming Processing in SQL: 00:04:00 •KSQL: Example: 00:14:00 •Demonstration: NYC Taxi and Fares: 00:01:00 •Streaming: Wrap Up: 00:02:00 •Database Polyglot: 00:04:00 •Extending your knowledge: 00:08:00 •Data Visualization: 00:11:00 •Building a Data-driven Organization - Conclusion: 00:07:00 •Conclusion: 00:03:00 •Assignment -SQL NoSQL Big Data and Hadoop: 00:00:00

MySQL for developers training course description This MySQL Developers training course is designed for MySQL Developers who have a good understanding of a MySQL database and experience of using SQL commands. The course provides further practical experience in more advanced MySQL commands and SQL statements including Stored Routines, Triggers and Event Scheduling. What will you learn Provide the skills needed to write more advanced queries and database maintenance statements on a MySQL database. Use advanced features of the MySQL Client. Use advanced data types. Manage the structure of databases and tables. Manage and using indexes. Write complex SQL query statements. Use advanced SQL expressions. Use advanced SQL functions. Perform advanced Insert, Update, Delete, Replace and Truncate Operations. Use user variable syntax and properties. Import and export data from within MySQL. Import and export data from the command line. Perform complex joins to access multiple tables. Perform complex subqueries. Create, manage and us views. Use prepared statements. Create and use stored routines. Create and use triggers. Obtain database metadata. Optimize queries. Work with the main storage engines. Debug MySQL applications. MySQL for developers training course details Who will benefit: MySQL Developers who have a basic understanding of a MySQL database and SQL commands as covered on the Introduction to MySQL course. Prerequisites: MySQL foundation Duration 5 days MySQL for developers training course contents Introduction Administration and Course Materials, Course Structure and Agenda, Delegate and Trainer Introductions. Client/server concepts MySQL client/server architecture, Server modes, Using client programs, Logging in options, Configuration files, Precedence of logging in options Hands on Using client/server The MySQL client program Using MySQL interactively, The MySQL prompts, Client commands and SQL statements, Editing, Selecting a database, Help, Safe updates, Using script files, Using a source file, Redirecting output into a file, Command line execution, Mysql output formats, Overriding the defaults, Html and xml output, MySQL Utilities. Hands on Using the MySQL client program Data types Bit data type, Numeric data types, Auto_increment, Character string data types, Character sets and collation, Binary string data types, Enum and Set data types, Temporal data types, Timezone support, Handling Missing Or Invalid Data Values, SQL_MODE options. Hands on Using data types Identifiers Using Quotes with identifier naming, Case sensitivity in Identifier naming, Qualifying columns with table and database names, Using reserved words as identifiers, Function names Hands on Using identifiers Databases Database properties, Creating a database, Selecting a database, Altering databases, Dropping databases, Obtaining database metadata, The SHOW command, The INFORMATION_SCHEMA database, The SHOW CREATE command Hands on Using databases Tables and indexes Table properties, Creating tables, Create table using Select or Like, Temporary tables and memory tables, Altering tables, Adding columns, Changing column widths and types, Renaming columns, Dropping columns, Adding constraints, Dropping constraints, Renaming tables, Change the table storage engine, Multiple alterations, Dropping tables, Emptying tables, Obtaining table metadata, Show create table, The information_schema, Index introduction, Structure of a mysql index, Creating and dropping indexes, Creating an index, Altering a table to add an index, Specifying index type, Dropping indexes, Obtaining Index Metadata. Hands on Creating, altering and dropping tables/indexes Querying for data The SQL select statement and MySQL differences, Advanced order by, Order by and collation, Order by with enum datatype, Order by with Set datatype, Ordering with distinct and group by Special features of union, Limit and order by clauses, Group By clause, Group_concat, Using Rollup in a Group By clause. Hands on Querying for data SQL Expressions and functions Components of expressions, Nulls, Numeric expressions, String expressions, Temporal expressions, Comparison functions, Flow control functions, Numeric functions, String functions, Temporal functions. Hands on Using expressions and functions Updating data Update operations and privileges, Inserting rows, Insert using a set clause, Inserting duplicate values, Replacing rows, Updating rows, Update using the order by and limit clauses, Deleting rows, The delete and truncate statements. Exercise: Inserting, updating, replacing and deleting data Connectors MySQL client interfaces, MySQL connectors, Oracle and community conectors, Connecting to MySQL server using Java and PHP connectors, MySQL and NoSQL, Innodb integration with memcached. Obtaining database metadata What is metadata?, The mysqlshow utility, The show and describe commands, Describing tables, The information_schema, Listing tables, Listing columns, Listing views, Listing key_columns_usage. Hands on Obtaining database metadata Debugging Mysql error messages, The show statement, Show errors, Show count(*) errors, Show warnings, Show count(*) warnings, Note messages, The perror utility. Hands on Debugging Joins Overview of inner joins, Cartesian product, Inner joins with original syntax, Non equi-join, Using table aliases to avoid name clashes, Inner Joins With ISO/ANSI Syntax, Outer Joins, Left outer joins, Right outer joins, Full outer joins, Updating multiple tables simultaneously, Updating rows in one table based on a condition in another, Updating rows in one table reading data from another, Deleting from multiple tables simultaneously, Deleting rows in one table based on a condition in another. Hands on Coding joins Subqueries Types of subquery, Multiple-column subqueries, Correlated subqueries, Using the ANY, ALL and SOME operators, Using the EXISTS operator, Subqueries as scalar expressions, Inline views, Converting subqueries to joins, Using subqueries in updates and deletes. Hands on Coding subqueries Views Why views are used, Creating views, View creation restrictions, View algorithms, Updateable views, Altering and dropping views, Displaying information about views, Privileges for views. Hands on Using views Import and Export Exporting using SQL, Privileges required to export data, Importing using SQL, Messages when loading data, Privileges required to load data, Exporting from the command line, Mysqldump main options, Importing from the command line, Mysqlimport main options. Hands on Importing and exporting User variables and prepared statements Creating User variables, User variables in a select, Prepared statements, The prepare statement, The execute statement, The deallocate statement, Using prepared statements in code, with connectors. Hands on Using variables and prepared statements Introduction to stored routines Types of stored routines, Benefits of stored routines, Stored routine features, Differences between procedures and functions, Introduction to the Block, Declaring variables and constants, Assigning values to variables, Definer rights and invoker rights, Using SELECT in stored routines, Altering and dropping stored routines, Obtaining stored routine metadata, Stored routine privileges and execution security. Hands on Writing simple stored routines Stored routines - program logic The IF .. THEN .. ELSEIF construct, The CASE statement, The basic loop, The while loop, The repeat loop, The iterate statement, Nested loops. Hands on Writing stored routines with program logic Stored routines - exception handlers and cursors Dealing with errors using Exception handlers, Cursors, What is a cursor?, Cursor operations, Declaring cursors, Opening and closing cursors, Fetching rows, Status checking. Hands on Writing stored routines with program logic Procedures with parameters Creating procedures with parameters, Calling Procedures With Parameters. Hands on Writing stored routines with parameters Functions What is a function?, The create function statement, Executing functions, Executing functions from code, Executing functions from SQL statements, The deterministic and SQL clauses. Hands on Writing functions Triggers Trigger creation, Restrictions on triggers, The create trigger statement, Using the old and new qualifiers, Managing triggers, Destroying triggers, Required privileges. Hands on Writing triggers Basic optimizations Normalisation of data to third normal form, Using indexes for optimization, General query enhancement, Using Explain to analyze queries, Choosing an INNODB or MYISAM storage engine, Using MySQL Enterprise Monitor in query optimization. Hands on Making use of basic optimizations More about indexes Indexes and joins Hands on Investigating indexes and joins

ADO.NET training course description This ADO.net training course is designed to enable developers to use the toolset provided with.NET for data access including ADO.net objects, data controls, designers and interoperability with earlier ADO objects. The course is applicable for those using C# or VB.NET with ADO.NET What will you learn Retrieve and manipulate data using Microsoft's ADO.NET library. Work with the ADO.NET object model. Update data, including handling stored procedures, parameters, and return value. Search, sort and filter data. Leverage the power of XML. ADO.NET training course details Who will benefit: Programmers working with ADO.Net. Prerequisites: Effective programming with VB.NET or Concise introduction to C# Duration 2 days ADO.NET training course contents Introducing ADO.Net Traditional Data Access Architecture. ADO.Net Disconnected Data Access Architecture. Different components of ADO.Net. A review of basic SQL queries SQL SELECT Statement. SQL INSERT Statement. SQL UPDATE Statement. SQL DELETE Statement. Common data access tasks with ADO.Net Accessing Data using ADO.Net. Defining the connection string. Defining a Connection. Defining the command or command string. Defining the Data Adapter. Creating and filling the DataSet. A Demonstration Application The Interface. Loading the table. Filling the controls on the Form. Navigating through the records. Updating Data Steps for updating the table. Building the Application. Loading the table and displaying data in the form's controls. Initialising Commands. Adding Parameters to the commands. The ToggleControls() method of our application. Editing (or Updating) Records. Event Handler for the Save Button. Event Handler for the Cancel Button. Inserting Records. Deleting a Record. Using Stored Procedures Sample Stored Procedures. UPDATE Stored Procedure. INSERT Stored Procedure. DELETE Stored Procedure. SELECT Stored Procedure. Using Stored Procedures with ADO.Net.

Overview Uplift Your Career & Skill Up to Your Dream Job - Learning Simplified From Home! Kickstart your career & boost your employability by helping you discover your skills, talents and interests with our special Learn Python, JavaScript, and Microsoft SQL for Data science Course. You'll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides professional training that employers are looking for in today's workplaces. The Learn Python, JavaScript, and Microsoft SQL for Data science Course is one of the most prestigious training offered at StudyHub and is highly valued by employers for good reason. This Learn Python, JavaScript, and Microsoft SQL for Data science Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field. This Learn Python, JavaScript, and Microsoft SQL for Data science Course, like every one of Study Hub's courses, is meticulously developed and well researched. Every one of the topics is divided into elementary modules, allowing our students to grasp each lesson quickly. At StudyHub, we don't just offer courses; we also provide a valuable teaching process. When you buy a course from StudyHub, you get unlimited Lifetime access with 24/7 dedicated tutor support. Why buy this Learn Python, JavaScript, and Microsoft SQL for Data science? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Learn Python, JavaScript, and Microsoft SQL for Data science there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Learn Python, JavaScript, and Microsoft SQL for Data science course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill.   Prerequisites This Learn Python, JavaScript, and Microsoft SQL for Data science does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Learn Python, JavaScript, and Microsoft SQL for Data science was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Learn Python, JavaScript, and Microsoft SQL for Data science is a great way for you to gain multiple skills from the comfort of your home.

Level 5 QLS Endorsed Diploma | 150 CPD Points | +Gifts: QLS Certificate + PDF Certificate | 24/7 Learner Support

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.

This is a hands-on comprehensive course for beginners and in just two hours, you will learn the fundamentals of the Hadoop Ecosystem and its three main building blocks. This course will prepare you to start learning more about big data and to implement Hadoop components in Azure Cloud using HDInsight.

Duration 5 Days 30 CPD hours This course is intended for This intermediate-level course requires students have incoming experience working with Oracle Database 18 or higher. Overview Working in a hands-on learning environment led by our expert facilitator, you'll explore: The Oracle Database Architecture Query Optimizer Tuning Container Databases and Pluggable Databases Oracle 19c Tuning features Evaluating Execution Plans Oracle Tuning Tools Using Automatic Workload Repository Join Types AWR Using Baselines Additional AWR performance tools Optimizer Statistics Monitoring a Service Bind Variables and database parameters Oracle's Real Application Testing (RAT) SQL Tuning Advisor Automatic Sql Tuning Sql Plan Management Shared Pool Tuning Tuning the database buffer cache Tuning the PGA (Program Global Area) Automatic Memory Management (AMM) Tuning Segment Space Utilization (ASSM) Automatic Storage Management Oracle 19C Database Tuning is an intermediate level course for Oracle database experienced attendees that explores core tuning skills such as Database parameters, SQL Tuning Advisor, SQL Access Advisor, Adaptive SQL plans and more. Overview Oracle Database Architecture Instance Definition Define SGA Define Background Processes Datafile Definition Query Optimizer SQL Parsing Optimizing Terms Optimizing Methods Query Plan Generation Query Plan Control Tuning Container Databases and Pluggable Databases Pluggable tuning parameters Define Container tuning structure Using PDB$SEED Create a new PDB Plug and unplug a PDB Oracle 12c Tuning features Identifying and Using Oracle's Heat Map 12c Compression Levels and Types Evaluating Execution Plans Defining SQL execution plans Automatic Workload Repository Reading execution plans Oracle Tuning Tools Monitoring tools overview Enterprise Manager Dynamic Performance Views Automatic Workload Repository Automatic Database Diagnostic Monitor Sql Tuning Advisor SQL Access Advisor Sql Access Advisor DB operation Tuning DB operation Active Reporting Using Automatic Workload Repository Defining AWR AWR Settings Creating AWR Baselines Metrics, Alerts, and Thresholds Defining Metrics Setting Alerts Setting Corrective Actions User Defined Metrics Metric Dynamic Views Join Types Nested Loops Join Sort Merge join Hash Join and Cartesian Join Equijoins and Nonequijoins Outer Joins Semijoins AWR Using Baselines Creating AWR baselines Creating AWR Repeating baselines Moving Window Baseline Additional AWR performance tools Automatic Maintenance Tasks Segment Advisor Statistics Gathering Automatic Tuning Optimizer Automatic Database Diagnostic Monitor Active Session History (ASH) Optimizer Statistics Optimizer Statistics Overview Table and Index Statistics Statistic Preferences Statistics Gathering e) Locking Statistics, Export/Import Statistics Pending and published statistics Optimizer Hints Optimizer Paths Cost Base Optimization Monitoring a Service Overview of what is an Oracle Service Creating an Oracle Service for Single instance and RAC Monitoring a Service Resource Management and a Service Enterprise Manager and a Service Bind Variables and database parameters Bind variable definition Cursor_sharing parameter Adaptive Cursor Sharing Oracle's Real Application Testing (RAT) Sql Performance Analyzer overview Sql Performance Analyzer Options Database Parameter changes Database version changes Creating SQL Tuning Sets Database Replay Overview Database Replay Configuration Database Replay Options SQL Tuning Advisor SQL Tuning Advisor: Overview SQL Tuning Advisor Limited Mode Sql Tuning Advisor Comprehensive mode Sql Tuning Profiles SQL Access Advisor SQL Access Advisor: Overview Sql Access Advisor options SQL Access Advisor and Sql Tuning Sets Sql Access Advisor and AWR Results and Implementation Automatic Sql Tuning Automatic Sql Tuning Maintenance Task Automatic Tuning Optimization implementation(ATO) Automatic Tuning Optimization Results Enable/Disable Automatic Tuning Optimization Sql Plan Management Sql plan Management and baseline overview Enable sql plan management Loading Sql Plan baselines into the SGA Adaptive plan management Shared Pool Tuning Shared pool architecture Shared pool parameters Library Cache Dictionary cache Large pool considerations and contents Tuning the database buffer cache Database buffer cache overview Database buffer cache parameters Oracle and Dirty reads and writes Automatic Shared Memory Management (ASMM) Buffer Cache goals and responsibility Buffer Cache pools Tuning the PGA (Program Global Area) PGA Overview PGA Database Parameters Temporary Segments Temporary Tablespace Sizing the PGA Automatic Memory Management (AMM) Oracle's Automatic Memory Management Overview Database Auto-tuned Parameters Database Non Auto-tuned Parameters Automatic Memory Management Hints and Sizing suggestions AMM versus ASMM Tuning Segment Space Utilization (ASSM) Overview of Automatic Segment Space Management Defining the DB_BLOCK_SIZE Defining DB_nk_CACHE_SIZE parameter The DB_BLOCK_SIZE Parameter Overview of table compression, block chaining, and block migration Automatic Storage Management Overview of ASM Definition of Grid Infrastructure ASM Instance ASM Diskgroups ASM Diskgroup parameters and templates ASMCMD