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Senior SQL Developers are in high demand and demand 100k+ salary in the IT industry.
If we have to advance in our career and earn a good salary, we need these Advanced SQL skills. So let's take the next step of learning these advanced concepts of Oracle SQL.
Learning these advanced SQL concepts would position you better in your working environment.
What will we learn?
Students will learn the art of breaking a big SQL statement into small pieces and rebuild it again.
Create Materialized views to replicate data across servers and improve performance by using simple SQL syntax.
Partition the tables for better management and performance optimization using SQL Partitioning.
Students will learn to use Analytic SQL to aggregate, analyze and report, and model data using the SQL Analytic capabilities.
Students will learn to interpret the concept of a hierarchical query, create a tree-structured report, format hierarchical data, and exclude branches from the tree structure using SQL Hierarchical features.
Students will also learn to use regular expressions and subexpressions to search for, match, and replace strings using SQL built in functions.
Group and aggregate data using the built in SQL functions like ROLLUP and CUBE operators.
See you inside,
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Learn on the go.
Desktop, iOS and Android.
Certificate of completion.
Welcome to the course!!!Preview
How to make best use of the program.
|Section 1: Software Installation|
Installing Java SDK
Installing SQL Developer
Running scripts necessary for the course
Scripts used in the demonstration
|Section 2: Manipulating data|
Default values for columns
Arithmetic calculations on NULL Values
Multi table Insert's
Merge the data...
|Section 3: Analytical Functions|
Analytical Functions Introduction
Why Analytical Functions Example 1Preview
Why Analytical Functions Example 2
Getting the cummulative Sum of Sales
Displaying Sales as a percentage of Total sales
Ranking your data
Performing Top N AnalysisPreview
Dividing your data into Bands
LAG and LEAD function Examples
Analyzing Sales growth across time
Analytical Functions Recap
|Section 4: Transforming the Data|
Row level data to Column level using CASE statementPreview
Row level data to Column level using PIVOTPreview
Row level data to Column level using LISTAGG
Column level data to Row level using UNION
Column level data to Row level using UNPIVOT
Data Transformation Recap
|Section 5: Hierarchical Queries|
Hierarchical Queries Introduction
Connect By clause
Creating the Hierarchy TreePreview
Sorting the Hierarchy Tree
CONNECT_BY_ROOT unary operator
Get me the Sales under Manager Raj
CONNECT BY for number generation
Hierarchical Queries Recap
|Section 6: Extensions to Group BY|
Extensions to Group BY
Sub Totals using ROLLUP functionPreview
Sub Totals using CUBE function
Limiting number of sub totals using GROUPING SETS function
Group by Extensions Recap
|Section 7: Table Partitioning|
Table Partitioning IntroductionPreview
Range Partition based on range of values
List Partition based on list of values
Hash Partition based on the hash key
Composite Partitioning by mixing things up!!
Interval Partition for automatic partition creation
Table Partitioning Recap
|Section 8: Materialized Views|
Materialized Views IntroductionPreview
Materialized Views creation Options
Materialized Views with ON COMMIT option
Materialized Views with ON DEMAND option
Materialized Views with REFRESH FAST option
Timing the refresh
Query Rewrite functionality
Materialized Views Recap
|Section 9: Regular Expressions|
Regular Expressions IntroductionPreview
Meta Characters . and +
Meta Characters ? and *
Interval Operator to match the number of occurances
Matching the characters in a List
Lets combine multiple expressions using |
Check for an expression in the beginning or end of a string
POSIX Character class operators
Search for meta characters by placing a escape character
More examples on Regular Expressions
|Section 10: Flashback operations|
Flashback operations Introduction
Tracking changes in Data
Flashback Operations Recap
Amarnath has a Masters Degree in computer science with around 15 years of experience in the IT industry. He has successfully designed and implemented projects for clients in South Africa, India and USA. Currently his main focus is on Datawarehouse Architecture.
Amarnath is currently working for a fortune 500 company in USA and has pioneered the data architecture discipline. His Innovative and simple architectural designs have won accolades in the company.
Today, he brings leadership and technical expertise to enable customers to optimize and modernize their IT environments leveraging Business Intelligence tools, Big Data, social, mobile, and the Internet of Things.