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Greenplum Architecture
Duration
3 Days
Course
ARGP-100
Available Formats
Classroom Training, Online Training
Course Description
Overview
This Greenplum Architecture and SQL course is designed to provide students with a deeper knowledge and understanding of the Greenplum Architecture and SQL and how to write it. Students will learn the Greenplum Architecture and SQL starting at the most basic level and going to the most advanced level with many examples.Objectives
Upon completion of the Greenplum Architecture and SQL course, students will be able to:
- Identify and apply basic SQL functions
- Understand the WHERE clause
- Contrast Distinct vs. Group by
- Recognize and apply functions, such as:
- Aggregation, Join, Date, OLAP, View, Sub-query and Statistical Aggregate
- Recognize and produce Temporary Tables
- Define and distinguish Strings
- Interrogate the Data
- Set Operators
- Demonstrate knowledge of the Data Manipulation Language (DML)
Audience
- Anyone who has a desire to learn the Greenplum Architecture and SQL from beginners to an advanced audience
Prerequisites
- None
Topics
- The Basics of a Single Computer
- Data in Memory is Fast as Lightning
- Parallel Processing Of Data
- Symmetric Multi-Processing (SMP) Server
- Commodity Hardware Servers are Configured for Greenplum
- The Segment's Responsibilities The Host's Plan is Either All Segments or a Single Segment
- Greenplum has Linear Scalability
- The Architecture of A Greenplum Data Warehouse
- Nexus is Now Available For Greenplum
- The Concepts of Greenplum Tables
- Tables are Either Distributed by Hash or Random
- Random Distribution Uses a Round Robin Technique
- Table are Either a Heap or Append-Only
- Tables are Stored in Either Row or Columnar Format
- Comparing Normal Table Vs. Columnar Tables
- Segments on Distributions are Aligned to Rebuild a Row
- Visualize the Data – Rows vs. Columns
- Table Rows are Either Sorted or Unsorted
- Creating a Clustered Index in Order to Physically Sort Rows
- Physically Ordered Tables Are Faster on Certain Queries
- Another Way to Create a Clustered Table
- Creating a B-Tree Index and then Running Analyze
- Creating a Bitmap Index
- Tables Can Be Partitioned
- Creating a Partitioned Table Using a List
- Creating a Multi-Level Partitioned Table
- Not Null and Unique Constraints
- Unique Constraints That Fail
- Primary Key Constraints
- A Primary Key Automatically Creates a Unique Index
- Creating an Automatic Number Called a Sequence
- Multiple INSERT example Using a Sequence
- Distribution Keys Hashed on Unique Values Spread Evenly
- Distribution Keys With Non-Unique Values Spread Unevenly
- Best Practices for Choosing a Distribution Key
- The Hash Map Determines which Segment owns the Row
- The Hash Map Determines which Node will Own the Row
- Hash Map Determines which Node will Own the Row
- A Review of the Hashing Process
- Non-Unique Distribution Keys have Skewed Data
- Greenplum Limitations
- Tables are Distributed Across All Segments
- The Table Header and the Data Rows are Stored Separately
- Segments Store Rows inside a Data Block Called a Page
- To Read a Data Block a Node Moves the Block into Memory
- A Full Table Scan Means All Nodes Must Read All Rows
- Rows are Organized inside a Page
- Heap Page
- Creating a Table that has a Clustered Index
- Clustered Index Page
- The Row Offset Array is the Guidance System for Every Row
- The Row Offset Array Provides Two Search Options
- The Row Offset Array Helps With Inserts
- B-Trees
- The Building of a B-Tree for a Clustered Index
- When Do I Create a Clustered Index?
- When Do I Create a Non Clustered Index?
- B-Tree for Non Clustered Index on a Clustered Table
- Adding a Non Clustered Index To A
- B-Tree for Non Clustered Index on a Heap Table
- The Four Stages of Modeling for Greenplum - Check out #4
- The Logical Model
- First, Second and Third Normal Form
- The Employee_Table and Department_Table can be Joined
- The Employee_Table and Department_Table Join SQL
- The Extended Logical Model Template
- User Access is of Great Importance
- User Access in Layman’s Terms
- User Access for Joins in Layman’s Terms
- The Nexus Shows Users the Table’s Distribution Key
- Data Demographics:
- Distinct Rows
- Distinct Rows Query
- Max Rows Null
- Max Rows Null Query
- Max Rows Per Value
- Max Rows Per Value
- Typical Rows Per Value
- Change Rating
- Typical Rows Per Value Query For Greenplum Systems
- SQL to Get the Average Rows Per Value for a Column (Mean)
- Factors When Choosing Greenplum Indexes
- Distribution Key Data Demographics Candidate Guidelines
- Distribution key Access Considerations
- Step 1 is to Pick All Potential Distribution Key Columns
- Step 2 is to Pick All Potential Secondary Indexes
- Answer to 2nd Step to Picking Potential Secondary Indexes
- Choose the Distribution Key and Secondary Indexes
- 3rd Step is to Picking your Indexes
- Our Index Picks
- Denormalization
- Derived Data
- Repeating Groups
- Pre-Joining Tables
- Storing Summary Data with a Trigger
- Summary Tables or Data Marts the Old Way
- Horizontal Partitioning the Old Way and the New Way
- Vertical Partitioning the Old Way
- Columnar Tables Are the New Vertical Partitioning
- Nexus Queries Every Major System
- Nexus Data Visualization
- Nexus is Doing a Five-Table Join
- Nexus Generates the SQL Automatically
- Nexus Delivers the Report
- Cross-System Joins From Teradata, Oracle and SQL Server
- The Tabs of the Super Join Builder
- The 9 Tabs of the Super Join Builder
- Objects Tab 1- Selecting Columns in the Objects Tab
- Columns Tab 2- Removing Columns From the Report in the Columns Tab
- Sorting Tab 3
- Joins Tab 4
- Where Tab 5- Using the WHERE Tab For Additional WHERE or AND
- SQL Tab 6 – check paragraph below
- Answer Set Tab 7
- Analytics Tab 9
- Nexus Data Movement
- Moving a Single Table To a Different System
- The Single Table Data Movement Screen
- Moving an Entire Database To a Different System
- The Database Mover Screen and Options Tab
- Converting DDL Table Structures
- Compare and Synchronize
- Compare Two Different Databases From Different Systems
- Comparisons Down to the Column Level
- The Results Tab
- View Differences
- Synchronizing Differences In the Results Tab
- Hound Dog Compression
- Introduction
- SELECT * (All Columns) in a Table
- Fully Qualifying a Database, Schema and Table
- SELECT Specific Columns in a Table
- Sort the Data with the ORDER BY Keyword
- ORDER BY Defaults to Ascending
- Use the Name or the Number in your ORDER BY Statement
- Two Examples of ORDER BY using Different Techniques
- Changing the ORDER BY to Descending Order
- NULL Values sort First in Ascending Mode (Default)
- NULL Values sort Last in Descending Mode (DESC)
- Major Sort vs. Minor Sorts
- Multiple Sort Keys using Names vs. Numbers
- Sorts are Alphabetical, NOT Logical
- Using A CASE Statement to Sort Logically
- How to ALIAS a Column Name
- A Missing Comma can by Mistake become an Alias
- Comments using Double Dashes are Single Line Comments
- Comments for Multi-Lines
- The WHERE Clause limits Returning Rows
- Double Quoted Aliases are for Reserved Words and Spaces
- Character Data needs Single Quotes in the WHERE Clause
- Comparisons against a Null Value
- Use IS NULL or IS NOT NULL when dealing with NULLs
- Using Greater Than or Equal To (>=)
- AND in the WHERE Clause
- OR in the WHERE Clause
- Troubleshooting Character Data
- Using Different Columns in an AND Statement
- What is the Order of Precedence?
- Using Parentheses to change the Order of Precedence
- Using an IN List in place of OR
- IN List vs. OR brings the same Results
- Using a NOT IN List
- Null Values in a NOT IN List Bring Back No Rows
- A Technique for Handling Nulls with a NOT IN List
- BETWEEN is Inclusive
- NOT BETWEEN is Also Inclusive
- LIKE uses Wildcards Percent ‘%’ and Underscore ‘_’
- LIKE command Underscore is Wildcard for one Character
- ilike
- LIKE Command Works Differently on Char Vs Varchar
- Troubleshooting LIKE Command on Character Data
- Introducing the TRIM Command
- Introducing the RTRIM Command
- Numbers are Right Justified and Character Data is Left
- A Visual of CHARACTER Data vs. VARCHAR Data
- Use the TRIM command to remove spaces on CHAR Data
- Escape Character in the LIKE Command changes Wildcards
- Escape Characters Turn off Wildcards in the LIKE Command
- Introducing the RTRIM Command
- An example of Data with Left and Right Justification
- A Visual of CHARACTER Data vs. VARCHAR Data
- RTRIM command Removes Trailing spaces on CHAR Data
- Using Like with an AND/OR Clause to Find Letters
- The Distinct Command
- Distinct vs. GROUP BY
- The 3 Rules of Aggregation
- There are Five Aggregates
- Troubleshooting Aggregates
- GROUP BY when Aggregates and Normal Columns Mix
- GROUP BY delivers one row per Group
- GROUP BY Dept_No or GROUP BY 1 the same thing
- Limiting Rows and Improving Performance with WHERE
- WHERE Clause in Aggregation limits unneeded Calculations
- Keyword HAVING tests Aggregates after they are Totaled
- Aggregates Return Null on Empty Tables
- Keyword HAVING is like an Extra WHERE Clause for Totals
- Keyword HAVING tests Aggregates after they are Totaled
- Getting the Average Values Per Column
- Average Values Per Column For all Columns in a Table
- Three types of Advanced Grouping
- Group By Grouping Sets/Rollup
- GROUP BY Cube
- Redistribution
- Big Table Small Table Join Strategy
- Duplication of the Smaller Table across All-Distributions
- If the Join Condition is the Distribution Key no Movement
- Matching Rows That Are On The Same Node Naturally
- Strategy 1 of 4 – The Merge Join
- Strategy 2 of 4 – The Hash Join
- Strategy 3 of 4 – The Nested Join
- Strategy 4 of 4 – The Product Join
- A Two-Table Join Using Traditional Syntax
- A two-table join using Non-ANSI Syntax with Table Alias
- You Can Fully Qualify All Columns
- A two-table join using ANSI Syntax
- Both Queries have the same Results and Performance
- LEFT OUTER JOIN
- RIGHT OUTER JOIN
- FULL OUTER JOIN
- Which Tables are the Left and which Tables are Right?
- INNER JOIN with Additional AND Clause
- ANSI INNER JOIN with Additional AND and WHERE Clause
- OUTER JOIN with Additional WHERE and AND Clause
- Evaluation Order for Outer Queries
- The DREADED Product Join
- The Horrifying Cartesian Product Join
- The ANSI Cartesian Join will ERROR
- The CROSS JOIN
- The Self Join
- The Self Join with ANSI Syntax
- How would you Join these two tables?
- An Associative Table is a Bridge that Joins Two Tables
- The 5-Table Join – Logical Insurance Model
- The Nexus Query Chameleon Writes the SQL for Users.
- Current_Date
- Current_Date, Current_Time, and Current_Timestamp
- Current_Time vs. LocalTime With Precision
- Local_Time and Local_Timestamp With Precision
- Now() and Timeofday() Functions
- Adding A Week to a Date
- Add or Subtract Days from a date
- Formatting Dates and Dollar Amounts
- The EXTRACT Command
- EXTRACT Command on the Century
- Date_part Command
- Date_Trunc Command With Time/Dates
- The AGE Command
- Epoch
- Using Intervals
- Interval Arithmetic Results
- A Complex Time Interval example using CAST
- The OVERLAPS Command
- Using Both CAST and CONVERT in Literal Values
- A Better Technique for YEAR, MONTH, and DAY Functions
- Postgres Conversion Functions
- To_Char command Examples
- Formatting A Date with To_Char
- To_Number
- To_Date
- To_Timestamp
- An IN List is much like a Subquery
- The Subquery
- The Three Steps of How a Basic Subquery Works
- These are Equivalent Queries
- The Final Answer Set from the Subquery
- Should you use a Subquery of a Join?
- The Basics of a Correlated Subquery
- The Top Query always runs first in a Correlated Subquery
- Correlated Subquery Example vs. a Join with a Derived Table
- How to handle a NOT IN with Potential NULL Values
- IN is equivalent to =ANY
- Using a Correlated Exists
- How a Correlated Exists matches up
- The Correlated NOT Exists
- CSUM
- The ANSI CSUM
- Troubleshooting The ANSI OLAP on a GROUP BY
- Reset with a PARTITION BY Statement
- PARTITION BY only Resets a Single OLAP not ALL of them
- Moving SUM
- How ANSI Moving SUM Handles the Sort
- Moving SUM every 3-rows Vs a Continuous Average
- Partition By Resets an ANSI OLAP
- Both the Greenplum Moving Average and ANSI Version
- Moving Average
- The Moving Window is Current Row and Preceding
- How Moving Average Handles the Sort
- Moving Average every 3-rows Vs a Continuous Average
- Partition By Resets an ANSI OLAP
- Moving Difference using ANSI Syntax with Partition By
- RANK Defaults to Ascending Order
- Getting RANK to Sort in DESC Order
- RANK() OVER and PARTITION BY
- RANK and DENSE RANK
- PERCENT_RANK() OVER
- COUNT OVER for a Sequential Number
- Troubleshooting COUNT OVER
- The MAX OVER Command
- Troubleshooting MAX OVER
- The MIN OVER Command
- Troubleshooting MIN OVER
- Finding a Value of a Column in the Next Row with MIN
- The Row_Number Command
- Using a Derived Table and Row_Number
- Ordered Analytics OVER
- CURRENT ROW AND UNBOUNDED FOLLOWING
- Different Windowing Options
- The CSUM For Each Product_Id and the Next Start Date
- How Ntile Works
- Ntile
- Ntile Percentile
- Using Tertiles (Partitions of Four)
- NTILE
- Using FIRST_VALUE
- FIRST_VALUE
- LAST_VALUE
- LEAD
- LAG
- CUME_DIST
- SUM(SUM(n))
- There are Two Types of Temporary Tables
- CREATING A Derived Table
- Naming the Derived Table
- Aliasing the Column Names in The Derived Table
- Multiple Ways to Alias the Columns in a Derived Table
- CREATING A Derived Table using the WITH Command
- The Same Derived Query shown Three Different Ways
- Most Derived Tables Are Used To Join To Other Tables
- The Three Components of a Derived Table
- Visualize This Derived Table
- A Derived Table and CAST Statements
- Clever Tricks on Aliasing Columns in a Derived Table
- MULTIPLE Derived Tables using the WITH Command
- Three Steps to Creating a Temporary Table
- Three Versions of Creating a Temporary Table
- ON COMMIT PRESERVE ROWS is the Greenplum Default
- ON COMMIT DELETE ROWS
- How to Use the ON COMMIT DELETE ROWS Option
- ON COMMIT DROP
- Create Table AS/LIKE
- Creating a Clustered Index on a Temporary Table
- The CHARACTERS Command Counts Characters
- CHARACTER_LENGTH and OCTET_LENGTH
- The TRIM Command
- Trim Combined with the CHARACTERS Command
- A Visual of the TRIM Command Using Concatenation
- The SUBSTRING Command
- An example using SUBSTRING, TRIM and CHAR Together
- The POSITION Command finds a Letters Position
- Concatenation
- Concatenation and SUBSTRING
- Four Concatenations Together
- Troubleshooting Concatenation
- The NULLIF Command
- The COALESCE Command – Fill In the Answers
- COALESCE is Equivalent to This CASE Statement
- The COALESCE Command
- The Basics of CAST (Convert and Store)
- A Rounding Example
- Some Great CAST (Convert And STore) example
- Using an ELSE in the Case Statement
- Using an ELSE as a Safety Net
- Rules For a Valued Case Statement
- Rules for a Searched Case Statement
- Valued Case Vs. A Searched Case
- The CASE Challenge
- Combining Searched Case and Valued Case
- A Trick for getting a Horizontal Case
- Nested Case
- Rules of Set Operators
- INTERSECT Explained Logically
- UNION Explained Logically
- UNION ALL Explained Logically
- EXCEPT Explained Logically
- An Equal Amount of Columns in both SELECT List
- Columns in the SELECT list should be from the same Domain
- The Top Query handles all Aliases
- The Bottom Query does the ORDER BY (a Number)
- Great Trick: Place your Set Operator in a Derived Table
- UNION Vs UNION ALL
- Using UNION ALL and Literals
- A Great example of how EXCEPT works
- USING Multiple SET Operators in a Single Request
- Changing the Order of Precedence with Parentheses
- Using UNION ALL for speed in Merging Data Sets
- The Fundamentals of Views
- Creating a Simple View to Restrict Sensitive Columns/Rows
- Basic Rules for Views
- Exception to the ORDER BY Rule inside a View
- Views sometimes CREATED for Formatting
- Creating a View to Join Tables Together
- Another Way to Alias Columns in a View CREATE
- The Standard Way Most Aliasing is Done
- What Happens When Both Aliasing Options Are Present
- Resolving Aliasing Problems in a View CREATE
- Answer to Resolving Aliasing Problems in a View CREATE
- Aggregates on View Aggregates
- Altering A Table
- A View that Errors After An ALTER
- Greenplum Has Only Two Distribution Policies
- Creating a Table With A Single Column Distribution Key
- The Default Table Storage is a Heap
- Creating a Table With a Multi-Column Distribution Key
- Creating a Table With Random Distribution
- Creating a Table With No Distribution Key
- Guidelines for Partitioning a Table
- Creating a Partitioned Table Using a Range
- A Visual of One Year of Data with Range Partitioning
- Creating a Partitioned Table Using a Range Per Day
- Creating a Partitioned Table Using a List
- Creating a Multi-Level Partitioned Table
- Not Null Constraints
- Unique Constraints
- Primary Key Constraints
- Check Constraints
- Append Only Tables
- Column-Orientated Tables
- CREATE INDEX Syntax
- Create Table LIKE
- Greenplum Data Types
- INSERT Syntax # 1
- INSERT Syntax # 2
- INSERT example with Syntax 3
- INSERT/SELECT Command
- Two UPDATE Examples
- Subquery UPDATE Command Syntax
- Join UPDATE Command Syntax
- Fast UPDATE
- The DELETE Command Basic Syntax
- To DELETE or to TRUNCATE
- Subquery and Join DELETE Command Syntax
- ANALYZE
- What Columns Should You Analyze?
- Why Analyze?
- VACUUM
- How to See an EXPLAIN Plan
- The Eight Rules to Reading an EXPLAIN Plan
- Interpreting Keywords in an EXPLAIN Plan
- Interpreting an EXPLAIN Plan
- A Single Segment Retrieve – The Fastest Query
- EXPLAIN With an ORDER BY Statement
- EXPLAIN ANALYZE
- EXPLAIN With a Range Query on a Table Partitioned By Day
- EXPLAIN That Uses a B-Tree Index Scan
- EXPLAIN That Uses a Bitmap Scan
- EXPLAIN With a Simple Subquery
- EXPLAIN With a Columnar Query
- EXPLAIN With a Clustered Index
- EXPLAIN With Join that has to Move Data
- EXPLAIN With Join that has to Move Data
- Changing the Join Query Changes the EXPLAIN Plan
- Analyzing the Tables Structures For a 3-Table Join
- An EXPLAIN For a 3-Table Join
- Explain of a Derived Table vs. a Correlated Subquery
- The Stats Table
- Above, is the Stats_Table data in which we will use in our statistical examples
- The STDDEV_POP Function
- The STDDEV_SAMP Function
- The VAR_POP Function
- The VAR_SAMP Function
- The VARIANCE Function
- The CORR Function
- The COVAR_POP Function
- The COVAR_SAMP Function
- The REGR_INTERCEPT Function
- The REGR_SLOPE Function
- The REGR_AVGX Function
- The REGR_AVGY Function
- The REGR_COUNT Function
- The REGR_R2 Function
- The REGR_SXX Function
- The REGR_SXY Function
- The REGR_SYY Function
- Using GROUP BY
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