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Hadoop Architecture
Duration
3 Days
Course
EJHD-100-ES
Available Formats
Classroom Training, Online Training
Course Description
Overview
This Hadoop Architecture and SQL course is designed to provide students with a deeper knowledge and understanding of the Hadoop Architecture and SQL and how to write it. Students will learn the Hadoop Architecture and SQL starting at the most basic level and going to the most advanced level with many examples.Objectives
Upon completion of the Hadoop 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 Hadoop Architecture and SQL from beginners to an advanced audience
Prerequisites
- None
Topics
- History of Data Warehousing
- The Growth of Computer Data and Use of Databases
- Definition of Enterprise Data vs. Big Data
- Why Enhance Your Company’s Data Warehousing Capabilities?
- Benefits of Big Data for Your Company
- Management Considerations
- Customer Considerations
- What is Hadoop All About?
- There is a Named Node and Up to 4000 Data Nodes
- The Named Node's Directory Tree
- The Data Nodes
- Hive MetaStore
- Data Layout and Protection – Step 1-4
- How are Blocks Distributed Amongst the Cluster?
- What is Parallel Processing?
- The Basics of a Single Computer
- Data in Memory is Fast as Lightning
- Parallel Processing Of Data
- Introduction to Hive
- Commodity Hardware Servers are Configured for Hadoop
- Commodity Hardware Allows Nodes to Scale Forever (Linear)
- The Named Node
- The Data Node's Responsibilities
- All Reducers, Some Reducers or a Single Reducer
- Hadoop has Linear Scalability
- The Architecture of a Hadoop Data Warehouse
- How to Find All Databases in the System
- Setting Your Default Database With the USE Command
- List the Tables in a Database With the Show Tables Command
- Show Basic Table Information with the Describe Command
- Show Detailed Table Information Using Describe Extended
- The Show Functions Command Lists all System Functions
- Describe Function Command Provides Function Information
- Describe Function Extended Command Provides Details
- Introduction
- SELECT * (All Columns) in a Table
- SELECT Specific Columns in a Table
- Place your Commas in front for better Debugging Capabilities
- Sort the Data with the ORDER BY Keyword
- ORDER BY Can Use the Column Number
- SORT BY Can Be Used Instead of ORDER BY
- Changing the ORDER BY to Descending Order
- Using the SORT BY in DESC Mode
- Major Sort vs. Minor Sorts
- SORT BY Using Major and Minor Sorts
- Using A CASE Statement to Sort Logically
- How to ALIAS a Column Name
- Comments using Double Dashes are Single Line Comments
- Comments for Multi-Lines
- Comments for Multi-Lines as Double Dashes per Line
- A Great Technique for Comments to Look for SQL Errors
- The WHERE Clause limits Returning Rows
- Case Sensitivity is Important
- You Cannot Use the Alias in the Where Clause
- NULL means NO DATA so Equals Null Returns No Rows
- Use IS NULL or IS NOT NULL when dealing with NULLs
- Using Greater Than or Equal To (>=)
- AND in the WHERE Clause
- Troubleshooting AND
- OR in the WHERE Clause
- Troubleshooting Or
- Troubleshooting Character Data
- Using Different Columns in an AND Statement
- What is the Order of Precedence?
- Using Parentheses to change the Order of Precedence
- An IN List is Another Technique
- Using an IN List in place of OR
- The IN List Can Use Character Data
- Using a NOT IN List
- BETWEEN and NOT BETWEEN are Inclusive
- LIKE command
- A Visual of CHARACTER Data vs. VARCHAR Data
- Use the TRIM command to remove spaces on CHAR Data Escape Characters
- RTRIM command Removes Trailing spaces on CHAR Data
- The Distinct Command
- The Distinct Command
- Distinct vs. GROUP BY
- Limit Will Limit the Returning Rows
- Limit Works Brilliantly with ORDER BY
- TABLESAMPLE
- Collect_List and Collect_Set
- GROUP BY when Aggregates and Normal Columns Mix
- GROUP BY Delivers one row per Group
- Limiting Rows and Improving Performance with WHERE
- Keyword HAVING tests Aggregates after they are Totaled
- Getting the Average Values Per Column
- Average Values Per Column For all Columns in a Table
- Hadoop Joins
- The Shuffle Join
- Shuffle
- Map Strategy
- Duplication of the Smaller Table across All-AMPs
- Using Buckets For Table Joins
- Sort-Merge Bucket Join Needs No Shuffling or Duplication
- A Two-Table Join Using Traditional Join Syntax/ ANSI Syntax
- Traditional Join Using a Table Alias
- ANSI Join Using a Table Alias
- ANSI Join Using a Table Alias With Keyword AS
- ANSI Join Using the Keyword JOIN Instead of INNER JOIN
- You Can Fully Qualify All Columns for Clarity
- A Two-Table Join in Action
- Another Way to Write a Join
- A Cartesian Product Join
- LEFT OUTER JOIN
- A LEFT SEMI JOIN Replaces a Subquery
- RIGHT OUTER JOIN
- FULL OUTER JOIN
- Which Tables are the Left and Which are the Right?
- INNER JOIN with an Additional WHERE and AND Clause
- OUTER JOIN with Additional WHERE and AND Clause
- Evaluation Order For Outer Queries
- Cartesian Product Join
- The CROSS JOIN With a WHERE Clause
- The CROSS JOIN With an ON Clause
- The Self Join
- How would you Join these two tables?
- An Associative Table is a Bridge that Joins Two Tables
- The Nexus Query Chameleon Writes the SQL for Users.
- An IN List is much like a Subquery
- An IN List Never has Duplicates – Just 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
- How to handle a NOT IN with potential NULL Values
- Using a Correlated Exists
- How a Correlated Exists matches up
- The Correlated NOT Exists
- Current_Date and Current_Timestamp Functions
- Extracted the Date From a Time Data Type
- Adding and Subtracting Days from a Time Column
- Adding Days and Providing a Discount
- Getting the Date Extracted From a:
- Time Data Type
- Time Using Substring
- Using Concat
- Day-Month-Year Format
- Day-Month-Year Format
- The Date in Perfect Day-Month-Year Format With CASE
- Getting a Count of All Orders Per Year Per Month
- The ADD_MONTHS Command
- Using Cast to Change a Data Type
- The Months_Between Command
- NEXT_DAY Command Finds a Future Day of the Week
- Interval Day
- Hadoop Calendar Knows Leap Year
- Interval Day, Month, Year Plus Cast
- The Row_Number Command
- Using a Derived Table and Row_Number
- Ordered Analytics OVER
- RANK and DENSE RANK
- RANK Defaults to Ascending Order
- Getting RANK to Sort in DESC Order
- RANK() OVER and PARTITION BY
- PERCENT_RANK() OVER
- CSUM
- The ANSI CSUM – Getting a Sequential Number
- Reset with a PARTITION BY Statement
- PARTITION BY only Resets a Single OLAP not ALL of them
- CURRENT ROW AND UNBOUNDED FOLLOWING
- Different Windowing Options
- Moving Sum has a Moving Window
- How ANSI Moving SUM Handles the Sort
- Moving SUM every 3-rows Vs a Continuous Average
- PARTITION BY Resets an ANSI OLAP
- The Moving Window is Current Row and Preceding
- Moving Average
- Moving Average every 3-rows Vs a Continuous Average
- PARTITION BY Resets an ANSI OLAP
- COUNT OVER for a Sequential Number
- COUNT OVER without Rows Unbounded Preceding
- The MAX OVER Command
- The MIN OVER Command
- The CSUM for Each Product_Id and the Next Start Date
- Ntile
- Using Quantiles (Partitions of Four)
- FIRST_VALUE
- LAST_VALUE
- LEAD
- LAG
- CUME_DIST
- SUM(SUM(n))
- There are two types of Temporary Tables
- CREATING A Derived Table
- CREATING A Derived Table using the WITH Command
- The Same Derived Query shown Two Different Ways
- Most Derived Tables Are Used To Join To Other Tables
- The Three Components of a Derived Table
- Visualize This Derived Table
- Our Join Example with A Different Column Aliasing Style
- Column Aliasing Can Default For Normal Columns
- Our Join Example With the WITH Syntax
- Clever Tricks on Aliasing Columns in a Derived Table
- Two Derived Tables Joining to a Permanent Table
- The Key to Multiple WITH Tables
- Joining Two WITH Tables to a Permanent Table
- Using a Derived Table and Row_Number
- LEAD
- Finding the First Occurrence
- Creating a Temporary Table
- Creating, Populating and Querying a Temporary Table
- Creating a Temporary Table
- Many Users Can Use the Same Temporary Table Name
- The LENGTH Command Counts Characters
- UPPER and LOWER Commands
- Using the LOWER Command
- A LOWER Command Example
- Using the UPPER Command
- Non-Letters are Unaffected by UPPER and LOWER
- SOUNDEX
- REGEXP_REPLACE
- Concatenation
- The TRIM Command trims both Leading and Trailing Spaces
- SUBSTRING and SUBSTR are equal, but use different syntax
- How SUBSTRING Works with NO ENDING POSITION
- Concatenation and SUBSTRING
- The Context_Ngrams Function
- Sentences Function
- The COALESCE Command – Fill In the Answers
- COALESCE is Equivalent to This CASE Statement
- 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
- Put a CASE in the ORDER BY
- The Fundamentals of Views
- Creating a Simple View to Restrict Sensitive Columns
- Describe a View
- Describe Extended a View
- You SELECT From a View
- Creating Views to Protect Sensitive Columns and Rows
- Querying Sensitive Columns and Rows in a View
- Basic Rules for Views
- How to Modify a View
- An Exception to the ORDER BY Rule inside a View
- Views Are Sometimes CREATED for Formatting
- Creating a View to Join Tables Together
- How 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 After a View Has Been Created
- Creating a Database
- The Basics of Creating a Table
- The ROW FORMAT will be Delimited or Serde
- Hive Data Type Fundamentals
- An Example of a Table Using All Basic Data Types
- Settings so Hive can Automatically Partition a Table
- Creating a Partitioned Table
- Creating an External Table
- Creating an External Table With a Specific Location
- INSERT/SELECT is One Method of Loading Data
- Using Buckets For Table Joins
- Defining Skewed Tables
- Defining a Table Location
- Creating a Text File Table
- Distribute By for Loading Data
- Sort By on Data Loads
- Cluster By Distributes and Sorts by the Same Key
- Hive does Not Store Data, But HDFS Does in These Formats
- Creating Tables as a Text file
- Hive SerDes Means Serializer/Deserializer
- Creating a Table as a SERDE
- Creating Tables as a SERDE with Advanced Options
- Creating Tables as an RCFile
- Creating Tables as ORC files
- Altering a Table to Add a Column
- Renaming a Table
- Dropping/Creating a Table
- Collecting Statistics – Cost Based Optimization (CBO)
- Collecting Statistics on Particular Columns of a Table
- Best Practices for Hive Cost Based Optimization
- Setting the Following Properties to Enable CBO
- Vectorization
- Use the DESCRIBE FORMATTED Function to See Statistics
- Hadoop Numeric Data Types
- Hadoop Date/Time Data Types
- Hadoop String Data Types Continued
- INSERT Syntax # 1-2
- INSERT/SELECT Command
- INSERT/SELECT example using All Columns (*)
- INSERT/SELECT example with Less Columns
- DELETE and TRUNCATE Examples
- Numeric Manipulation Functions
- Finding the Cube Root
- Ceiling Gets the Smallest Integer Not Smaller Than X
- Floor Finds the Largest Integer Not Greater Than X
- The Round Function and Precision
- The Conv Function
- The Stats Table
- Compute_Stats Function
- The STDDEV_POP Function
- A STDDEV_POP Example
- The STDDEV_SAMP Function
- A STDDEV_SAMP Example
- The VAR_POP Function
- A VAR_POP Example
- The VAR_SAMP Function
- A VAR_SAMP Example
- The VARIANCE Function
- A VARIANCE Example
- The CORR Function
- A CORR Example
- Another CORR Example so you can Compare
- The COVAR_POP Function
- A COVAR_POP Example
- Another COVAR_POP Example so you can Compare
- The COVAR_SAMP Function
- A COVAR_SAMP Example
- Another COVAR_SAMP Example so you can Compare
- Using GROUP BY
- There are Many Options to See an EXPLAIN Plan
- Explain Output has Three Parts
- EXPLAIN EXTENDED and the Abstract Syntax Tree
- EXPLAIN EXTENDED Stage Plans and Stage Dependencies
- EXPLAIN DEPENDENCY Keywords in an Explain
- EXPLAIN AUTHORIZATION Keywords in an Explain
- Using a WHERE Clause Explains a Predicate
- EXPLAIN With an ORDER BY Statement
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