Aster Data SQL and MapReduce Training

In this Aster Data SQL and MapReduce training class, participants will learn Aster Data SQL and MapReduce beginning at a basic level and moving to more advanced functions.

This course is designed for all users of Aster Data to help give them the skills to write and understand Aster Data SQL and MapReduce.

Goals
  1. Gain the skills to write and understand Aster Data SQL and MapReduce.
Outline
  1. The Aster Data Architecture
    1. What is Parallel Processing?
    2. Aster Data is a Parallel Processing System
    3. Each vworker holds a Portion of Every Table
    4. The Rows of a Table are Spread Across All vworkers
    5. Aster Tables are defined as Fact or Dimension when Created
    6. Fact Table
    7. A More Detailed Look at the Fact Table Distribution
    8. Dimension Table are Replicated
    9. A Dimension Table is often Replicated across vworkers
    10. Aster Data has Fact and Dimension Tables
    11. Aster Tables are defined as Fact or Dimension when Created
    12. Fact and Dimension Tables can be Hashed by the same Key
    13. Distribution Key Rules
    14. Aster Data Uses a Hash Formula
    15. The Hash Map Determines which vworker will own the Row
    16. The Hash Formula, Hash Map and vworker
    17. Placing rows on the vworker
    18. A Review of the Hashing Process
    19. Like Data Hashes to the Same vworker
    20. Distribution Key Data Types
    21. Run ANALYZE to COLLECT STATISTICS on a Table
    22. Some Examples of ANALYZE
    23. What Columns to Analyze
  2. Four Options for Aster Data Table Design
    1. There are Four Options to Aster Table Design
    2. Straight up Distribute by Hash
    3. Straight up Distribute by Hash - Problems
    4. Straight up Distribute by Replication
    5. Partition the Table with Logical Partitioning
    6. This Partitioned Table Sorts Rows by Month of Order_Date
    7. An All vworkers Retrieve By Way of a Single Partition
    8. You can Partition a Table by Range or by List
    9. A Partitioned By List Example with Three Tactical Queries
    10. Aster Data Multi-Level Partitioning
    11. Aster Allows for Multi-Level Partitioning
    12. SQL Commands for Logical Partitioning as One Table
    13. What Partitions are on my Table?
    14. What does a Columnar Table look like?
    15. A Comparison of Data for Normal Vs. Columnar
    16. A Columnar Table is best for Queries with Few Columns
    17. When to use a Columnar Table
  3. How Joins Work Inside the Aster Engine
    1. The Joining of Two Tables
    2. Aster Moves Joining Rows to the Same vworker
    3. Because of the Join Rule – Dimension Table are Replicated
    4. The Two Different Philosophies for Table Join Design
    5. What Could You Do If Two Tables Joined 1000 Times a Day?
    6. Fact and Dimension Tables can be Hashed by the same Key
    7. Joining Two Tables with the same PK/FK Distribution Key
    8. A Join With Co-Location
    9. A Performance Tuning Technique for Large Joins
    10. The Joining of Two Tables with an Additional WHERE Clause
    11. Aster Performs Joins Using Three Different Methods
    12. The Hash Join
    13. The Merge Join
    14. Nested Loop Joins
  4. Temporary and Analytic Tables
    1. Aster has Three Types of Data
    2. Create a Permanent Table Using Create Table AS (CTAS)
    3. Create a Logically Partitioned Table and Populate It
    4. Create a Temporary Table with using Create Table AS (CTAS)
    5. A Temporary Table in Action
    6. A Temporary Table That Uses an Insert/Select
    7. Create an Analytic Table Using an Insert/Select
    8. Create an Analytic Table Using CREATE TABLE AS (CTAS)
    9. Operations that Invalidate an Analytic Table
    10. If an Analytic Table is Invalid
    11. Tera-Tom History
  5. Tera-Tom's Top Tips
    1. Tera-Tom's Top Tips
    2. Tera-Tom's Top Tips # 2
    3. Tera-Tom's Top Tips #3
    4. Tera-Tom's Top Tips # 3 Rewritten
    5. Tera-Tom's Top Tips #4
    6. When the GROUP BY Column is NOT the Distribution Key
    7. Example of GROUP BY Column is NOT the Distribution Key
    8. Tera-Tom's Top Tips #5
    9. Tera-Tom's Top Tips #6 – Use EXPLAIN
    10. Query Plan and Estimates
    11. Explain Plan Showing a Hash Join
    12. Explain Plan Showing a Merge Join
    13. Explain Plan Showing a Nested Loop Join
  6. Indexes
    1. There are Only Three Types of Scans
    2. Guidelines for Indexes
    3. An Index Syntax Example
    4. The B-Tree Index
    5. Which Columns Might You Create an Index?
    6. A Visual of an Index (Conceptually)
    7. A Query Using an Index Uses All vworkers
    8. Multicolumn indexes
    9. A NUSI BITMAP Theory
    10. A NUSI Bitmap in Action
    11. Indexes on Expressions
    12. Indexes on Extracts of Dates
    13. GiST Indexes
    14. Five Operational Tips for Efficient Indexing
    15. REINDEX
    16. createCompressedIndexOnCompressedTableByDefault Flag
  7. SQL-MapReduce
    1. MapReduce History
    2. What is MapReduce?
    3. What is SQL-MapReduce?
    4. SQL-MapReduce Input
    5. SQL-MapReduce Output
    6. Subtle SQL-MapReduce Processing
    7. Aster Data Provides an Analytic Foundation
    8. Path Analysis
    9. Text Analysis
    10. Statistical Analysis
    11. Segmentation (Data Mining)
    12. Graph Analysis
    13. Transformation of Data
    14. Sessionize
    15. Tokenize
    16. SQL-MapReduce Function… nPath
    17. nPath SELECT Clause
    18. nPath ON Clause
    19. nPath PARTITION BY Expression
    20. nPath DIMENSION Expression
    21. nPath ORDER BY Expression
    22. nPath MODE Clause has Overlapping or NonOverlapping
    23. nPath PATTERN Clause
    24. Pattern Operators
    25. Pattern Operators Order of Precedence
    26. Matching Patterns Which Repeat
    27. nPath SYMBOLS Clause
    28. nPath RESULTS Clause
    29. Adding an Aggregate to nPath Results
    30. SQL-MapReduce Examples - Use Regular SQL
    31. SQL-MapReduce Examples - Create Objects
    32. SQL-MapReduce Examples - Subquery
    33. SQL-MapReduce Examples - Query as Input
    34. SQL-MapReduce Examples - Nesting Functions
    35. SQL-MapReduce Examples - Functions in Derived Tables
    36. SQL-MapReduce Examples - SMAVG
    37. SQL-MapReduce Examples - Pack Function
    38. SQL-MapReduce Examples - Pivot Columns
    39. Workshop: Create This Table
    40. Login to your GNOME Terminal
    41. Login to your Linux
    42. Using the GNOME Terminal Unzip the bank_web_data.zip
    43. Use the Function ncluster_loader to Load the Bank Data
    44. Run this nPath Map Reduce Function on your Table
    45. nPath in Action
    46. Operators at their Simplest
    47. Pattern
    48. Accumulate
    49. Accumulate With All Pages
    50. Accumulate – nPath with a WHERE Clause
    51. SQL-MapReduce Examples - Path Generator
    52. SQL-MapReduce Examples - Linear Regression
    53. SQL-MapReduce Examples - Naive Bayes
    54. Join Aster, Teradata and Hadoop Tables; feed into MapReduce
    55. Run Both of these Examples Together and Compare
    56. Run this nPath Map Reduce Function
    57. nPath in Action
    58. Another nPath Example
    59. Finding Out What Functions You Have Installed
    60. Multi-Case
    61. The Multi-Case Function
    62. The Multi-Case Function in Nexus
    63. The Multi-Case Function Mixing and Matching
    64. The Multi-Case Function Mixing and Matching
    65. SQL-MapReduce Examples - cFilter
    66. CFILTER in Action with Bank_Web_Clicks
    67. CFILTER in Action
    68. CFILTER using Nexus
    69. nPath Error
  8. Time and Date
    1. Date, Time, and Timestamp Keywords
    2. Add or Subtract Days from a date
    3. The to_char command
    4. A Summary of Math Operations on Dates
    5. Using a Math Operation to find your Age in Years
    6. Find What Day of the week you were Born
    7. Date Related Functions
    8. The EXTRACT Command
    9. EXTRACT from DATES and TIME
    10. EXTRACT with DATE and TIME Literals
    11. EXTRACT of the Month on Aggregate Queries
    12. A Side Title example with Reserved Words as an Alias
    13. Implied Extract of Day, Month and Year
    14. DATE_PART Function
    15. DATE_TRUNC Function
    16. DATE_TRUNC Function using TIME
    17. Aster NOW() Function
  9. Aster Windows Functions
    1. Cumulative Sum
    2. Cumulative Sum - Major and Minor Sort Key(s)
    3. The ANSI CSUM – Getting a Sequential Number
    4. The ANSI OLAP – Reset with a PARTITION BY Statement
    5. PARTITION BY only Resets a Single OLAP not ALL of them
    6. ANSI Moving Sum is Current Row and Preceding n Rows
    7. How ANSI Moving SUM Handles the Sort
    8. Moving SUM every 3-rows vs. a Continuous Sum
    9. Moving Average
    10. Partition By Resets an ANSI OLAP
    11. Moving Average Using BETWEEN
    12. Moving Difference using ANSI Syntax
    13. Moving Difference using ANSI Syntax with Partition By
    14. RANK Defaults to Ascending Order
    15. Getting RANK to Sort in DESC Order
    16. You can use Window Functions in Expressions
    17. RANK() OVER and PARTITION BY
    18. DENSE_RANK() OVER
    19. PERCENT_RANK() OVER
    20. PERCENT_RANK() OVER with 14 rows in Calculation
    21. PERCENT_RANK() OVER with 21 rows in Calculation
    22. RANK With ORDER BY SUM()
    23. COUNT OVER for a Sequential Number
    24. The MAX OVER Command
    25. MAX OVER with PARTITION BY Reset
    26. The MIN OVER Command
    27. The Row_Number Command
    28. NTILE
    29. NTILE Using a Value of 10
    30. NTILE With a Partition
    31. CUME_DIST
    32. CUME_DIST With a Partition
    33. LEAD
    34. LEAD With Partitioning
    35. LAG
    36. LAG with Partitioning
    37. FIRST_VALUE
    38. FIRST_VALUE After Sorting by the Highest Value
    39. FIRST_VALUE with Partitioning
    40. LAST_VALUE
    41. NTH_VALUE
    42. NTH_VALUE With Partition
    43. SUM(SUM(n))
  10. The Fundamental SQL Commands That Work on Aster
    1. BETWEEN is Inclusive
    2. BETWEEN Works for Character Data
    3. LIKE uses Wildcards Percent '%' and Underscore '_'
    4. LIKE command Underscore is Wildcard for one Character
    5. GROUP BY Vs. DISTINCT – Good Advice
    6. The Five Aggregates of Aster Data
    7. GROUP BY when Aggregates and Normal Columns Mix
    8. GROUP BY Delivers one row per Group
    9. GROUP BY Dept_No or GROUP BY 1 the same thing
    10. Limiting Rows and Improving Performance with WHERE
    11. WHERE Clause in Aggregation limits unneeded Calculations
    12. Keyword HAVING tests Aggregates after they are Totaled
    13. Keyword HAVING is like an Extra WHERE Clause for Totals
    14. Getting the Average Values per Column
    15. Getting the Average Values per Column
    16. Average Values per Column for All Columns in a Table
    17. A two-table join using Non-ANSI Syntax
    18. A two-table join using Non-ANSI Syntax with Table Alias
    19. Aliases and Fully Qualifying Columns
    20. A two-table join using ANSI Syntax
    21. Both Queries have the same Results and Performance
    22. LEFT OUTER JOIN
    23. LEFT OUTER JOIN Brings Back All Rows in the Left Table
    24. RIGHT OUTER JOIN
    25. RIGHT OUTER JOIN Brings Back All Rows in the RIGHT Table
    26. FULL OUTER JOIN
    27. FULL OUTER JOIN Brings Back All Rows in All Tables
    28. INNER JOIN with Additional AND Clause
    29. ANSI INNER JOIN with Additional AND Clause
    30. ANSI INNER JOIN with Additional WHERE Clause
    31. OUTER JOIN with Additional WHERE Clause
    32. OUTER JOIN with Additional AND Clause
    33. Results from OUTER JOIN with Additional AND Clause
    34. The DREADED Product Join
    35. Result Set of the DREADED Product Join
    36. The Horrifying Cartesian Product Join
    37. The ANSI Cartesian Join will ERROR
    38. How would you Join these two tables?
    39. How would you Join these two tables? You Can't Yet!
    40. An Associative Table is a Bridge that Joins Two Tables
    41. The 5-Table Join – Logical Insurance Model
    42. The Nexus Query Chameleon Writes the SQL for Users.
    43. An IN List is much like a Subquery
    44. An IN List Never has Duplicates – Just like a Subquery
    45. An IN List Ignores Duplicates
    46. The Subquery
    47. How a Basic Subquery Works
    48. The Final Answer Set from the Subquery
    49. Should you use a Subquery or a Join?
    50. CHARACTER_LENGTH AND OCTET_LENGTH
    51. The TRIM Command trims both Leading and Trailing Spaces
    52. Trim and Trailing is Case Sensitive
    53. Trim and Trailing works if Case right
    54. The SUBSTRING Command
    55. How SUBSTRING Works with NO ENDING POSITION
    56. Using SUBSTRING to move Backwards
    57. How SUBSTRING Works with a Starting Position of -1
    58. How SUBSTRING Works with an Ending Position of 0
    59. An Example using SUBSTRING, TRIM and CHAR Together
    60. SUBSTRING and SUBSTR are equal, but use different syntax
    61. The POSITION Command finds a Letters Position
    62. Concatenation
    63. The Basics of CAST (Convert and Store)
    64. Some Great CAST (Convert and Store) Examples
    65. Some Great CAST (Convert and Store) Examples
    66. Combining Searched Case and Valued Case
    67. A Trick for getting a Horizontal Case
    68. Nested Case
    69. Put a CASE in the ORDER BY
Class Materials

Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.

Preparing for Class

Training for your Team

Length: 2 Days
  • Private Class for your Team
  • Online or On-location
  • Customizable
  • Expert Instructors

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