Excerpt from the CloverETL Basic Training slides. The basic course lasts 3 days and covers basic principles, CloverETL Designer walkthrough, transaction analysis, lookups, database connections, working with structured data, XML etc. More at www.cloveretl.com/services/training

1. Basic Training Course
for CloverETL software
Training teaser ―
excerpt from Basic Training Course
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2. Training Course Documentation
 This presentations accompanies the training course
delivery
 It can serve as a baseline for self-study
 The course focuses on fundamentals of CloverETL
platform which are needed for graph development
and management
 This document includes additional topics which are
intended to be used as introductions to more
advanced concepts and techniques
 The additional topics are not a formal part of the
course; they may or may not be referenced during the
class time depending on factors such as time
constraints and project relevance
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3. Training Course Objectives
 On successful completion of this course you will be
able to:
› Develop solutions to business problems using CloverETL
platform
› Compose graphs using Designer and Engine components
› Describe data formats with metadata definitions
› Access data from multiple sources including files and
databases
› Detect and react to errors in data
› Optimize your existing graphs
› Deploy and manage graphs in CloverETL Server
environment
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4. Agenda
DAY 1
 Introduction
 Basic Principles
 Getting Started
 Designer Walkthrough
 Transaction Analysis
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5. Agenda
DAY 2
 Graphs for Real World
 Customer Profile Analysis
 Lookups: Searching in Data
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6. Agenda
DAY 3
 Database Datasources
 Working with Structured Data
 XML input/output
 Final Review
 Test
 Q&A
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7. B6. Task Discussion
Sometimes data need to be enriched with referential
information:
 Who are the debtors?
Steps:
› Find customers identifiers who have negative personal
balance
› Look up details for all such customers – first and last name.
How:
› Use lookup tables to prepare the data for searching
› Use LookupJoin component to search the table
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8. Lookup Tables
Lookup tables are data structures that allow fast
searches over data
 Simple lookup is a hash table in memory
 Database lookup is a database table with local cache
 Range lookup allows performing range queries
› “Is the value A in range <10,20> or (20,100> ?”
 Persistent lookup uses index files to search data
 Aspell lookup allows similarity search over strings
› “Find matches for keyword ‘car’”. “Bar, card, cars”
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9. Lookup Table Structure
Data stored in lookup tables has the following
structure:
 Search key
› One or multiple fields
 Return value
› Returned when a match with key is found
› Some tables allow storing duplicate keys
› More than one match can be found
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10. Populating Lookup Tables
Data for a lookup table can be provided by several means:
 Manual data entry
› Data are part of lookup table definition
 File reference
› Table definition contains URL of the input file
› Metadata describe format of input file
› Simple parsing
 Dynamic population
› Designated component for writing into lookup files
› Data can be created dynamically by a graph
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11. Using Lookup Tables
 Lookup tables are reusable and can be accessed from
all reformat-like components.
 Reduce the size of the lookup by reducing record
width and including only applicable records in it.
 Lookup table must fit into memory or the graph will
fail
› does not apply to database and persistent lookups
 Comparable to Hash Join in performance
 Offer more flexibility than joiners for partial matching
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12. Component LookupTableReaderWriter
 The component can read or write contents of a
lookup tables of any type
 Use lookup table to:
› Dynamically populate lookup table with data
› Prepare the data for lookup when advanced parsing is
needed
› Dump lookup table into file or database
 Found in the Others section of Component Palette
 To configure the component, you need to provide:
› Target lookup table
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13. B6. Complete Graph Section
Step B6. Populate lookup table with data
Key points:
Use Simple lookup table type
Drop unnecessary fields prior to loading into table.
Split the graph into two phases, 0 and 1.
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14. Component LookupJoin
 LookupJoin component searching a lookup table for
match with records from regular data flow.
 Use lookup table to:
› Search any kind of lookup table for a match.
› Find records that did not have any match
› Comfortably handle multiple matches
 Found in the Joiners section of Component Palette
 To configure the component, you need to provide:
› Lookup table
› Joining key
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15. B6. Complete Graph Section
Step B5. Populate lookup table with data
Key points:
Use ExtFilter to find customers with negative
balance.
Use LookupJoin to search lookup table
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16. B7. Task Discussion
Range queries can be used to group similar records:
 What level of risk do the debtors impose?
Steps:
› Use three risk levels: low, medium, high
› Risk level is assigned based on amount of money owed
How:
› Use range lookup table to accommodate the range query
› Use lookup(<table_name>).get() to search the table from
transformation code
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17. Range Lookup Definition
 Data for range lookup:
-1000|0|Low
-10000|-1000|Medium
-1000000|-10000|High
Interval Return
range value
Interval
Inclusivity
 Notes Interval
range
› Only first match is returned -> order of data matters
› null value in range definition means “unlimited”
• Data to match everything:
||the rest
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18. B7. Complete Graph Section
Step B7. What level of risk do the debtors impose?
Key points:
Use range lookup to create risk level intervals
Use Reformat and lookup() to perform search
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