cloveretl basic training excerpt

All rights reserved Javlin 2011

Basic Training Course for CloverETL software

Training teaser ― excerpt from Basic Training Course


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

Training Course Documentation

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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

Training Course Objectives

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DAY 1 Introduction Basic Principles

Getting Started Designer Walkthrough Transaction Analysis

Agenda

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DAY 2 Graphs for Real World Customer Profile Analysis

Lookups: Searching in Data

Agenda

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DAY 3 Database Datasources

Working with Structured Data

XML input/output

Final Review

Test

Q & A

Agenda

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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

B6. Task Discussion

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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”

Lookup Tables

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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

Lookup Table Structure

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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

Populating Lookup Tables

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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

Using Lookup Tables

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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

Component LookupTableReaderWriter

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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.

B6. Complete Graph Section

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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

Component LookupJoin

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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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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

B7. Task Discussion

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Data for range lookup:

-1000|0|Low -10000|-1000|Medium -1000000|-10000|High

Notes

› Only first match is returned -> order of data matters › null value in range definition means “unlimited”

• Data to match everything: ||the rest

Range Lookup Definition

Interval range

Interval Inclusivity

Interval range

Return value

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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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cloveretl basic training excerpt

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