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If you look in the PDI MAIN directory you will see a sub-directory “simple-jndi”, which contains a file CALLED “jdbc.properties”. You should change this file so that the JNDI INFORMATION matches the one you use in your application server.

After that you set in the connection TAB of Spoon the “Method of access” to JNDI, the “Connection type” to the type of database you’re using. And “Connection name” to the name of the JDNI datasource (as USED in “jdbc.properties”).

If you look in the PDI main directory you will see a sub-directory “simple-jndi”, which contains a file called “jdbc.properties”. You should change this file so that the JNDI information matches the one you use in your application server.

After that you set in the connection tab of Spoon the “Method of access” to JNDI, the “Connection type” to the type of database you’re using. And “Connection name” to the name of the JDNI datasource (as used in “jdbc.properties”).

Transformations stream data through their STEPS:

1. That means that the SLOWEST step is going to determine the speed of a transformation.
2. So you optimize the slowest steps first. How can you TELL which step is the slowest: look at the size of the input buffer in the log view.
3. In the LATEST 3.1.0-M1 nightly build you will also find a graphical overview of this: HTTP://WWW.IBRIDGE.BE/?P=92
4. (the “graph” button at the bottom of the log view will show the details).
5. A slow step will have consistently large input buffer sizes. A fast step will consistently have low input buffer sizes.

Transformations stream data through their steps:

You can’t. PDI will complain in most of the cases if you have DUPLICATE fieldnames. Before PDI v2.5.0 you were ABLE to force duplicate fields, but ALSO only the first value of the duplicate fields could ever be used.

You can’t. PDI will complain in most of the cases if you have duplicate fieldnames. Before PDI v2.5.0 you were able to force duplicate fields, but also only the first value of the duplicate fields could ever be used.

SEVERAL solutions exist:

Use a “Select Values” step renaming a field while selecting also the original one. The result will be that the original field will be duplicated to another name.

It will look as FOLLOWS:

This will DUPLICATE fieldA to fieldB and fieldC.

Use a calculator step and use e.g. The NLV(A,B) operation as follows:

This will have the same effect as the first solution: 3 fields in the output which are copies of each other: fieldA, fieldB, and fieldC.

Use a JavaScript step to copy the field:

This will have the same effect as the previous solutions: 3 fields in the output which are copies of each other: fieldA, fieldB, and fieldC.

Several solutions exist:

Use a “Select Values” step renaming a field while selecting also the original one. The result will be that the original field will be duplicated to another name.

It will look as follows:

This will duplicate fieldA to fieldB and fieldC.

Use a calculator step and use e.g. The NLV(A,B) operation as follows:

This will have the same effect as the first solution: 3 fields in the output which are copies of each other: fieldA, fieldB, and fieldC.

Use a JavaScript step to copy the field:

This will have the same effect as the previous solutions: 3 fields in the output which are copies of each other: fieldA, fieldB, and fieldC.

It is a cube to VIEW data where we can slice and dice the data. It have time DIMENSION, LOCATIONS and figures.

It is a cube to view data where we can slice and dice the data. It have time dimension, locations and figures.

MDX is multi- DIMENSIONAL EXPRESSION which is a MAIN QUERY language IMPLEMENTED by the Mondrains.

MDX is multi- dimensional expression which is a main query language implemented by the Mondrains.

ABINITIO,DATASTAGE, INFORMATICA, Cognos Decision STREAM, ETC

Abinitio,DataStage, Informatica, Cognos Decision Stream, etc

XML is an extensiable markup language which DEFINES a SET of rule for encoding documents in both FORMATS which is human READABLE and machine readable.

XML is an extensiable markup language which defines a set of rule for encoding documents in both formats which is human readable and machine readable.

ETL TOOL is used for extracting data from the legecy system and LOAD it into specified database with some PROCESSING of cleansing data.

OLAP Tool is used for reporting process . Here data is available in MULTIDIMENSIONAL model hence we can write simple query to extract data from database.

ETL Tool is used for extracting data from the legecy system and load it into specified database with some processing of cleansing data.

OLAP Tool is used for reporting process . Here data is available in multidimensional model hence we can write simple query to extract data from database.

A DATA warehouse is said to be a three-tier SYSTEM where a middle system provides usable data in a secure WAY to END users. Both SIDE of this middle system are the end users and the back-end data stores.

A data warehouse is said to be a three-tier system where a middle system provides usable data in a secure way to end users. Both side of this middle system are the end users and the back-end data stores.

It CREATES and CONFIGURE the SET of TRANSFORMATION.

It creates and configure the set of transformation.

It is a set of INSTRUCTION which tell when and how to MOVE DATA from RESPECTIVE SOURCE to target.

It is a set of instruction which tell when and how to move data from respective source to target.

DATAFLOW from SOURCE to TARGET is CALLED as MAPPING.

Dataflow from source to target is called as mapping.

1. Full Load means completely erasing the insides of ONE or more tables and filling with FRESH data.
2. Incremental Load means applying ongoing changes to one or more tables based on a PREDEFINED SCHEDULE.

DATA staging is actually a group of procedures used to prepare SOURCE system data for LOADING a data warehouse.

Data staging is actually a group of procedures used to prepare source system data for loading a data warehouse.

SNAPSHOTS are read-only COPIES of a master table located on a remote node which can be periodically refreshed to REFLECT CHANGES made to the master table.

Snapshots are read-only copies of a master table located on a remote node which can be periodically refreshed to reflect changes made to the master table.

ETL is extraction , transforming , LOADING process the STEPS are :

1 – define the source
2 – define the target
3 – create the mapping
4 – create the session
5 – create the WORK flow

ETL is extraction , transforming , loading process the steps are :

1 – define the source
2 – define the target
3 – create the mapping
4 – create the session
5 – create the work flow

ETL Tool is used to get data from MANY source system like RDBMS, SAP, ETC. and CONVERT them based on the user requirement. It is REQUIRED when data float across many systems.

ETL Tool is used to get data from many source system like RDBMS, SAP, etc. and convert them based on the user requirement. It is required when data float across many systems.

Repository is a storage LOCATION where we can STORE the DATA SAFELY without any harmness.

Repository is a storage location where we can store the data safely without any harmness.

It is the technology which enables files to be transparently ENCRYPTED to SECURE personal data from attackers with PHYSICAL ACCESS to the computer.

It is the technology which enables files to be transparently encrypted to secure personal data from attackers with physical access to the computer.

1. Right-click on the FOLDER or FILE we WANT to decrypt, and then click on Properties OPTION.
2. Click the General tab, and then click Advanced.
3. CLEAR the Encrypt contents to secure data check box, click OK, and then click OK again.

A HIERARCHICAL navigation menu ALLOWS the user to come directly to a section of the site SEVERAL LEVELS below the TOP.

A hierarchical navigation menu allows the user to come directly to a section of the site several levels below the top.

It is an ENTRI LEVEL TOOL for DATA MANIPULATION.

It is an entri level tool for data manipulation.

It is a VISUAL, banded report writer. It has VARIOUS FEATURES lilke using subreports, CHARTS and GRAPHS etc.

It is a visual, banded report writer. It has various features lilke using subreports, charts and graphs etc.

Pentaho Data Mining USED the Waikato Environment for Information ANALYSIS to SEARCH data for patterns. It have functions for data processing, regression analysis, classification METHODS, etc.

Pentaho Data Mining used the Waikato Environment for Information Analysis to search data for patterns. It have functions for data processing, regression analysis, classification methods, etc.

PENTAHO SCHEMA Workbench offers a GRAPHICAL edge for designing OLAP CUBES for Pentaho ANALYSIS.

Pentaho Schema Workbench offers a graphical edge for designing OLAP cubes for Pentaho Analysis.

Pentaho Reporting ALLOWS organizations to easily ACCESS, format and deliver INFORMATION to EMPLOYEES, customers and partners.

Pentaho Reporting allows organizations to easily access, format and deliver information to employees, customers and partners.

PENTAHO Dashboards give BUSINESS users the CRITICAL INFORMATION they need to UNDERSTAND and improve organizational performance.

Pentaho Dashboards give business users the critical information they need to understand and improve organizational performance.

1. SUITE Pentaho

• BI Platform (JBoss Portal)
• Pentaho Dashboard
• JFreeReport
• Mondrian
• KETTLE
• Weka

2. All BUILD under Java platform

1. Suite Pentaho

2. All build under Java platform

Arguments are command LINE arguments that we would normally specify during batch processing .

Variables are environment or PDI variables that we would normally set in a PREVIOUS TRANSFORMATION in a job.

Arguments are command line arguments that we would normally specify during batch processing .

Variables are environment or PDI variables that we would normally set in a previous transformation in a job.

1. Open Source
2. Have community that support the users
3. Running well under multi platform (Windows, Linux, Macintosh, Solaris, Unix, etc)
4. Have complete package from reporting, ETL for WAREHOUSING DATA management,
5. OLAP server data mining ALSO dashboard.

1. Open Source
2. Have community that support the users
3. Running well under multi platform (Windows, Linux, Macintosh, Solaris, Unix, etc)
4. Have complete package from reporting, ETL for warehousing data management,
5. OLAP server data mining also dashboard.

we can’t. if we have DUPLICATE fieldnames. Before PDI v2.5.0 we were able to force duplicate FIELDS, but ALSO only the FIRST value of the duplicate fields could EVER be used.

we can’t. if we have duplicate fieldnames. Before PDI v2.5.0 we were able to force duplicate fields, but also only the first value of the duplicate fields could ever be used.

This is not possible as in PDI transformations all the steps run in PARALLEL. So we can’t sequentialize them. This WOULD require architectural changes to PDI and sequential PROCESSING also result in very slow processing.

This is not possible as in PDI transformations all the steps run in parallel. So we can’t sequentialize them. This would require architectural changes to PDI and sequential processing also result in very slow processing.

BIT is not a standard SQL data type. It’s not even standard on MySQL as the meaning (core definition) CHANGED from MySQL version 4 to 5.

Also a BIT USES 2 bytes on MySQL. That’s why in PDI we made the SAFE choice and went for a CHAR(1) to store a boolean. There is a simple workaround available: change the data type with a Select Values step to “INTEGER” in the metadata tab. This converts it to 1 for “true” and 0 for “false”, just like MySQL expects.

BIT is not a standard SQL data type. It’s not even standard on MySQL as the meaning (core definition) changed from MySQL version 4 to 5.

Also a BIT uses 2 bytes on MySQL. That’s why in PDI we made the safe choice and went for a char(1) to store a boolean. There is a simple workaround available: change the data type with a Select Values step to “Integer” in the metadata tab. This converts it to 1 for “true” and 0 for “false”, just like MySQL expects.

We can CREATE a NEW conversion or close and re-open the ONES we have loaded in SPOON.

We can Create a new conversion or close and re-open the ones we have loaded in Spoon.

it is not possible as in PDI transformations all of the STEPS RUN in PARALLEL. So we can’t sequentialize them.

it is not possible as in PDI transformations all of the steps run in parallel. So we can’t sequentialize them.

1. If we want to join 2 tables from the same DATABASE, we can use a “Table INPUT” STEP and do the join in SQL itself.
2. If we want to join 2 tables that are not in the same database. We can use the the “Database Join”.

1. Transformations is MOVING and transforming ROWS from source to target.
2. JOBS are more about high level FLOW control.

The Cube will contain the following data:

1. 3 Fact fields: Sales, COSTS and Discounts
2. Time Dimension: with the following hierarchy: Year, QUARTER and Month
3. 2 Customer Dimensions: one with location (Region, Country) and the other with Customer Group and Customer Name
4. Product Dimension: CONTAINING a Product Name

The Cube will contain the following data:

FINITE ORDERED list of elements is CALLED as TUPLE.

Finite ordered list of elements is called as tuple.

MULTIDIMENSIONAL Expressions (MDX) is a query language for OLAP DATABASES, much like SQL is a query language for RELATIONAL databases. It is also a CALCULATION language, with syntax similar to spreadsheet formulas.

Multidimensional Expressions (MDX) is a query language for OLAP databases, much like SQL is a query language for relational databases. It is also a calculation language, with syntax similar to spreadsheet formulas.

Pentaho Reporting Evaluation is a PARTICULAR package of a subset of the Pentaho Reporting capabilities, DESIGNED for typical first-phase evaluation activities such as accessing sample data, creating and editing reports, and VIEWING and INTERACTING with reports.

Pentaho Reporting Evaluation is a particular package of a subset of the Pentaho Reporting capabilities, designed for typical first-phase evaluation activities such as accessing sample data, creating and editing reports, and viewing and interacting with reports.

With the help of Pentaho’s open source METADATA capabilities, ADMINISTRATORS can outline a layer of ABSTRACTION that presents DATABASE information to business users in familiar business terms.

With the help of Pentaho’s open source metadata capabilities, administrators can outline a layer of abstraction that presents database information to business users in familiar business terms.

Pentaho METADATA is a PIECE of the Pentaho BI Platform designed to MAKE it easier for users to access INFORMATION in business terms.

Pentaho Metadata is a piece of the Pentaho BI Platform designed to make it easier for users to access information in business terms.

YES, PENTAHO is a TRADEMARK.

Yes, Pentaho is a trademark.

1. JAVA developers who generally use project components to rapidly assemble custom BI solutions
2. ISVs who can improve the value and ability of their solutions by embedding BI functionality
3. End-Users who can quickly deploy packaged BI solutions which are EITHER modest or greater to traditional commercial offerings at a dramatically lower cost.

The Pentaho BI Project encompasses the following major APPLICATION areas:

1. Business Intelligence Platform
2. Data MINING
3. Reporting
4. DASHBOARDS
5. Business Intelligence Platform

The Pentaho BI Project encompasses the following major application areas:

The Pentaho BI Project is an current EFFORT by the OPEN Source communal to PROVIDE GROUPS with best-in-class solutions for their initiative Business Intelligence (BI) NEEDS.

The Pentaho BI Project is an current effort by the Open Source communal to provide groups with best-in-class solutions for their initiative Business Intelligence (BI) needs.

Direct Analytics on MONGODB: It authorizes BUSINESS analysts and IT to ACCESS, analyze, and visualize MongoDB data.

Science Pack: Pentaho’s Data Science Pack operationalizes analytical modeling and machine learning while ALLOWING data scientists and developers to unburden the labor of data preparation to Pentaho Data Integration.

Full YARN Support for Hadoop: Pentaho’s YARN mixing enables ORGANIZATIONS to exploit the full computing power of Hadoop while leveraging existing skillsets and technology investments.

Direct Analytics on MongoDB: It authorizes business analysts and IT to access, analyze, and visualize MongoDB data.

Science Pack: Pentaho’s Data Science Pack operationalizes analytical modeling and machine learning while allowing data scientists and developers to unburden the labor of data preparation to Pentaho Data Integration.

Full YARN Support for Hadoop: Pentaho’s YARN mixing enables organizations to exploit the full computing power of Hadoop while leveraging existing skillsets and technology investments.

It addresses the BLOCKADES that block the organization’s ABILITY to GET value from all our data. Pentaho is DISCOVERED to ensure that each member of our team from developers to business users can easily convert data into value.

It addresses the blockades that block the organization’s ability to get value from all our data. Pentaho is discovered to ensure that each member of our team from developers to business users can easily convert data into value.