High availability and scalability with Pentaho Data Integration

Experts often possess more data than judgment.” – Colin Powell….hmmm, those experts surely are not using a scalable Business Intelligence solution to optimize that data which can help them make better decisions. :-)

Data is everywhere! The amount of data being collected by organizations today is experiencing explosive growth. In general, ETL (Extract Transform Load) tools have been designed to move, cleanse, integrate, normalize and enrich raw data to make it meaningful and available for knowledge workers and decision support systems. Once data has been “optimized,” only then can it be turned into “actionable” information using the appropriate business applications or Business Intelligence software. This information could then be used to discover how to increase profits, reduce costs or even write a program that suggests what your next movie on Netflix should be. The capability to pre-process this raw-data before making it available to the masses, becomes increasingly vital to organizations who must collect, merge and create a centralized repository containing “one version of the truth.” Having an ETL solution that is always available, extensible and highly scalable is an integral part of processing this data.

Pentaho Data Integration

Pentaho Data Integration (PDI) can provide such a solution for many varying ETL needs. Built upon a open Java framework, PDI uses a metadata driven design approach that eliminates the need to write, compile or maintain code. It provides an intuitive design interface with a rich library of prepacked plug-able design components. ETL developers with skill sets that range from the novice to the Data Warehouse expert can take advantage of the robust capabilities available within PDI immediately with little to no training.

The PDI Component Stack

Creating a highly available and scalable solution with Pentaho Data Integration begins with understanding the PDI component stack.

● Spoon – IDE – for creating Jobs, Transformations including the semantic layer for BI platform
● Pan – command line tool for executing Transformations modeled in Spoon
● Kitchen – command line tool for executing Jobs modeled in Spoon
● Carte – lightweight ETL server for remote execution
● Enterprise Data Integration Server – remote execution, version control repository, enterprise security
● Java API – write your own plug-ins or integrate into your own applications

Spoon is used to create the ETL design flow in the form of a Job or Transformation on a developer’s workstation. A Job coordinates and orchestrates the ETL process with components that control file movement, scripting, conditional flow logic, notification as well as the execution of other Jobs and Transformations. The Transformation is responsible for the extraction, transformation and loading or movement of the data. The flow is then published or scheduled to the Carte or Data Integration Server for remote execution. Kitchen and Pan can be used to call PDI Jobs and Transformations from your external command line shell scripts or 3rd party programs. There is also a complete Java SDK available to integrate and embed these process into your Java applications.

Figure 1: Sample Transformation that performs some data quality and exception checks before loading the cleansed data

PDI Remote Execution and Clusters

The core of a scalable/available PDI ETL solution involves the use of multiple Carte or Data Integration servers defined as “Slaves” in the ETL process. The remote Carte servers are started on different systems in the network infrastructure and listen for further instructions. Within the PDI process, a Cluster Scheme can be defined with one Master and multiple Slave nodes. This Cluster Scheme can be used to distribute the ETL workload in parallel appropriately across these multiple systems. It is also possible to define Dynamic Clusters where the Slave servers are only known at run-time. This is very useful in cloud computing scenarios where hosts are added or removed at will. More information on this topic including load statistics can be found here in an independent consulting white paper created by Nick Goodman from Bayon Technologies, “Scaling Out Large Data Volume Processing in the Cloud or on Premise.”

Figure 2: Cx2 means these steps are executed clustered on two Slave servers
All other steps are executed on the Master server

The Concept of High Availability, Recover-ability and Scalability

Building a highly available, scalable, recoverable solution with Pentaho Data Integration can involve a number of different parts, concepts and people. It is not a check box that you simply toggle when you want to enable or disable it. It involves careful design and planning to prepare and anticipate the events that may occur during an ETL process. Did the RDBMS go down? Did the Slave node die? Did I lose network connectivity during the load? Was there a data truncation error at the database? How much data will be processed on peak times? The list can go on and on. Fortunately PDI arms you with a variety of components including complete ETL metric logging, web services and dynamic variables that can be used to build recover-ability, availability, scalability scenarios into your PDI ETL solution.

For example, Managing Consultant in EMEA, Jens Bleuel developed a PDI implementation of the popular Watchdog concept. A solution that includes checks to monitor if everything is on track is using the concept of a Watchdog when executing its tasks and events. Visit the link above for more information on this implementation.

 

 

Putting it all together – (Sample)

Diethard Steiner, active Pentaho Community member and contributor, has written an excellent tutorial that explains how to set up PDI ETL remote execution using the Carte server. He also provides a complete tutorial (including sample files provided by Matt Casters, Chief Architect and founder of Kettle) on setting up a simple “available” solution to process files, using Pentaho Data Integration. You can get it here. Please note that advanced topics such as this are also covered in greater detail (designed by our Managing Consultant Jens Bleuel – EMEA) in our training course available here.

Summary

When attempting to process the vast amounts of data collected on a daily basis, it is critical to have a Data Integration solution that is not only easy to use but easily extendable. Pentaho Data Integration achieves this extensibility with its open architecture, component stack and object library which can be used to build a scalable and highly available ETL solution without exhaustive training and no code to write, compile or maintain.

Happy ETLing.

Regards,

Michael Tarallo
Senior Director of Sales Engineering
Pentaho

This blog was originally published on the Pentaho Evaluation Sandbox. A comprehensive resource for evaluating and testing Pentaho BI.

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