This course enables the project administrators and ETL developers to acquire the skills necessary to develop parallel jobs in DataStage. The emphasis is on developers. Only administrative functions that are relevant to DataStage developers are fully discussed. Students will learn to create parallel jobs that access sequential and relational data and combine and transform the data using functions and other job components.
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This course teaches how to build QualityStage parallel jobs that investigate, standardize, match, and consolidate data records. Students will gain experience by building an application that combines customer data from three source systems into a single master customer record.
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This course is designed to introduce advanced parallel job development techniques in DataStage v11.5. In this course you will develop a deeper understanding of the DataStage architecture, including a deeper understanding of the DataStage development and runtime environments. This will enable you to design parallel jobs that are robust, less subject to errors, reusable, and optimized for better performance.
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This KM413G: IBM InfoSphere Advanced QualityStage v11.5 course will step you through the Quality Stage data cleansing process. You will transform an unstructured data source into a format suitable for loading into an existing data target. You will cleanse the source data by building a customer rule set that you create and use that rule set to standardize the data. You will next build a reference match to relate the cleansed source data to the existing target data.
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This KM423G: IBM InfoSphere DataStage v11.x – Advanced Data Processing course is designed to introduce you to advanced parallel job data processing techniques in DataStage v11.5. In this course you will develop data techniques for processing different types of complex data resources including relational data, unstructured data (Excel spreadsheets), and XML data. In addition, you will learn advanced techniques for processing data, including techniques for masking data and techniques for validating data using data rules. Finally, you will learn techniques for updating data in a star schema data warehouse using the DataStage SCD (Slowly Changing Dimensions) stage. Even if you are not working with all of these specific types of data, you will benefit from this course by learning advanced DataStage job design techniques, techniques that go beyond those utilized in the DataStage Essentials course.
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This course gets those charged with administering Information Server v11.5 and its suite of many products and components started with the basic administrative tasks necessary to support Information Server users and developers.
The course begins with a functional overview of Information Server and the products and components that support these functions. Then it focuses on the basic administrative tasks an Information Server administrator will need to perform including user management, session management, and reporting management tasks. The course covers both the use of Information Server administrative clients such as the Administration Console and Metadata Asset Manager and the use of command line tools such as istool and encrypt.
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This course teaches Information Server and/or DataStage administrators to configure, manage, and monitor the DataStage Engine which plays a crucial role in Information Server. It not only runs high performance parallel ETL jobs designed and built in DataStage. It also supports other Information Server products including Information Analyzer, QualityStage, and Data Click. After introducing DataStage parallel jobs and the Engine that runs them, the course describes DataStage project configuration, the Engine’s development and runtime environments, and the Engine’s data source connectivity. In addition the course explains how to import and export DataStage objects, how to run and monitor DataStage jobs through the command line and GUI, and how to use some important Engine utilities.
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In this course students learn how the Information Governance Catalog is used to govern information assets through the development of a governance catalog of categories and terms. This catalog documents information assets and governance policies and rules that implement the high-level strategy and objectives of a governance program.
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This course enables students to acquire the skills necessary to use the Information Governance Catalog to analyze metadata stored within the Information Server Repository. The emphasis is on how metadata gets captured within the repository and how to explore and analyze the metadata it contains.
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This KM700G: IBM BigIntegrate for Data Engineers v11.5.0.2 course teaches data engineers how to run DataStage jobs in a Hadoop environment. You will run jobs in traditional and YARN mode, access HDFS files and Hive tables using different file formats and connector stages.
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In this course, you will learn how to use the IBM InfoSphere suite to analyze data and report results to business users. Information discovered during analysis will be used to construct data rules. This course will also explore techniques for delivering data analysis results to ETL developers and demonstrate how to develop more meaningful meta data to reflect data discovery results. An information analysis methodology and a case study will be used to guide exercises.
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This LX010G: Linux Basics course is designed to teach the basics of the Linux environment. Class activities include accessing a system, navigating the directory hierarchy, using the vi editor, using common commands, tools, and scripting concepts.
