This course is designed to introduce the student to some of the additional capabilities and the administration of IBM Big SQL. IBM Big SQL allows you to access your HDFS data by providing a logical view to it. You can use the same SQL that was developed for your data warehouse data on your HDFS data. This course covers Big SQL security using row and column access controls, impersonation, and data federation. The course also covers some of the best practices, performance tuning, and monitoring techniques, YARN integration and also includes an optional unit to explore a Big SQL installation.
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K04002G – Loading dashDB Tables Using the Command Line Processor
This offering teaches cloud database support staff how to use a Command Line Processor (CLP) to load data into a dashDB for Analytics or dashDB for Transactions database.
- Self-Paced Virtual Classroom
K04007G – Loading dashDB Tables Using CLPPLUS and REST API
This offering teaches cloud database support staff how to use the CLPPLUS and REST API command line tools to load data into a dashDB for Analytics or dashDB for Transactions database.
- Self-Paced Virtual Classroom
KM020G – IBM InfoSphere Data Replication – InfoSphere Change Data Capture Essentials
In this course students will learn about the InfoSphere Change Data Capture (CDC) component of the IBM InfoSphere Data Replication family of solutions. This course will examine the architecture, components and capabilities of CDC, and discuss various ways to setup and implement the software. The course will explore how to operate and troubleshoot CDC and discuss “Best Practices” in maintaining the Environment. Lastly, use cases will be provided to help student understand how replication is used using InfoSphere Change Data Capture to a Business Environment.
- Instructor Led Online
KM204G – IBM InfoSphere DataStage Essentials
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.
KM213G – IBM InfoSphere QualityStage Essentials
This course teaches students 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.
KM404G – IBM InfoSphere Advanced DataStage: Parallel Framework
This course is designed to introduce advanced parallel job development techniques in DataStage v11.5. In this course students 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.
KM413G – IBM InfoSphere Advanced QualityStage
This course introduces students to the QualityStage data cleansing process. Students will transform an unstructured data source into a format suitable for loading into an existing data target. Students will cleanse the source data by building a customer rule set that they create and use that rule set to standardize the data. Students will next build a reference match to relate the cleansed source data to the existing target data.
KM423G – IBM InfoSphere DataStage – Advanced Data Processing
This course is designed to introduce students to advanced parallel job data processing techniques in InfoSphere DataStage v11.5. In this course students will develop data techniques for processing different types of complex data resources including relational data, unstructured data (Excel spreadsheets), and XML data. In addition, students will learn advanced techniques for processing data, including techniques for masking data and techniques for validating data using data rules. Finally, students will learn techniques for updating data in a star schema data warehouse using the DataStage SCD (Slowly Changing Dimensions) stage. Even if students are not working with all of these specific types of data, they will benefit from this course by learning advanced DataStage job design techniques, techniques that go beyond those utilized in the DataStage Essentials course.