IIS DataStage Test

This topic focuses on the essential tools within IBM DataStage: DataStage Designer and DataStage Director. Candidates are expected to know how to design, develop, and manage ETL jobs. The questions focus on creating jobs in Designer, handling job stages, specifying parameters, and managing reusable components. Additionally, candidates are tested on their ability to execute and monitor jobs in Director, understand job logs, manage job queues, and address runtime errors. This section is crucial for assessing the operational knowledge required for real-world DataStage development and management.

This section assesses the candidate's deep knowledge of DataStage components like Transformer, Lookup, Join, Merge, Funnel, and Aggregator stages. Each stage has unique functionality to manipulate data, join datasets, or handle errors. Candidates should understand when and how to apply each component based on the data requirements. For example, understanding how the Transformer stage is used for complex data transformations or how Lookups are handled efficiently in large datasets. This section also focuses on how these stages interact in parallel or sequential jobs.

This topic examines the candidate’s proficiency in orchestrating and scheduling DataStage jobs. Job sequences allow multiple jobs to be linked in an interdependent workflow, requiring knowledge of triggers, conditional branching, and error handling. Questions focus on creating job sequences that manage dependencies, handle retries on failure, and send notifications upon completion. Candidates are also tested on their ability to schedule jobs using cron jobs or DataStage’s internal scheduling tools, ensuring ETL workflows run on time. Managing these sequences efficiently is critical in production environments to meet data delivery SLAs.

In this section, the candidate’s knowledge of optimizing ETL jobs for large-scale data processing is evaluated. It covers advanced techniques like partitioning data to parallelize workloads, optimizing buffer memory usage, and implementing resource estimation to avoid bottlenecks in system resources. Candidates are also tested on job performance metrics, job execution time reduction, and the ability to tune DataStage jobs for complex data pipelines. Other topics include optimizing sort operations, minimizing data spillage, and effectively using join strategies to improve ETL performance.

This topic assesses the candidate's ability to integrate DataStage with external services using Service-Oriented Architecture (SOA) principles. Candidates must demonstrate proficiency in using DataStage to consume and publish Web Services and REST APIs for seamless data flow between systems. This includes configuring service stages, mapping data, handling service errors, and ensuring security through authentication mechanisms like OAuth. Additionally, questions will cover DataStage’s capability to handle real-time data integration and trigger jobs based on events from external services, making this skill crucial for modern ETL processes.

As cloud-based architectures become more prevalent, this topic evaluates the candidate's ability to integrate DataStage with cloud platforms such as AWS and Azure. Candidates are tested on their understanding of using DataStage with cloud storage services (like AWS S3, Azure Blob), and managing ETL workloads that interface with Big Data technologies like Hadoop and Apache Spark. Key skills include optimizing data movement to/from cloud environments, managing hybrid cloud infrastructures, and working with distributed datasets. Cloud-based ETL processes are critical for modern data architectures and should be mastered at advanced levels.

This topic evaluates a candidate’s ability to manage metadata and implement data governance using IBM tools like Information Analyzer, Metadata Workbench, and Business Glossary. Candidates must demonstrate proficiency in metadata management, including capturing, storing, and maintaining metadata throughout the ETL lifecycle. Questions cover how metadata is used for data lineage, data cataloging, and ensuring compliance with governance standards. Candidates must also understand how to implement data quality rules and manage metadata-related workflows that help ensure data is trusted, traceable, and fit for analysis.

In this section, candidates are tested on their ability to automate DataStage jobs using Unix shell scripting. This involves writing scripts to automate ETL processes, including job initiation, monitoring, and error handling. Candidates should know how to integrate DataStage jobs with cron jobs, perform file management tasks, and automate DataStage job logs for reporting. Advanced questions focus on using Unix to manage DataStage job parameters, create backups, and perform complex tasks like triggering jobs based on file arrival. Automation skills are essential for streamlining ETL processes and reducing manual intervention.

This topic assesses candidates’ ability to administer DataStage environments, including installing and configuring DataStage components, managing DataStage Services, and performing system upgrades. Candidates should be familiar with configuring DataStage Engines, managing user roles and permissions, and performing routine maintenance tasks like backup and restore of DSX files. Additionally, advanced questions focus on troubleshooting performance issues, addressing system outages, and scaling the DataStage environment to handle increased workloads. High availability configurations, load balancing, and clustering are also critical topics in system administration.