Mastering IBM Infosphere QualityStage

Explore the IBM information server architecture with a unified user interface, common services, a central repository, parallel processing, and broad connectivity. Integrates quality stage and data cleansing features.

Explore IBM Infosphere Information Analyzer, IBM DataStage, and IBM Quality Stage integration, revealing data profiling, data quality, and metadata-driven ETL workflows.

Explore the four key tiers of the IBM Infosphere Information Server architecture and compare topologies from single server to fully clustered, highlighting scalability, fault tolerance, and resource isolation.

Explore IBM Infosphere DataStage, Governance Catalog, and industry warehousing models; learn that DataStage handles ETL, Governance Catalog covers data lineage and quality, and models address sector-specific warehousing needs.

Identify the five common data quality issues in enterprise data stores: lack of information standards, misfielded data, free form fields, data myopia, and redundancy.

Explore how IBM InfoSphere QualityStage standardizes, cleanses, and enriches data to meet quality goals, resolving ambiguities and deduplicating records to create a single authoritative view.

Explore IBM WebSphere Quality Stage's investigate, standardize, match, and survive functions to cleanse data, deduplicate records, and create a unified, best-record view using free-form text parsing and address verification.

Profile data with IBM Infosphere QualityStage, using Information Analyzer for cleansing and matching. Investigate full-volume data to classify, parse, validate, and stabilize data for consolidation.

Analyze the character discrete data with the C mask in IBM Infosphere Datastage and Quality Stage, reading Oracle customer data and producing pattern reports with samples and frequency cutoffs.

Explore how to apply the character discrete option with the T mask in IBM Infosphere QualityStage and configure it for email, phone, and contact columns to analyze patterns and frequencies.

Explore the character concatenate investigate stage with C, T, and X masks to validate email and phone data, ensuring the preferred contact method matches available information and maintains data quality.

Merge gender and active status with the character concatenate option and C masks, creating a single composite attribute from the Oracle customer table for downstream pattern analysis.

Explore the word investigate stage by loading Oracle customer data, analyzing the email column with a predefined rule set, and generating token and pattern reports for data quality management.

Transform data within IBM Infosphere QualityStage into standardized, fixed-column formats by validating content and standardizing spelling and abbreviations, using customizable rule sets from the investigate stage for consistent, match-ready output.

Walk through a standardized stage example in IBM Infosphere QualityStage, reading from an Oracle customer table, parsing emails into name and domain, and outputting to uppercase email standardization data set.

Explore validation rule sets, including email address validation with tokens and domain qualifiers, and phone and date validation rules that parse inputs, standardize formats, and populate BI and error fields.

Explore email standardization by presenting the output beside original columns and detailing standardized fields such as email user, email domain, and email URL, along with vmail and unhandled pattern flags.

Master data matching in IBM WebSphere Quality Stage by identifying duplicates, householding, and creating match groups across sources. Apply blocking, weights, and thresholds to classify matches and flag clerical review.

Consolidate duplicates, replace with superior data, fill missing values, and enrich records using rules in the survivorship stage of IBM WebSphere Quality Stage, producing a clean output.

Consolidate duplicate customer records with the survivorship stage by selecting the highest age as the best candidate, applying simple survive rules such as longest field and most frequent values.

Master IBM Infosphere QualityStage by loading customer data into a dataset, reinforcing the data loading concept within IBM Infosphere QualityStage.

Master the LookUp stage in IBM Datastage and QualityStage to perform data lookups within IBM Infosphere QualityStage workflows.

Learn to transform data with IBM DataStage and QualityStage to cleanse data and improve overall data quality within the Infosphere platform.

Investigate source data with the Investigation Stage in IBM Infosphere QualityStage, learning how to profile, cleanse, and assess data quality for better integration.