[05/Balsas/8]

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Questions and Answers

What is one of the types of versioning mentioned in Flow.BI?

  • Data Duplication Versioning
  • Schema Alteration Versioning
  • Metadata Versioning (correct)
  • Data Reduction Versioning

Why is versioning important for auditing and compliance?

  • It reduces the amount of data stored.
  • It limits access to historical data.
  • It increases reporting speed.
  • It provides an audit trail for transparency. (correct)

What does the rollback capability in versioning allow users to do?

  • Delete all historical records.
  • Revert to previous versions of data. (correct)
  • Enhance reporting performance.
  • Completely change data definitions.

How does versioning assist in impact analysis?

<p>By understanding the effects of data changes on downstream processes. (C)</p> Signup and view all the answers

What kind of data do information marts often limit access to?

<p>The latest version of data. (B)</p> Signup and view all the answers

Which SQL command is used to join the control table for versioning purposes?

<p>INNER JOIN control.control_ldts (C)</p> Signup and view all the answers

In Flow.BI, what aspect is maintained through report versioning?

<p>Changes made to reports over time. (A)</p> Signup and view all the answers

What does historical analysis enable organizations to do?

<p>Analyze data trends and changes over time. (B)</p> Signup and view all the answers

What is a disadvantage of not implementing versioning?

<p>Lack of data transparency. (C)</p> Signup and view all the answers

Which component is considered critical for effective data management in Flow.BI?

<p>Versioning. (A)</p> Signup and view all the answers

SQL API always provides historical data without any limitations.

<p>False (B)</p> Signup and view all the answers

Omitting gsr_ldts and gsr_sdts can help filter the data depending on specific needs.

<p>True (A)</p> Signup and view all the answers

Metadata versioning involves maintaining a record of changes to data definitions and business rules.

<p>True (A)</p> Signup and view all the answers

Versioning in Flow.BI does not allow for the rollback of changes made to data.

<p>False (B)</p> Signup and view all the answers

Auditing and compliance are not considered significant benefits of versioning in Flow.BI.

<p>False (B)</p> Signup and view all the answers

Versioning does not have any impact on the integrity and consistency of historical and current data.

<p>False (B)</p> Signup and view all the answers

The control table is joined using an outer join to manage versioning in Flow.BI.

<p>False (B)</p> Signup and view all the answers

Impact analysis in versioning helps understand how changes affect downstream processes like reports and dashboards.

<p>True (A)</p> Signup and view all the answers

Flow.BI only supports historical analysis but does not track changes to reports and dashboards.

<p>False (B)</p> Signup and view all the answers

Maintaining versioning is deemed an inconsequential element for effective data management in Flow.BI.

<p>False (B)</p> Signup and view all the answers

Match the types of versioning in Flow.BI with their descriptions:

<p>Metadata Versioning = Maintains a history of updates to data definitions and business rules Report Versioning = Tracks changes made to reports and dashboards over time Historical Analysis = Enables analysis of data trends and changes over time Rollback Capability = Allows reverting to previous versions in case of errors</p> Signup and view all the answers

Match the benefits of versioning in Flow.BI with their purposes:

<p>Auditing and Compliance = Provides an audit trail to meet regulatory requirements Data Consistency and Integrity = Ensures historical and current data remain aligned Impact Analysis = Helps understand schema or data changes effects on processes Rollback Capability = Enables recovery from errors in data changes</p> Signup and view all the answers

Match the versioning components with their applications:

<p>Inner Join of Control Table = Filters based on latest version tracking gsr_ldts and gsr_sdts = Used to control data visibility in queries Control.control_ldts = Enables condition checks for data updates Versioning Process = Manages historical records in a repository</p> Signup and view all the answers

Match the versioning impacts in Flow.BI with their effects:

<p>Historical Analysis = Supports better data governance Data Consistency = Aligns old and new data for business needs Impact Analysis = Evaluates consequences of changes on downstream reports Rollback Capability = Restores system to previous stable states</p> Signup and view all the answers

Match the SQL API functions with their features:

<p>Historical Data Access = Can provide data from different versions Data Filtering = Enables retrieval of specific needed information Information Marts = Typically limited to the latest data version Join Control Table = Used for maintaining data version integrity</p> Signup and view all the answers

Match the terms related to versioning in Flow.BI with their definitions:

<p>Versioning = Maintaining multiple versions of data and metadata Impact Analysis = Understanding effects of changes on analytics Audit Trail = Record of changes for regulatory compliance Data Definition Updates = Changes made to describe data attributes</p> Signup and view all the answers

Match the aspects addressed by versioning with their significance:

<p>Change Tracking = Enables tracking modifications over time Historical Data Preservation = Supports analysis of trends and patterns Error Recovery = Facilitates restoring previous versions Transparency = Ensures accountability in data management</p> Signup and view all the answers

Match the actions related to filtering in Flow.BI with their purposes:

<p>Omitting gsr_ldts and gsr_sdts = Helps refine data to necessary elements Joining Control Table = Ensures correct version retrieval Filtering Data = Improves data analysis accuracy Accessing Latest Versions = Provides the most relevant current data</p> Signup and view all the answers

Match the version management processes with their advantages:

<p>Version Control = Facilitates data lineage tracking Regular Updates = Maintains metadata freshness Historical Records = Supports robust auditing and analysis Data Integrity Checks = Ensures quality of data over time</p> Signup and view all the answers

Flashcards

Versioning

Keeping track of changes to data or metadata in a system, like taking snapshots of the data at different points in time.

gsr_ldts & gsr_sdts

A specific timestamp that represents a particular version of the data within a table.

Information Marts

Data warehouses that usually store only the most recent version of data.

Omitting gsr_ldts and gsr_sdts

Removing the "gsr_ldts" and "gsr_sdts" columns from your SQL query to focus only on the current data.

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Control Table

A table that holds information about the latest versions of data in other tables, allowing efficient filtering.

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Metadata Versioning

Maintaining a historical record of how data definitions, business rules, or data lineages have changed over time.

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Report Versioning

Tracking changes made to reports and dashboards over time, preserving different versions for comparison.

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Historical Analysis

The ability to analyze how data trends and changes have evolved across different versions.

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Auditing and Compliance

Providing an audit trail to ensure transparency and compliance with regulations.

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Rollback Capability

Enabling the recovery of older versions of data in case of errors or unintended changes.

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gsr_ldts (and gsr_sdts)

A special timestamp column used in SQL queries to identify different versions of data within a table.

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Information Marts and Latest Versions

A database design principle where each table only holds the most recent version of data, simplifying querying and analysis.

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Control Table (for Versioning)

A specialized table that stores the latest versions of data from other tables, allowing for efficient filtering and retrieval of current data.

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Versioning in Flow.BI

The concept of tracking and managing different versions of a data warehouse schema, metadata, or data itself. This allows for a history of changes to the structure, data, or processes.

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Metadata Versioning in Flow.BI

Keeping a record of changes made to the metadata of a database, such as definitions of tables, columns, or data relationships.

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Report Versioning in Flow.BI

Preserving different versions of reports and dashboards, allowing for comparison and understanding how the data and presentation have changed over time.

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Historical Analysis with Versioning

Understanding how data trends or data structures have changed by analyzing different versions of the data, allowing for insightful historical analysis.

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Auditing and Compliance with Versioning

Ensuring transparency and compliance with regulations by providing a documented history of changes made to the data or processes.

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Rollback Capability in Versioning

The ability to revert to older versions of a database or data structures in case of errors or unintended changes.

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Impact Analysis with Versioning

Understanding how changes to data or schema might affect downstream processes, such as reports or analysis workflows.

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What is versioning?

The process of tracking and managing changes to data, schema, or metadata. It allows you to keep track of historical versions and revert to previous states if needed.

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What are "gsr_ldts" and "gsr_sdts"?

These columns are used to specify the version of the data in SQL queries. They help identify specific point-in-time snapshots of data.

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What are information marts?

Data warehouses that usually store only the most recent version of data. They are optimized for quick access to the latest information.

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How to find the latest version of data in a table?

This technique helps find the most recent version of data in a table, which is often crucial in information marts.

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What is a control table?

A table that holds information about the latest versions of data in other tables. It helps you filter and retrieve current data efficiently.

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What is metadata versioning?

This type of versioning preserves the history of changes made to data definitions, business rules, and data lineage.

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What is report versioning?

This type of versioning tracks revisions made to reports and dashboards. It allows comparing different versions and understanding how they evolved.

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What is the importance of historical analysis?

This enables analyzing how data has changed and discovering trends over time. It helps identify patterns and understand the evolution of data.

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How does versioning help with auditing and compliance?

Versioning provides an audit trail that demonstrates transparency and compliance with regulations. It helps ensure accountability and traceability.

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What is the benefit of rollback capability?

Versioning allows reverting to previous versions of data or schema if errors or unintended changes occur. It provides a safety net for data management.

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Study Notes

Versioning in SQL API

  • SQL API can provide historical data using columns gsr_ldts and gsr_sdts.
  • Information marts often only include the latest version, sometimes omitting gsr_ldts and gsr_sdts.
  • Historical data can be filtered based on specific criteria.
  • Use an INNER JOIN with a control table to filter by version:
inner join control.control_ldts cl on 
    cl.gsr_client = x.gsr_client and cl.gsr_inst = x.gsr_inst 
    and cl.gsr_ldts = x.gsr_ldts and cl.is_latest

Versioning in Flow.BI

  • Flow.BI manages historical data, schemas, and metadata through versioning, maintaining and managing historical records or multiple versions of data, schema, or metadata.
  • Tracks changes, enabling better control, auditing, and historical analysis.

Types of Versioning

  • Metadata Versioning: Tracks changes to metadata (data definitions, rules, lineage).
  • Report Versioning: Keeps track of report/dashboard changes (e.g., lineage report versions, comparing how metrics and designs have evolved).

Importance of Versioning

  • Historical Analysis: Allows studying data trends over time, enabling analysis of data trends and changes over time.
  • Auditing and Compliance: Provides an audit trail for regulatory requirements, ensuring transparency and accountability.
  • Rollback Capability: Allows reverting to previous versions.
  • Data Consistency and Integrity: Ensures consistency between historical and current data, ensuring that historical and current data remain consistent and aligned with the business's needs.
  • Impact Analysis: Helps understand how changes affect downstream processes (reports, dashboards), helping in understanding the effect of schema or data changes on downstream processes.
  • Versioning in Flow.BI is crucial for data management and analytics, preserving historical data, and supporting data governance. It's a critical component for effective data management and analytics.

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