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What Is Structured Aata archiving And Why ILM?

Structured data archiving technologies help IT leaders retire legacy applications, reduce capital and operating expenses, and meet governance and compliance requirements. Structured data archiving is the ability to index, migrate and protect application data in secondary databases or flat files typically located on lower-cost storage for policy-based retention. It makes data available in context and protects it in the event of litigation or an audit.

Structured data archiving addresses:

  • Storage optimization — It can reduce the volume of data in production and maintain seamless data access. The benefits of using this technology include reduced capital and operating expenditures, improved information governance, improved recoverability, lower risk of regulatory compliance violations, and access to secondary data for reporting and analysis.
  • Governance — The technology preserves data for compliance when retiring applications. Structured data is often transactional and related to financial accounts or back-office functions (for example, HR, patient enrollment in healthcare and other use cases that might be regulated) that require information governance, control and security, along with the ability to respond to related events such as audits, litigation and investigation. These and other requirements, such as maintaining information context, can prevent organizations from moving data to lower-cost tiers of storage, or adopting other do-it-yourself approaches.
  • Cost optimization — Structured data archiving and application retirement can result in significant ROI. Structured data in legacy systems, ERP and databases accumulates over years — and, in some cases, over decades — driving up operational and capital expenses.
  • Data scalability — The technology can manage large volumes of nontraditional data resulting from newer applications that can generate billions of small objects. Scalability to petabytes of capacity is required in these cases.

The desire to leverage archives as a secondary data store for big data analytics is driving the growth of the structured data archiving market. Newer market participants are offering alternate ways for managing archived data that involve virtual copies of databases, extreme compression and native SQL access.

 

Source http://www.gartner.com

 

“TE_7020 Unbound field in Source Qualifier” when running session

Problem Description

TE_7020 Unbound field in Source Qualifier

Informatica Error – TE_7020 Unbound field in Source Qualifier

When running a session the session fails with the following error:

“TE_7020 Unbound field in Source Qualifier”

Solution
This error will occur when there is an inconsistency between the Source Qualifier and the source table.
Either there is a field in the Source Qualifier that is not in the physical table or there is a column of the source object that has no link to the corresponding port in the Source Qualifier.

  1. To resolve this, re-import the source definition into the Source Analyzer in Designer.
  2. Bring the new Source definition into the mapping.
    This will also re-create the Source Qualifier.
  3. Connect the new Source Qualifier to the rest of the mapping as before.