Understanding What Are The 3 Types Of Data Storage

Problem Overview

Large organizations face significant challenges in managing data across various storage types, including operational databases, data lakes, and archival systems. The complexity of data movement across these layers often leads to failures in lifecycle controls, breaks in data lineage, and divergence of archives from the system of record. Compliance and audit events can expose hidden gaps in data governance, necessitating a thorough understanding of how data is stored, retained, and accessed.

Mention of any specific tool, platform, or vendor is for illustrative purposes only and does not constitute compliance advice, engineering guidance, or a recommendation. Organizations must validate against internal policies, regulatory obligations, and platform documentation.

Expert Diagnostics: Why the System Fails

1. Data lineage often breaks when data is ingested into disparate systems, leading to challenges in tracking the origin and transformations of data.2. Retention policy drift can occur when policies are not uniformly enforced across different storage types, resulting in potential compliance risks.3. Interoperability constraints between systems can create data silos, complicating the retrieval and analysis of data across platforms.4. Temporal constraints, such as event dates and audit cycles, can misalign with retention policies, leading to premature data disposal or unnecessary retention.5. Cost and latency trade-offs are frequently overlooked, impacting the efficiency of data retrieval and storage management.

Strategic Paths to Resolution

1. Implement centralized data governance frameworks to ensure consistent retention policies across all storage types.2. Utilize data lineage tools to enhance visibility into data movement and transformations across systems.3. Establish clear lifecycle policies that align with compliance requirements and operational needs.4. Invest in interoperability solutions to bridge gaps between data silos and facilitate seamless data access.

Comparing Your Resolution Pathways

| Storage Type | Governance Strength | Cost Scaling | Policy Enforcement | Lineage Visibility | Portability (cloud/region) | AI/ML Readiness ||———————-|———————|————–|——————–|——————–|—————————-|——————|| Archive Patterns | Moderate | High | Low | Low | Moderate | Low || Lakehouse | High | Moderate | High | High | High | High || Object Store | Moderate | Low | Moderate | Moderate | High | Moderate || Compliance Platform | High | High | High | High | Low | Low |

Ingestion and Metadata Layer (Schema & Lineage)

Ingestion processes often encounter failure modes such as schema drift, where the structure of incoming data does not match existing schemas, leading to data integrity issues. For instance, a lineage_view may not accurately reflect the transformations applied to a dataset_id if the schema changes are not documented. Additionally, data silos can emerge when ingestion tools are not compatible across platforms, such as between a SaaS application and an on-premises ERP system. Variances in retention policies, such as differing retention_policy_id across systems, can further complicate lineage tracking.

Lifecycle and Compliance Layer (Retention & Audit)

Lifecycle management often fails due to misalignment between retention policies and actual data usage. For example, a compliance_event may reveal that data classified under a specific data_class is retained longer than necessary, violating established retention_policy_id. Temporal constraints, such as the event_date of data creation, can also misalign with audit cycles, leading to compliance risks. Data silos, such as those between operational databases and archival systems, can hinder the ability to conduct comprehensive audits, exposing gaps in governance.

Archive and Disposal Layer (Cost & Governance)

Archiving processes can diverge from the system of record due to inconsistent governance practices. For instance, an archive_object may not be disposed of in accordance with the defined retention_policy_id, leading to unnecessary storage costs. Interoperability constraints between archival systems and compliance platforms can further complicate the disposal process, as data may not be easily retrievable for audits. Additionally, temporal constraints, such as disposal windows, can be overlooked, resulting in prolonged retention of data that should have been purged.

Security and Access Control (Identity & Policy)

Access control mechanisms must be robust to prevent unauthorized access to sensitive data across various storage types. Policies governing access must align with the classification of data, such as data_class, to ensure compliance with internal and external regulations. Failure to enforce these policies can lead to security breaches and compliance violations, particularly when data is moved between systems with differing security protocols.

Decision Framework (Context not Advice)

Organizations should evaluate their data management practices against established frameworks that consider the unique context of their operations. Factors such as data lineage, retention policies, and compliance requirements must be assessed to identify potential gaps and areas for improvement. This evaluation should be ongoing, adapting to changes in technology and regulatory landscapes.

System Interoperability and Tooling Examples

Ingestion tools, catalogs, lineage engines, archive platforms, and compliance systems must effectively exchange artifacts such as retention_policy_id, lineage_view, and archive_object to maintain data integrity and compliance. However, interoperability challenges often arise, particularly when systems are not designed to communicate seamlessly. For example, a lineage engine may not capture changes made in an archive platform, leading to discrepancies in data tracking. For more information on enterprise lifecycle resources, visit Solix enterprise lifecycle resources.

What To Do Next (Self-Inventory Only)

Organizations should conduct a self-inventory of their data management practices, focusing on the effectiveness of their ingestion, retention, and archiving processes. This inventory should include an assessment of data lineage, compliance readiness, and the alignment of policies across systems.

FAQ (Complex Friction Points)

– What happens to lineage_view during decommissioning?- How does region_code affect retention_policy_id for cross-border workloads?- Why does compliance_event pressure disrupt archive_object disposal timelines?- What are the implications of schema drift on data integrity during ingestion?- How can organizations identify and mitigate data silos in their architecture?1. Legacy Application Centric Archives2. Lift and Shift Cloud Storage3. Policy Driven Archive Platform

Operational Landscape Expert Context

In my experience, the divergence between design documents and actual operational behavior is a common theme in enterprise data governance. For instance, I once encountered a situation where the architecture diagrams promised seamless data flow between ingestion and storage systems, yet the reality was starkly different. Upon auditing the logs, I discovered that data was frequently misrouted due to misconfigured job parameters, leading to significant data quality issues. This misalignment between documented expectations and operational reality highlighted a primary failure type: a process breakdown that stemmed from inadequate communication between teams responsible for implementation and those who designed the governance framework. The discrepancies in the logs revealed that the promised data integrity checks were not being executed as intended, resulting in orphaned records that were never addressed.

Lineage loss during handoffs between teams is another critical issue I have observed. In one instance, I found that governance information was transferred between platforms without essential timestamps or identifiers, which rendered the data lineage nearly impossible to trace. This became evident when I attempted to reconcile the data flows and found that key metadata was missing, leading to confusion about the data’s origin and its compliance status. The root cause of this issue was primarily a human shortcut, team members relied on informal communication rather than formal documentation practices. As a result, I had to undertake extensive reconciliation work, cross-referencing various logs and exports to piece together the lineage, which was a time-consuming and error-prone process.

Time pressure often exacerbates these issues, as I have seen firsthand during critical reporting cycles. In one particular case, a looming audit deadline forced teams to prioritize speed over thoroughness, leading to incomplete lineage documentation and gaps in the audit trail. I later reconstructed the history of the data by sifting through scattered exports, job logs, and change tickets, which were often poorly organized. This experience underscored the tradeoff between meeting tight deadlines and maintaining a defensible documentation quality. The shortcuts taken during this period resulted in significant challenges when trying to validate compliance, as the necessary records were either missing or fragmented.

Audit evidence and documentation lineage have consistently emerged as pain points across many of the estates I have worked with. Fragmented records, overwritten summaries, and unregistered copies made it exceedingly difficult to connect early design decisions to the later states of the data. For example, I frequently encountered situations where initial governance frameworks were not adequately reflected in the operational documentation, leading to confusion during audits. These observations reflect a broader trend I have seen, where the lack of cohesive documentation practices results in a fragmented understanding of data governance. The challenges I faced in these environments highlight the importance of maintaining a clear and consistent documentation strategy to ensure compliance and effective data management.

REF: NIST (2020)
Source overview: NIST Special Publication 800-53 Revision 5: Security and Privacy Controls for Information Systems and Organizations
NOTE: Provides a comprehensive framework for security and privacy controls, relevant to data governance and compliance mechanisms in enterprise environments, including data storage considerations.
https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final

Author:

Devin Howard I am a senior data governance strategist with over ten years of experience focusing on enterprise data governance and lifecycle management. I analyzed audit logs and designed retention schedules to address what are the 3 types of data storage, revealing issues like orphaned archives and incomplete audit trails. My work involved mapping data flows between ingestion and storage systems, ensuring compliance across operational and compliance records while coordinating with data and infrastructure teams.

DISCLAIMER: THE CONTENT, VIEWS, AND OPINIONS EXPRESSED IN THIS BLOG ARE SOLELY THOSE OF THE AUTHOR(S) AND DO NOT REFLECT THE OFFICIAL POLICY OR POSITION OF SOLIX TECHNOLOGIES, INC., ITS AFFILIATES, OR PARTNERS. THIS BLOG IS OPERATED INDEPENDENTLY AND IS NOT REVIEWED OR ENDORSED BY SOLIX TECHNOLOGIES, INC. IN AN OFFICIAL CAPACITY. ALL THIRD-PARTY TRADEMARKS, LOGOS, AND COPYRIGHTED MATERIALS REFERENCED HEREIN ARE THE PROPERTY OF THEIR RESPECTIVE OWNERS. ANY USE IS STRICTLY FOR IDENTIFICATION, COMMENTARY, OR EDUCATIONAL PURPOSES UNDER THE DOCTRINE OF FAIR USE (U.S. COPYRIGHT ACT § 107 AND INTERNATIONAL EQUIVALENTS). NO SPONSORSHIP, ENDORSEMENT, OR AFFILIATION WITH SOLIX TECHNOLOGIES, INC. IS IMPLIED. CONTENT IS PROVIDED "AS-IS" WITHOUT WARRANTIES OF ACCURACY, COMPLETENESS, OR FITNESS FOR ANY PURPOSE. SOLIX TECHNOLOGIES, INC. DISCLAIMS ALL LIABILITY FOR ACTIONS TAKEN BASED ON THIS MATERIAL. READERS ASSUME FULL RESPONSIBILITY FOR THEIR USE OF THIS INFORMATION. SOLIX RESPECTS INTELLECTUAL PROPERTY RIGHTS. TO SUBMIT A DMCA TAKEDOWN REQUEST, EMAIL INFO@SOLIX.COM WITH: (1) IDENTIFICATION OF THE WORK, (2) THE INFRINGING MATERIAL’S URL, (3) YOUR CONTACT DETAILS, AND (4) A STATEMENT OF GOOD FAITH. VALID CLAIMS WILL RECEIVE PROMPT ATTENTION. BY ACCESSING THIS BLOG, YOU AGREE TO THIS DISCLAIMER AND OUR TERMS OF USE. THIS AGREEMENT IS GOVERNED BY THE LAWS OF CALIFORNIA.