what-is-unstructured-data-and-how-does-it-relate-to-your-vmware-vsphere-environment

What if you could radically improve your search performance while drastically bettering and speeding your troubleshooting efforts? Unstructured data has the power to improve all of your physical, virtual, and cloud environments with powerful analytics, operational intelligence, and improved visibility across the organization. Here’s what unstructured data is and how it can improve your VMware vSphere environment.

Defining Unstructured Data

_unstructured data is everywhere - also in your VMware vSphere Environment

Photo courtesy of Rose Business Technologies

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Tremendous volumes of unstructured log data is generated during normal operations. But without some way to capture, store, and analyze it, it is useless to the organization and expensive to store, plus there is way too much to sift through by hand.

In order to understand unstructured data, you have to be able to visualize how it is different from structured data. Structured data is data that exists in a structured format, which is easy to store and retrieve in a typical, traditional database like SQL. Structured data includes data sets like raw performance metrics and capacity metrics. Unstructured data, on the other hand, is data that is not easily stored or retrieved using classic databases. It includes data sets like application logs, system logs.

The average VMware vSphere host (ESXi) generates 250 MB of log data per day. Microsoft Exchange deployments produce at least 1 GB of unstructured log data per day, quite often more. To put this into perspective, 1 GB of data is equivalent to 677,963 pages of text. There is definitely no shortage of log data available for analysis and improvements! But this amount of data is clearly too much for anyone to manually dig through, no matter how valuable it is.

How Unstructured Data Can Improve Your VMware Environment

Unstructured log data has a lot to offer the VMware developer. First, it assists with IT operations management, including better visibility, faster MTTR, and improved troubleshooting within the network, applications, databases, virtualized environments, and IT infrastructure. Unstructured log data can also free up IT team members’ time that is normally spent manually searching through log data, leaving them more time to spend on strategic projects the organization needs.

Unstructured data can lessen the amount of unexpected downtime by providing analytical data to be proactive about identifying and solving problems. This, in turn, improves the perception and satisfaction users have in the IT department. This data can also help in Root Cause Analysis and with improved ability to be compliant with relevant regulations. Once IT learns to leverage unstructured log data, they can avoid fines and restrictions that might be imposed by failed audits. Log data can be invaluable in adhering to internal and industry best practices, as well as mandatory compliance regulations and standards.

Unstructured log data can also be helpful in DevOps environments. It frees up the time developers normally have to spend troubleshooting latency issues and performance problems, allowing them to spend more time building new features and functionality into the apps the organization needs. Additionally, this data can speed the rate at which developers can release new software applications and helps avoid costly and frustrating delays that pop up in response to unanticipated issues during development and testing.

How VMware Developers Can Take Advantage of Unstructured Data

more log data to come

With log data and effective analytical tools, you can identify and troubleshoot problems proactively and respond to issues even before they become problems that lead to downtime.

For VMware environments, the ideal way to begin collecting, storing, analyzing, and benefiting from unstructured log data is vRealize Log Insight, Graylog or we offer log analysis also as an extension to Health Analyzer. It doesn’t even require mastering a new query language in order to use and benefit from it.

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Use Case - Tamper-resistant Clinical Trials

Goal:

Blockchain PoCs were unsuccessful due to complexity and lack of developers.

Still the goal of data immutability as well as client verification is a crucial. Furthermore, the system needs to be easy to use and operate (allowing backup, maintenance windows aso.).

Implementation:

immudb is running in different datacenters across the globe. All clinical trial information is stored in immudb either as transactions or the pdf documents as a whole.

Having that single source of truth with versioned, timestamped, and cryptographically verifiable records, enables a whole new way of transparency and trust.

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Goal:

Store the source data, the decision and the rule base for financial support from governments timestamped, verifiable.

A very important functionality is the ability to compare the historic decision (based on the past rulebase) with the rulebase at a different date. Fully cryptographic verifiable Time Travel queries are required to be able to achieve that comparison.

Implementation:

While the source data, rulebase and the documented decision are stored in verifiable Blobs in immudb, the transaction is stored using the relational layer of immudb.

That allows the use of immudb’s time travel capabilities to retrieve verified historic data and recalculate with the most recent rulebase.

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No matter if it’s an eCommerce platform or NFT marketplace, the goals are similar:

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  • prevent overwrite or updates on transactions
  • comply with regulations (PCI, GDPR, …)


Implementation:

immudb is typically scaled out using Hyperscaler (i. e. AWS, Google Cloud, Microsoft Azure) distributed across the Globe. Auditors are also distributed to track the verification proof over time. Additionally, the shop or marketplace applications store immudb cryptographic state information. That high level of integrity and tamper-evidence while maintaining a very high transaction speed is key for companies to chose immudb.

Use Case - IoT Sensor Data

Goal:

IoT sensor data received by devices collecting environment data needs to be stored locally in a cryptographically verifiable manner until the data is transferred to a central datacenter. The data integrity needs to be verifiable at any given point in time and while in transit.

Implementation:

immudb runs embedded on the IoT device itself and is consistently audited by external probes. The data transfer to audit is minimal and works even with minimum bandwidth and unreliable connections.

Whenever the IoT devices are connected to a high bandwidth, the data transfer happens to a data center (large immudb deployment) and the source and destination date integrity is fully verified.

Use Case - DevOps Evidence

Goal:

CI/CD and application build logs need to be stored auditable and tamper-evident.
A very high Performance is required as the system should not slow down any build process.
Scalability is key as billions of artifacts are expected within the next years.
Next to a possibility of integrity validation, data needs to be retrievable by pipeline job id or digital asset checksum.

Implementation:

As part of the CI/CD audit functionality, data is stored within immudb using the Key/Value functionality. Key is either the CI/CD job id (i. e. Jenkins or GitLab) or the checksum of the resulting build or container image.

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