Configuration Manager

Network Configuration: a new source of observability

Have you ever considered Network Configuration Observability? For years, network observability has revolved around a familiar set of data sources: metrics, logs, events, traces, SNMP polling, streaming telemetry, and network traffic analysis. Each of these contributes a different perspective on the health and behavior of the infrastructure. 

But an interesting question is beginning to emerge:

How far can observability go?

Increasingly, organizations are asking not only how to collect more telemetry, but how to leverage every piece of operational information available across their infrastructure. The objective is no longer to monitor individual devices; it is to understand the complete state of the network from every possible angle. Each second the network is not performing properly represents a significant cost to the company

The trend is clear: modern observability platforms are expected to ingest and correlate virtually any log, metric, event, or operational dataset generated by any vendor across the network.

From Monitoring Platform to Unified Operations Platform

As Viewtinet has evolved over the years, many of our customers have gradually replaced traditional monitoring tools and standalone Network Management Systems (NMS) with a single unified observability platform. The goal has never been simply to consolidate dashboards. Instead, it has been to provide a single platform capable of collecting, analyzing, correlating, and understanding every relevant source of operational data.

This philosophy recently led us to introduce Network Configuration Manager, extending Viewtinet beyond traditional observability while maintaining the same vision: one platform for monitoring, management, analytics, and AI-driven operations.

Configuration Manager

Network Configuration Management Is Nothing New

The capabilities of a Network Configuration Manager are well established.

Network administrators rely on these platforms to:

  • Automatically back up device configurations
  • Restore previous configurations
  • Deploy configuration changes
  • Compare configuration versions
  • Track configuration history
  • Detect unauthorized modifications
  • Support compliance and auditing processes

These capabilities remain essential for day-to-day network operations. However, the real opportunity lies elsewhere.

Configuration Data Is Also Observability Data

Once configuration management becomes part of a unified observability platform, network configurations stop being just administrative files. They become another operational data source.

Just as logs describe what happened, metrics describe performance, and traffic analysis explains network behavior, configurations describe how the infrastructure is intended to operate. This contextual information becomes incredibly valuable when analyzed alongside every other telemetry source. A configuration change is no longer an isolated administrative event. It becomes another observable event that can be correlated with alarms, performance degradation, application behavior, traffic anomalies, and infrastructure events.

Configuration manager for observability

AI Needs Context

One of the biggest challenges for AI-powered observability is not building better algorithms.

It is providing AI with sufficient context.

A machine learning model or a Large Language Model can only reason about the information available to it. The richer and more diverse the available datasets, the more accurate the conclusions become.

This is precisely where configuration data plays a critical role.

Within Viewtinet, AI-driven Root Cause Analysis continuously analyzes all available information sources—including metrics, logs, events, topology, network traffic, and now configuration history.

When a network issue appears shortly after a configuration modification, the platform can automatically correlate both events without requiring manual investigation.

Instead of an engineer spending hours asking questions such as:

  • What changed?
  • Who modified this router?
  • Was a firewall policy updated?
  • Did someone alter the routing configuration yesterday?

The platform can identify those relationships automatically as part of the RCA process. Configuration history becomes evidence. Not just documentation.

AI network configuration RCA observability

From Management Feature to Intelligence Source

Interestingly, Network Configuration Manager was initially introduced to address traditional NMS use cases. Indeed, backups, version control, compliance, and configuration deployment remain indispensable capabilities.

But once configuration data is integrated into a multidimensional observability platform, its value changes completely: it becomes another dimension of observability. Another source of evidence. Another dataset that AI can correlate with thousands of other signals to understand not only what happened, but why it happened.

As observability continues to evolve, the boundary between monitoring and management is becoming increasingly blurred. Every operational dataset has the potential to contribute to a better understanding of the infrastructure. Configuration files are no exception.  They are no longer just backups waiting to be restored. They have become one of the most valuable sources of contextual information for modern AI-driven observability.

ViewtiAI Root Cause Analysis

How Artificial Intelligence Observability Is Transforming ITOps

 

Introduction. Why AI Is Essential for Observability Today

Observability has evolved far beyond the traditional concept of monitoring. While monitoring focuses on collecting metrics and reporting system health, observability seeks to understand the why and how behind issues across the IT and IoT ecosystem — especially at scale. With the proliferation of cloud-native architecture, SD-WAN, cybersecurity solutions, collaboration software, distributed systems, and AI-driven applications, the volume and complexity of telemetry data have exploded, making manual analysis increasingly untenable.

This is where Artificial Intelligence Observability becomes a strategic imperative — not just a technology trend. The shift from monitoring to observability has already helped organizations gain proactivity, improve productivity, and significantly reduce MTTR. Upgrading to AI-powered observability closes the loop for highly efficient ITOps, empowering DevOps, SRE, and IT operations teams to detect, diagnose, and remediate issues faster and with greater confidence.

 

The Limitations of Traditional Monitoring and the Rise of Artificial Intelligence Observability

Traditional monitoring tools are valuable for tracking individual metrics or thresholds, but they often fall short in complex environments. Monitoring answers what is happening; observability answers why it is happening.

In contrast, AI observability employs machine learning (ML), deep learning (DL), and advanced analytics (including LLM-based processing) to go beyond static dashboards and alert thresholds. It enables systems to automatically correlate data across sources (metrics, logs, events, traces, and network traffic), detect anomalies, and infer root causes with high confidence.

This shift reduces time-to-resolution, minimizes manual investigation, and enables teams to operate with higher reliability, resilience, and operational intelligence.

Artificial Intelligence anomaly detection

Key Benefits of AI-Powered Observability

Intelligent Correlation Across Diverse Data Sources

One of the core strengths of AI in observability is its ability to intelligently correlate all available logs and metrics to reveal relationships that would be nearly impossible to identify manually. This correlation is essential for isolating root causes and is a key differentiator between observability and traditional monitoring.

Explainability and Actionable Insights

AI observability is not only about identifying anomalies; it’s about understanding them. Modern AI observability tools provide explanations in natural language and contextual insights that help teams grasp why an issue occurred, what it impacts, and how to resolve it. This approach bridges the gap between raw telemetry and human decision-making, making observability outputs more consumable and actionable.

Real-Time Detection, Prediction, and Adaptive Intelligence

AI models excel in pattern recognition and predictive analysis. Instead of waiting for thresholds to be breached, AI can detect subtle behavioral deviations and forecast future performance degradations before they impact users. This capability enhances operational resilience and enables proactive remediation.

Understandable For Everyone

The IT ecosystem is evolving quickly. There are more and more technologies, vendors, and tool stacks to manage, and it is increasingly difficult for IT teams to stay ahead of this complexity. By providing LLM-based interfaces and an AI Copilot, the artificial intelligence observability platform enables any user — not just experts — to understand issues, generate reports in natural language, and obtain meaningful insights without needing to know where the data comes from or how it is collected.

 

The Technical Challenges of AI-Driven Observability

While AI brings significant value, it also introduces new architectural and computational challenges:

High-Volume Telemetry and Data Ingestion

Modern observability platforms ingest enormous volumes of telemetry data — from logs and metrics to network traffic and API events. Aggregating and processing this data in real time without overwhelming network bandwidth or storage infrastructure requires efficient data pipelines, intelligent sampling, and edge processing.

Computational Overhead

Running advanced analytics, deep learning models, and LLM-based reasoning at scale requires robust compute resources. Balancing latency, accuracy, and cost is critical, especially when processing high-throughput streams of telemetry.

Explainability and Transparency

As models grow more complex, achieving explainability becomes increasingly important. Observability solutions must not only detect issues but also translate insights into human-understandable explanations that operational teams can trust.

Data Governance and Compliance

Observability systems often handle sensitive telemetry. Ensuring data privacy, compliance, and secure handling is essential, particularly in regulated industries.

 

How Viewtinet Is Leading with AI Observability

Viewtinet approaches AI observability with a comprehensive strategy that addresses these challenges head-on — combining advanced analytics with practical operational intelligence.

Intelligent Correlation and Root-Cause Analysis

Viewtinet’s platform leverages AI to correlate metrics, logs, network traffic, and event data to identify root causes automatically. Intelligent correlation helps teams detect not just that a problem occurred, but why it did — and what the underlying drivers are.

Natural Language Interaction and Explainability

By integrating large language models directly into the analytics pipeline, Viewtinet enables users to interact with the observability platform using natural language prompts. Teams can ask questions, query insights, or request troubleshooting guidance in plain English, receiving explanations that are both contextual and human-readable.

This capability enhances collaboration between technical and non-technical stakeholders and accelerates problem resolution.

Autonomous Dashboards and KPI Discovery

Viewtinet’s AI engine automatically generates dashboards, KPIs, and analytic views without requiring manual configuration of data sources or ETL rules. The system identifies relevant datasets, models them, and creates observability content in seconds — dramatically reducing time-to-value.

Scalable, Real-Time Performance Monitoring

By processing telemetry close to the source and optimizing data ingestion, Viewtinet ensures that insights are delivered in real time without overwhelming network or compute resources. This architecture supports high-velocity environments and large enterprise deployments.

Copilot artificial intelligence observability

Conclusions: Future-Ready Observability with Artificial Intelligence

AI is no longer a nice-to-have in observability — it’s a strategic requirement for modern IT operations. Intelligent correlation, explainability, predictive insights, and automated analytics empower teams to manage increasingly complex infrastructures with confidence.

Organizations that leverage artificial intelligence observability gain:

  • Faster time-to-resolution for incidents
  • More relevant, contextual insights
  • Proactive detection and prediction capabilities
  • Reduced operational noise and human overhead
  • Greater alignment between IT performance and business outcomes

Platforms like Viewtinet (ViewtiAI) are at the forefront of this transformation, delivering end-to-end AI observability that addresses both the technical complexity of large-scale data and the business need for clarity and actionability.

Why Observability Is a Trending Topic in Today’s IT Ecosystem

From Monitoring to Observability

For decades, IT teams have relied on monitoring tools to keep systems running. Monitoring answers predefined questions: Is the server up or down? Is CPU usage above 80%? Is a specific application reachable? This approach works when infrastructures are relatively static and predictable.

But the current IT landscape is anything but static. Hybrid clouds, microservices, SaaS, IoT, and edge computing have multiplied dependencies and accelerated change. In this dynamic environment, monitoring shows symptoms—but often fails to explain the causes.

Observability goes a step further. It is not about predefined thresholds, but about providing the context to answer unknown questions. It leverages logs, metrics, traces, and network data to correlate events and detect patterns that monitoring tools miss. In practice, this means that IT teams can move from reactive firefighting to proactive optimization.

Why observability matters now

Several converging factors explain why observability has become a trending priority:

  1. Complexity of IT ecosystems
    Multi-cloud environments, distributed applications, and containerized microservices make dependencies hard to map. Observability creates a unified view.

  2. Cybersecurity and compliance
    With cyber threats increasing in scale and sophistication, enriched telemetry is vital to detect anomalies and reduce mean time to detect (MTTD).

  3. User experience as a KPI
    Digital services are directly tied to customer loyalty and revenue. It ensures end-to-end visibility into applications, networks, and services, enabling better user experience.

  4. AIOps and automation
    Observability platforms generate the granular, contextual data that AI and ML engines need to predict failures, detect root causes, and automate remediation.

 

The Business Benefits

Organizations that adopt this technology achieve:

  • Faster troubleshooting – Identify root causes in minutes, not hours.
  • Operational efficiency – Reduce tool sprawl by unifying data sources.
  • Better decision-making – Gain actionable insights that go beyond alerts.
  • Cost optimization – Improve resource usage and reduce downtime.

 

 

Moving Forward with Unified Observability

Observability is trending because it reflects today’s reality: IT is more complex, interconnected, and business-critical than ever. Monitoring can tell you that something is wrong; observability explains what, where, and why—and even predicts issues before they occur.

At the same time, some organizations hesitate to move away from their legacy monitoring tools because those platforms also bundle network management functionalities such as autodiscovery, inventory, and configuration management. This is a valid concern—yet with Viewtinet, there is no trade-off.

Viewtinet’s Unified Observability platform combines the best of both worlds:

  • Network Management System capabilities (autodiscovery, inventory, configuration management)
  • Full observability (logs, metrics, traces, and network traffic analytics)

Its multitenant architecture allows Managed Service Providers (MSPs) to deliver observability in a SaaS model, while large enterprises can deploy it on-premise to retain complete data control. By unifying traffic analysis, log management, metrics, NMS features, and advanced analytics into a single solution, Viewtinet enables organizations to modernize without compromise—embracing observability while preserving and enhancing the functionalities they already rely on.

 

Observability

switching monitoring to observability

Switching from DX Spectrum to Viewtinet

As enterprises modernize their IT operations, the need to move away from legacy tools becomes critical. If you’re still using DX Spectrum, it’s time to consider a transition to a more modern, unified observability platform. Switching from DX Spectrum to Viewtinet enables greater visibility, flexibility, and performance in your monitoring ecosystem. In this post, we’ll walk you through the benefits of the switch and outline the key migration steps.

switching monitoring to observability

Why Switch from DX Spectrum to Viewtinet?

DX Spectrum has served many organizations well for traditional ICMP and SNMP-based monitoring. However, its aging architecture and limited integration capabilities make it less suited for modern IT environments that require:

– Unified observability across diverse systems and data sources (including SD-WAN, MS-Teams, etc.)

– Enhanced dashboard customization, historical, and real-time visualizations

– Advanced alarm management with flexible notification policies

– Integration with CMDBs and inventory systems

– Streamlined device onboarding and configuration

Viewtinet offers all this and more in a single, modern platform. With native support for SNMP, ICMP, Syslog, NetFlow, Call Detail Records, WMI, webhooks, and a wide range of APIs, Viewtinet consolidates your monitoring and log analytics into one unified view. You just need Viewtilog for data integration and Viewtisight for data visualization.

 

Migration Steps: From Legacy to Unified Observability. Switching from DX Spectrum to Viewtinet

Migrating from DX Spectrum to Viewtinet can be done smoothly by following a well-structured process. Below are the main steps to ensure a successful transition:

1. Export Devices from DX Spectrum

Start by exporting the complete list of monitored devices from DX Spectrum. This forms the base inventory for the migration.

 

2. Validate Device Coverage from PM/UIM

If you’re using Performance Management (PM) or Unified Infrastructure Management (UIM), validate that all devices managed there are also present in Spectrum. Export those lists as well to ensure full visibility.

 

3. Export SNMP Credentials

Extract the SNMP community strings or v3 credentials from Spectrum. These will be reused in Viewtinet for device polling and must be validated during onboarding.

 

4. Analyze the Monitoring Configuration

Review the collected data to understand:

– Polling Method per Device: Determine if each device uses SNMP, ICMP, or both.

– Custom Configurations: Identify special configurations or thresholds applied in Spectrum.

– Model Types and SysObjectIDs: Validate support for all device types in Viewtinet. If any device is unsupported, a custom plugin can be created.

– Alarm Notifier Settings: Check alarm policies and notification rules to replicate or improve them in Viewtinet.

– Dashboards in PM/UIM: Review existing dashboards to plan a better, more interactive experience in Viewtinet.

– Asset Fields: If asset metadata is defined in Spectrum, plan to map these fields into Viewtinet’s inventory. If not, consider integrating with a CMDB or importing asset information from an external file.

 

5. Verify Network Connectivity

Ensure that Viewtinet has proper network access to all devices to be monitored. This may require configuring firewall rules or routing changes.

 

6. Register Devices in Viewtinet

Onboard devices into Viewtinet using the validated inventory and credentials. Devices can be grouped and enriched with asset metadata during this phase.

 

7. Build Custom Dashboards and Alarms

Finally, create tailored dashboards and alarm policies based on the customer’s operational needs. Leverage Viewtinet’s powerful visualization tools to improve monitoring efficiency and incident response.

Final Thoughts about Switching from DX Spectrum to Viewtinet

Switching from DX Spectrum to Viewtinet is not just a technical migration — it’s a strategic move towards unified observability. The process ensures better integration, deeper insights, and a future-proof platform designed to adapt to evolving IT environments.

Whether you’re a service integrator or a large enterprise, Viewtinet makes it easy to modernize your monitoring stack while maintaining business continuity throughout the migration.

Need help with your transition plan? Contact us to get started.

Switching from DX Spectrum to Viewtinet

Unified Observability: The New Key to Monitoring and Cybersecurity

The growing complexity of IT infrastructures has driven a clear trend: the adoption of unified observability solutions. This approach, which integrates the monitoring of all network components and applications into a single platform, is revolutionizing how organizations manage their technological infrastructure.

Unified observability represents a key opportunity for integrators looking to offer their customers advanced solutions. Traditional monitoring solutions often cannot integrate logs and metrics from SD-WAN environments, MS Teams, and many other solutions that export data through complex APIs, Syslog, and other formats.

Simplifying Infrastructure Monitoring with Unified Observability

One of the greatest benefits of unified observability is the ability to monitor all infrastructure elements and their logs from a single platform. Traditionally, organizations have used multiple tools to supervise each part of their environment: networks, applications, databases, storage systems, etc. This fragmented approach complicates management, increases costs, and reduces overall visibility.

With a unified platform, IT and cybersecurity teams have access to a comprehensive view of all critical events and data without needing to switch between multiple interfaces. This centralization not only reduces the risk of human error but also allows for faster and more efficient anomaly detection, improving response capabilities.

syslog and network maps for unified observability

Enhanced Cybersecurity with Unified Observability

Unified observability not only simplifies monitoring but also provides added value in the field of cybersecurity. A platform like Viewtinet integrates all logs generated by the network, systems, and applications, enabling advanced analytics to identify suspicious patterns. This makes it easier to detect potential threats before they escalate. It allows for high-level and low-level dashboards, automated reports, event correlation, and MS Teams notifications, among many other options.

 

Reducing the Load on SIEMs

Moreover, an observability solution can act as an intelligent filter for the SIEM. It collects all logs but only forwards to the SIEM those that meet specific correlation rules. This reduces the load on the SIEM, improving its performance, decreasing licensing costs, and extending log retention periods.

 

The Rise of the Head of Observability

The message resonates so strongly in large enterprises that it has led them to create a new position in their organizational charts: the Head of Observability. Their main mission is to reduce the number of existing log analysis and monitoring tools and gain greater visibility of all new critical services. These new leaders are betting on Viewtinet + APM + OpenTelemetry as the perfect combination to cover 100% of their needs. In some cases, multinationals have managed to reduce 15 different tools to just 2 or 3.

 

A Growing Market Opportunity for Integrators

For integrators looking to capitalize on this trend, the market is full of opportunities. More and more companies, especially those with complex infrastructures, are looking for solutions that allow them to consolidate and optimize their operations. The growing demand for unified observability platforms is driving an increase in the number of projects.

 

Conclusion: The Future of IT Monitoring

Viewtinet Unified Observability is transforming the way companies monitor their IT and IoT environments. By centralizing monitoring tools and optimizing cybersecurity log management, organizations can reduce costs, simplify their ecosystem, and improve operational efficiency. Companies that adopt this approach will be better prepared to face current and future challenges.

monitoring and alarms unified observability
Syslog dashboards

Syslog Data

Syslog is a widely used standard protocol for logging system events, errors, and other messages in computer systems. Syslog data can be incredibly valuable for detecting security breaches, identifying system errors, and tracking down performance issues. However, working with syslog data can be challenging, especially when dealing with large amounts of data. In this blog article, we’ll explore the two main ways to work with syslog data: parsing the data and working with the raw data.

syslog man

Parsing the syslog data

One way to work with syslog data is to parse it and create dashboards with real-time analytics. Parsing the data involves breaking it down into its constituent parts, such as the timestamp, source IP address, message type, and severity level. This parsed data can then be stored in a database, and visualized in dashboards using tools like Viewtinet.

The benefits of parsing syslog data include:

  1. Real-time analytics: Dashboards can be created that provide real-time insights into system performance, security breaches, and other critical events.

  2. Better searchability: Parsed data can be easily searched and filtered, making it easier to identify specific events or patterns in the data.

  3. Reduced storage costs: Parsing data can often result in a smaller data footprint, which can help reduce storage costs.

Syslog dashboards

Working with syslog raw data

Another way to work with syslog data is to work with the raw data. In this case, the analytics tool needs to understand the different available values per log. Raw data is the unprocessed data that comes directly from the syslog source, without any parsing or manipulation.

The benefits of working with raw syslog data include:

  1. More flexibility: Raw data can be transformed and analyzed in a variety of ways, depending on the needs of the organization. This flexibility allows for more customized analytics and reporting.

  2. Deeper insights: Working with raw data allows for deeper insights into system performance, security breaches, and other critical events. This is because raw data can provide more detailed information than parsed data.

  3. More comprehensive coverage: Working with raw data can help organizations capture and analyze all available data, rather than just a subset of parsed data.

Raw syslog data

Which approach is best for syslog?

Deciding which approach to use depends on the needs of the organization. If real-time analytics and reduced storage costs are important, then parsing the data may be the best option. On the other hand, if flexibility and deep insights are more important, then working with the raw data may be the better choice.

Ultimately, it’s important to have a solid understanding of the organization’s needs and goals when working with syslog. By doing so, organizations can make more informed decisions about how to best work with their syslog data and achieve their desired outcomes.

 

Despite this, there may be some environments where the amount and complexity of data are so vast that the query response time may be too long. For these cases, the index logging will solve any performance issue. As a result of the indexing, you might not be able to drill down or follow different post-analysis troubleshooting. Nevertheless, if you already set the parameters that you want to analyze and correlate, index logging is the best method to get real-time results.

Using Viewtinet for syslog

The vision of Viewtinet has always been to create a flexible platform suitable for several and different use cases. Having said that, why limiting to one method when you can have both at the same time?

Viewtinet proposes an end-to-end solution. It is capable of integrating the data, store it and provide full analytics and observability. Thanks to the visual smart data broker, Viewtinet can integrate and parse syslog data from any vendor.

However, depending on the vendor and type of syslog data, or even if the company needs to keep the raw syslog data for regulatory reasons, Viewtinet can also work in this format. It has an intelligent layer to identify the different keys and we can select a key to identifiy the logs that content that key, and then filter by the available values.

Viewtinet chosen as a 2021 Top 100 Startup Winner

As 2021 comes to an end, it is time to look backward and analyze the year earns. We all started the year with optimism after the unusual previous 2020 but with a lot of doubts about how the pandemic was going to behave. We gave total freedom to work from home and we preferred to avoid face-to-face meetings and travels. This could be a handicap for a startup but the conclusion is that we have to be very proud of our team. The product improvements have been outstanding and the amount and size of companies that are trusting Viewtinet’s team and technology are increasing.

All this effort and work has had a very nice surprise before the year ends: Red Herring has chosen Viewtinet as a 2021 Top 100 winner in Europe.

Press Release

December 16th, 2021; – After much consideration, the Red Herring judging panel has announced its Top 100 Europe winners this evening, recognizing Europe’s leading private companies and celebrating these startups’ innovations and technologies across their respective industries.

Red Herring Top 100 Europe enlists outstanding entrepreneurs and promising companies. It selects the award winners from approximately 1,200 privately financed companies each year in the European Region. Since 1996, Red Herring has kept tabs on these up-and-comers. Red Herring’s Top 100 list has become a mark of distinction for identifying promising new companies and entrepreneurs. Red Herring’s editors were among the first to recognize that companies such as Alibaba, Facebook, Google, SalesForce.com, Skype, SuperCell, Spotify, Twitter, and YouTube would change the way we live and work. Thousands of the most interesting and innovative companies have graced the Top 100 list over the years.

“In 2021, selecting the top achievers was by no means a small feat,” said Alex Vieux, publisher and CEO of Red Herring. “In fact, we had the toughest time in years because so many entrepreneurs had crossed significant milestones so early in the European tech ecosystem. But after much thought, rigorous contemplation and discussion, we narrowed our list down from hundreds of candidates from across Europe to the Top 100 Winners. We believe Viewtinet embodies the vision, drive and innovation that define a successful entrepreneurial venture. Viewtinet should be proud of its accomplishment, as the competition was very strong.”

Red Herring’s editorial staff evaluated the companies on both quantitative and qualitative criteria, such as financial performance, technological innovation, management quality, overall business strategy and market penetration. This assessment was complemented by a review of the track records and standings of similar startups in the same verticals, allowing Red Herring to see past the “hype” and make the list a valuable instrument of discovery and advocacy for the most promising new business models in Europe.

Following Viewtinet’s well-deserved win, they are then invited to showcase their company to the US market at the Top 100 North America event and compete internationally for the Top 100 Global in October. Red Herring is dedicated to following Viewtinet’s path to further success and innovation.

Dashboards

Index logging versus index-free logging

When searching for a log management solution, some providers are claiming the advantages of index logging, and others claiming the advantages of index-free logging. Which one is the best? In my opinion, it depends on different factors. In this article, I will try to bring some light to this.

 

Index logging

With this method, the data is being processed upfront. It creates different tables that combine different indexing options. As a result, when querying the database, the result will appear instantly. Moreover, even when managing a vast amount of data, the index-logging leads to instant query results.

Certainly, this method will induce a high-speed response. However, we must take into consideration that this upfront processing needs more CPU & RAM from the server. In addition, the servers need more disk space because of the tables created by the indexing. Also, you need to define upfront the searchable elements and/or correlations.

Index-free logging

In contrast, with index-free logging, the data is stored as a table on the fly. The ingestion process is quicker and does not consume additional CPU, RAM, or disk space. Furthermore, there are several mechanisms to speed up the queries results: containers, data compression & data aggregation. On the whole, thanks to the current state of the art, this method can provide real-time responses to heavy queries.

Undoubtedly, the results will not appear at the same speed as index logging, but still, it seems likely that this experience is enough in most cases. Moreover, everything is searchable.

Dashboards

Conclusion

I’d say that depending on the use cases, the complexity of the data sets, and their sizes, the optimal approach could be one or the other.

Evidently, index-free logging is less costly in terms of hardware. Not only it can ingest data faster with fewer resources but also unindexed tables can provide deeper analytics procedures. What I mean is that when you are investigating an issue, with unindexed tables you can drill down by any of the available dimensions.  So, if you are analyzing stored data, it seems like the best approach.

Despite this, there may be some environments where the amount and complexity of data are so vast that the query response time may be too long. For these cases, the index logging will solve any performance issue. As a result of the indexing, you might not be able to drill down or follow different post-analysis troubleshooting. Nevertheless, if you already set the parameters that you want to analyze and correlate, index logging is the best method to get real-time results.

The Viewtinet approach

The vision of Viewtinet has always been to create a flexible platform suitable for several and different use cases. Having said that, why limiting to one logging method when you can have both?

The Viewtinet style is to be easy to use and intuitive. Viewtinet has already integrations with most of the IT data sources in the market. The templates in the Visual Smart Data Broker have already taken into consideration the optimal logging method. So from the user experience, Viewtinet is already providing the most efficient configuration.

With new or custom data sources, what Viewtinet does by default is index-free logging. This way, the system administrators can benefit from having all the data stored, to create any report, dashboard, and to drill down. After that, in case a report or several reports appear with some delay, with the visual interface, you can click the indexing option to switch from index-free logging to index-logging.

This way, you may not know upfront how you are going to analyze the data. You will have all the possibilities. Then, once you design your dashboard you can optimize the performance by indexing specific reports if needed.

QoS Traffic Shaping IT Analytics

Viewtinet launches VN-1000-Smart Analytics & Control

I believe that it is difficult today to find the best vendor that matches the specific IT needs of an organization. There are several specialized vendors in each of the segments, but of course, the budget is never unlimited.  Certainly, one IT manager always wants to get the most recommended and best-positioned supplier to be backed up. However, the reality is that usually these technologies take advantage of their position and are far away from the budget.

 

At Viewtinet, we were thinking about the best way to help the companies with the different technologies that we provide, and how we could provide different solutions pretty much demanded. As a result, we developed the idea of providing most of our modules running together under the same appliance. This is how Viewtinet provides a packed IT observability and control platform, easy to deploy and with lower TCO.

QoS Traffic Shaping IT Analytics

Multiple functionalities, one single appliance

 

Viewtinet provides a very unique architecture. Its Smart Analytics tool, together with the smart data broker, provides a single interface for the entire Viewtinet ecosystem. As it happens, from the same GUI we will be able to manage all the log data, wire data, and to provide traffic control and optimization. Not only it is flexible and customizable to handle these different products but also very easy to use.

 

This device, the VN-1000-Smart Analytics and Control, includes the following features:

  • Log Data: integration and analytics of every IT data source. Includes SNMP, Netflow, Syslog, call detail records, etc.
  • Wire data: IP traffic analysis. Application recognition, network KPIs, QoE measurements, IP packet sniffer.
  • Traffic control and optimization: traffic shaping, QoS per application, subnet, etc.

 

Overall, Viewtinet is positioning a device that covers different technologies: network monitoring, analytics, IP probe, traffic sniffer, bandwidth management platforms, etc.  For this reason, it will significantly reduce operational costs, improve productivity, and reduce downtimes.

Network Observability with Viewtinet

Easy deployment, with passive bypass

 

The VN-1000-Smart Analytics and control are delivered with a passive bypass to avoid adding a pain point in the network. There are different versions of the platform to match the required number and types of interfaces.

The bypass is physically connected inline of the network. The VN-1000-SAC is connected both to the bypass (to analyze and control de traffic) and to the management network (to receive the logs and registers from the different IT actives of the network).

QoS IP Sniffer deployment

Conclusion

 

Many companies are avoiding the jump to IT observability due to its implementation costs. Other companies are searching for QoS vendors alternatives to substitute the end-of-life devices that are already in place (i.e. Packet Shaper). Many IT managers have also given up searching for affordable IP Sniffers. What Viewtinet is providing is a very unique platform that covers all these common needs, at a significantly lower TCO than acquiring these technologies separately.

Wire Data Analytics and Traffic Optimization

Wire data analytics AND Traffic Optimization

Given the evolution of communication networks, their administration and supervision tasks usually lead the responsible team to work reactively, trying to solve application performance problems that have been reported by users.

It is not easy in a distributed environment, with applications hosted in the private cloud, and others in the public cloud, to be able to identify where the problem is. Is it in any of the elements that are part of the communication chain? Is it in the SaaS provider? Is it in my communications service provider? Is it a problem at the network level, internal or in the Internet exit?

Consequently, many organizations have adopted Wire Data solutions. This consists of network probes capable of identifying all applications using DPI (deep packet inspection), measuring their performance, and providing metrics of their quality of experience. Once the behavior of the network at level 7 is understood, we should move to the next level: if we identify network issues, latencies, bottlenecks, or low quality of experience, we must be able to act and improve the network performance

In the first place, it is vitally important to have an application control and management system. In order to avoid bottlenecks, it is necessary to grant different quality of service policies to different applications according to their nature. What’s more, nowadays, given the new scenario caused by the Covid-19, there is a need to ensure that all video call applications have the necessary assured bandwidth to operate in real-time. Similarly, if there is saturation in the network, it is convenient to decrease the priority of other less critical flows such as email applications, system updates, etc. and even block unwanted traffic on the network such as Thor, Netflix, etc. All these functionalities can be achieved with Viewtify QoS, the latest generation of bandwidth management products.

Secondly, part of the internet’s success is due to the TCP protocol. However, in its initial design, it was not taken into account how the networks were going to be in the future. As a result, the TCP protocol is very conservative answering to discards, high latency or congestion, causing performance limitations. Until now, optimization solutions were understood to improve WAN performance and required one instance at each of the sides of the communication. This is because the approach was to tunnel the traffic in a different protocol. Therefore, this kind of implementations leads to extremely complex and expensive projects.

Viewtinet, with Viewtify TCPO, provides a disruptive technology, that allows optimizing TCP flows from a single point of communication. It does not break the TCP protocol, making it work even for HTTPS. That is why in environments where all users are connected by Wi-Fi, such as hotels and universities, it is turning out to be a tremendously effective technology, optimizing up to 200% the performance of many flows.

Taking all of the above into account, Viewtinet proposes a unique solution where, from a single appliance, all these needs can be met:

  • Network probe, Viewtimon, capable of identifying applications, providing network KPIs, and measuring the quality of experience. Besides, it can work as a sniffer to capture packets continuously 24×7.

 

  • Bandwidth manager, Viewtify QoS, to grant different priorities and quality of service rules to guarantee the efficiency of critical applications.

 

  • TCP optimization, Viewtify TCPO, capable of accelerating all TCP flows, increasing flow speed, and improving the quality of experience.

 

Now that PacketShaper has been discontinued, Viewtinet provides the most complete alternative, with advanced network analytics, intuitive management based in HTTPS with unlimited policy levels for QoS, and TCP optimization, everything under the same appliance.