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Day: July 10, 2024

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ONE Data Lake & Splunk: Revolutionizing Network Data Analytics – Part 1

In February, we introduced the ONE Data Lake as part of our ONES 2.1 release, highlighting its integration capabilities with Splunk and AWS. In this blog post, we’ll delve into how the Data Lake integrates specifically with Splunk.

A data lake serves as a centralized storage facility capable of accommodating large quantities of structured, semi-structured, and unstructured data on a significant scale.These are typically built using scalable distributed cloud-based storage systems, such as Amazon S3, Azure Data Lake Storage, or Google Cloud Storage.

A pivotal benefit of a data lake lies in its capacity to handle substantial amounts of data from diverse origins, offering a cohesive storage solution conducive to data exploration, analytics, and informed decision-making processes.

Aviz ONE-Data Lake functions as a platform facilitating the migration of on-premises network data to cloud storage. It encompasses metrics that capture operational data across the network’s control plane, data plane, system, platform, and traffic. Serving as an upgraded iteration of Aviz Open Networking Enterprise Suite (ONES), ONE-Data Lake stores the metrics previously utilized in ONES onto the cloud.

Why Splunk?

Splunk is highly significant for organizations across diverse industries for multiple reasons:
  • 1. Operational Insight:
Splunk empowers organizations to obtain immediate insights from their operational data, facilitating the monitoring of system and application health and performance. This capability aids in promptly identifying and addressing issues, thereby reducing downtime and enhancing operational efficiency.
  • 2. Security Surveillance and Threat Identification:
Splunk is extensively utilized for Security Information and Event Management (SIEM) objectives, aiding organizations in overseeing their IT environments for security threats and irregularities. By correlating data from diverse sources, it can efficiently identify and address security incidents, thereby bolstering the overall cybersecurity stance.
  • 3. Regulatory Adherence and Oversight:
Splunk supports regulatory adherence and oversight by empowering organizations to gather, analyze, and report on data pertinent to regulatory requirements and industry standards. This capability is especially critical for sectors like finance, healthcare, and government, where stringent compliance mandates are in place.
  • 4. IT Operations and DevOps:
Splunk aids in IT operations and DevOps practices by providing visibility into IT infrastructure, application performance, and deployment processes. This allows organizations to identify areas for optimization, streamline operations, and accelerate the development and delivery of software applications
  • 5. Machine Learning and Predictive Analytics:
Splunk equips organizations with machine learning and predictive analytics functionalities, empowering them to uncover patterns, detect anomalies, and forecast outcomes from their data. This supports proactive resolution of issues, capacity planning, and efforts in risk management
  • 6. Customer Satisfaction Administration:

Splunk can be utilized to assess customer interactions and feedback from various channels, enabling organizations to delve deeper into customer requirements and preferences. This information can then be utilized to tailor offerings, elevate customer satisfaction levels, and nurture brand loyalty

To sum up, Splunk is an essential tool for organizations to leverage data efficiently, promoting operational excellence, strengthening security measures, ensuring compliance, and achieving business objectives.

Integrating Splunk with ONES:

Steps involved to integrate the Splunk cloud service with ONES,
  • 1. Mapping Splunk instance with the ONES server
To integrate the Splunk service with ONES, follow these steps:
  • Configure Splunk Instances: Set up the Splunk instances on the ONES cloud page to start pushing metrics to the designated cloud endpoint.
  • Provide Necessary Details: The following information is required for the integration:
  • Splunk URL: The URL of your Splunk instance.
  • Unique Token: A unique token for authentication and secure data transmission.
  • Index: The specific index in Splunk where the pushed metrics will be stored.
By ensuring these details are accurately provided, you can successfully configure and integrate the Splunk service with ONES, enabling seamless metric collection and analysis.
Figure 1: Cloud Instance configuration page in ONES
Figure 2: Instance created and ready for data streaming
  • 2. Managing the created Instance through ONES:
The cloud instance created within ONES offers several management options to enhance user experience and sustainability. Users can update the integration settings, pause and resume metric uploads to the cloud, and delete the created integration when needed. These features make it easy for users to maintain and manage their cloud endpoint integrations effectively.
Figure 3 : Updating the integration details
Figure 4: Option to pause and resume the metric streaming to cloud
Figure 5: Option to delete the integration created
  • 3. User defined metric update:
The end user has the flexibility to select which metrics from their network monitored by ONES should be uploaded to the designated cloud service. This ONES 2.1 release supports various metrics, including Traffic Statistics, ASIC Capacity, Device Health, and Inventory. Administrators can choose and deselect metrics from the available list within these categories according to their preferences.
Figure 6 : Multiple options available for metric update on cloud
  • 4. Multi vendor support
The metric update is not limited to any particular hardware or network operating system (NOS). ONE-Data Lake’s data collection capability extends across various network operating systems, including Cisco NX-OS, Arista AOS, SONiC, and Non-SONiC. Data streaming occurs via the gnmi process on SONiC-supported devices and through SNMP on OS from other vendors.
Figure 7: ONES inventory showing multiple vendor devices streaming

Splunk Analytical capabilities:

  • 1. Event based Visualization:
Events within Splunk generally contain timestamped data alongside related metadata and content. Each event undergoes parsing and indexing separately, facilitating users to efficiently search, analyze, and visualize data. Splunk automatically extracts fields from events during indexing, streamlining filtering and correlation based on specific criteria.
Figure 8 - Inventory details from NX-OS is captured as events in Splunk
  • 2. Chart representation:
This entails visually depicting data using charts or graphs, aiding users in comprehending patterns, trends, and relationships within the data more readily than analyzing raw data alone. These graphical representations encompass diverse types such as bar charts, line charts, pie charts, scatter plots, and others, each tailored to specific data types and analytical objectives
Figure 9 - Pie Chart in Splunk representing the data from different NOS vendors

Conclusion:

Aviz ONE-Data Lake functions as the cloud-based version of ONES, enabling the storage of network data in cloud repositories. It operates independently of any particular cloud platform and supports data streaming from leading network device manufacturers such as Dell, Mellanox, Arista, and Cisco. Network administrators have the freedom to specify the metrics they want to transfer to the cloud endpoint, granting customized control over the data storage procedure.

Schedule your demo today because with ONE Data Lake integrated with Splunk, you’re not just managing data — you’re revolutionizing network analytics for unparalleled insights and efficiency.
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From Hype to Reality: Navigating the Challenges of AI in Network Telemetry

AI is riding the crest of a technological wave, crowned the “Peak of Inflated Expectations” by Gartner’s 2023 Hype Cycle. Platforms like ChatGPT have become more than just buzzwords; they’re blazing a trail into a new era of technological possibilities. This isn’t a fleeting fad; it’s a fuel injection for innovation, poised to transform the landscape across industries including the Networking domain.

Think beyond chatbots and clever tweets.  AI’s true potential lies in its ability to learn, adapt, and create. It can craft personalized experiences, generate realistic synthetic data, and even write code, all while pushing the boundaries of what we thought possible. This isn’t just about hype; it’s about harnessing the power of creativity to revolutionize the way we live, work, and play. So buckle up, because the AI revolution is just getting started. And this blog, let me give some insights into how AI can transform Network telemetry and enhance the experience.

Gartner Hype Cycle - AI

Understanding Network Telemetry and applying AI

What is Network Telemetry?

Network Telemetry is the process of data collection, inspection, normalization and interpreting to generate information that helps the end user to visualize the network state and make decisions.

Beyond simply collecting data, network telemetry transforms it into actionable intelligence. Through meticulous analysis and normalization, it illuminates the network’s current state, enabling informed decisions and proactive interventions. Think of it as the network’s nervous system, providing a constant pulse of information for precise navigation.

  • Real-time Visibility: Critical for prompt issue detection and ensuring optimal performance. Proactive Issue Resolution
  • Performance Optimization:Insights into network traffic patterns, resource utilization, and application performance, helps to optimize configurations, allocate resources efficiently
  • Capacity Planning: Provides an overview of how network resources are utilized over time. helps to anticipate future demands and scale infrastructure accordingly
  • Troubleshooting and Diagnostics: Detailed network’s state helps with pin pointing root cause of the problems
  • Automation and Orchestration:Automated systems leverage telemetry for informed, dynamic adjustments in configurations and resource allocation based on current network conditions
  • Enhanced User Experience:Helps to develop tools that can be a single pane of glass visualization of entire network, this enhances the user experience

Harnessing the Power of AI for Network Telemetry

The convergence of AI and network telemetry represents a significant evolutionary leap in network management. By integrating AI’s analytical prowess with established telemetry infrastructure, we can unlock transformative benefits that enhance network security, optimize resource allocation, and streamline troubleshooting.

Elevating Network Intelligence:

  • Advanced Anomaly Detection: Move beyond basic pattern recognition. AI models, trained on historical data, can identify even subtle deviations from normal network behavior, providing early warnings of potential cyber threats or operational anomalies
  • Automated Root Cause Analysis: Eliminate the guesswork. AI-powered analytics delve into telemetry data to diagnose network issues with precision, significantly reducing troubleshooting time and minimizing service disruptions
  • Proactive Traffic Forecasting: Gain the upper hand on network congestion. AI can predict future traffic patterns with remarkable accuracy, enabling proactive resource allocation and infrastructure optimization to ensure a seamless user experience
  • Enhanced Network Resilience: Proactively address vulnerabilities before they become problems. AI models can simulate various network scenarios, identifying potential issues in new devices and configurations before deployment, paving the way for a more robust and secure network architecture
  • Data-Driven Insights at Your Fingertips: Unleash the power of conversational intelligence. AI-powered chatbots can translate complex telemetry data into readily digestible insights and actionable recommendations, empowering network teams at all levels with real-time knowledge

Beyond Hype, Embracing a Paradigm Shift:

The integration of AI into network telemetry isn’t just a technological trend; it’s a strategic imperative. By embracing this transformative technology, organizations can build a future-proof network infrastructure characterized by enhanced security, proactive efficiency, and informed decision-making. This is not a revolution, but an evolution, a seamless integration of AI’s capabilities to empower existing systems and propel network management to new heights.

Reframing the Challenges: Building Robust AI for Network Telemetry

While the promises of AI in network telemetry are vast, navigating its implementation requires careful consideration of several key challenges:

Data-Driven Foundations:

  • Data Quality and Availability: Robust AI models rely on diverse, high-quality telemetry data. Organizations lacking established data collection or struggling with inconsistent formats and evolving network topologies may face initial hurdles
  • Model Selection and Adaptation: Choosing the optimal AI model for your specific network needs and adapting it to your unique data landscape requires careful consideration and expertise. Continuous training and re-training also pose ongoing resource demands

Trust and Transparency:

  • Explainability and Transparency: Building trust in AI-driven insights requires understanding how models arrive at their conclusions. Explainable AI techniques are crucial to ensuring transparency and fostering user confidence
  • Hallucination Control: Models can inadvertently generate inaccurate or nonsensical data, often referred to as "hallucinations." Implementing robust safeguards and error detection mechanisms is essential to mitigate these risks

AI TRISM: Transforming Network Telemetry with Trust, Reliability, and Safety

Applying the AI TRISM framework to network telemetry unlocks a new era of trust, reliability, and safety in our connected world. Trust is bolstered by transparent models that explain how anomalies are detected and prioritized, allowing network administrators to understand and make informed decisions. Reliability soars through AI-powered anomaly detection, automatically pinpointing issues before they snowball into outages, while synthetic data generation ensures robust training even with limited real-world telemetry. Safety takes center stage as AI models learn to differentiate between harmless fluctuations and genuine threats, protecting critical infrastructure from cyberattacks and malicious actors.

Imagine a network humming with the silent symphony of AI. Anomalous blips in traffic flow are instantly flagged, not by rigid thresholds, but by AI models continuously learning the network’s healthy rhythm. Security threats are swiftly identified and neutralized, not through brute force, but by AI’s uncanny ability to discern friend from foe. This is the future of network telemetry, powered by AI TRISM – a future where trust, reliability, and safety weave a protective web around our increasingly interconnected lives.

We, at Aviz, are harnessing the power of AI to make significant improvements in the networking landscape. Expect even more advancements to come from us soon.

Contact us today because with our cutting-edge AI solutions, you’re not just navigating the hype — you’re transforming your network telemetry into a powerhouse of innovation, efficiency, and security.

From Hype to Reality: Navigating the Challenges of AI in Network Telemetry

AI is riding the crest of a technological wave, crowned the “Peak of Inflated Expectations” by Gartner’s 2023 Hype Cycle. Platforms like ChatGPT have become more than just buzzwords; they’re blazing a trail into a new era of technological possibilities. This isn’t a fleeting fad; it’s a fuel injection for innovation, poised to transform the […]