10 Things to Consider Before Multicasting Your Observability Data

Will Krause
Circonus
Learn more about Apica

Multicasting in this context refers to the process of directing data streams to two or more destinations. This might look like sending the same telemetry data to both an on-premises storage system and a cloud-based observability platform concurrently. The two principal benefits of this strategy are cost savings and service redundancy. ■ Cost Savings: Depending on the use-case, storing or processing data in one location might be cheaper than another. By multicasting the data, businesses can choose the most cost-effective solution for each specific need, without being locked into one destination. ■ Service Redundancy: No system is foolproof. By sending data to multiple locations, you create a built-in backup. If one service goes down, data isn't lost and can still be accessed and analyzed from another source. The following are 10 things to consider before multicasting you observability data:

1. Consistency of User Expectations

It's crucial that both destinations receive data reliably and consistently. If it is unclear to users what data resides in which platform, it will impede adoption and make this strategy less effective. A common heuristic is to keep all of your data in a cheaper observability platform and send the more essential data to the more feature rich expensive platform. Likewise if one platform has data integrity issues due to the fact that no one is using it outside of break glass scenarios, it will reduce the effectiveness of this strategy.

2. Data Consistency

While it's good to have a process for evaluating the correctness of your data, when you write data to two systems, not everything will always line up. This could be due to ingestion latency, differences in how each platform rolls up long term data, or even just the graphing libraries that are used. Make sure to set the right expectations with teams, that small differences are expected if both platforms are in active use.

3. Bandwidth and Network Load

Transmitting the same piece of data multiple times can put an additional load on your network. This is more of an issue if you're sending data out from a cloud environment where you have to pay the egress cost. Additionally, some telemetry components are aggregation points that can push the limits of vertical scaling (for example carbon relay servers). Multicasting the data may not be possible directly at that point in the architecture due to limitations in how much data can traverse the NIC. It's essential to understand the impact on bandwidth and provision appropriately.

4. Cost Analysis

While multicasting can lead to savings, it's crucial to do a detailed cost analysis. Transmitting and storing data in multiple places might increase costs in certain scenarios.

5. Security and Compliance

Different storage destinations might have different security features and compliance certifications. Ensure that all destinations align with your company's security and regulatory needs.

6. Tool Integration

Not all observability tools might natively support multicasting data. Some observability vendors' agents can only send data to their product. You may need to explore a multi-agent strategy in cases like that.

7. Data Retrieval and Analysis

With data residing in multiple locations, the way your teams will need to engage with the data may differ. If you're using a popular open source telemetry dashboarding tool, then there will be at least some degree of consistency with how to engage with the data, even if the query syntax supported by each platform is different. This becomes a little more challenging if your teams are using the UI of the higher cost observability platform.

8. Data Lifecycle Management

Consider how long you need the data stored in each location. You might choose to have short-term data in one location and long-term archival in another.

9. Maintenance and Monitoring

With more destinations come more points of potential failure. Implement robust monitoring to ensure all destinations are consistently available and performing as expected. This is a good opportunity to introduce cross monitoring, where each observability stack monitors the other.

10. Migration and Scalability

As your business grows, you might need to migrate or scale your lower cost observability platform. Ensure the chosen destinations support such migrations without significant overhead.

Conclusion

Multicasting data that is collected by your observability tools offers an innovative approach to maximize both cost efficiency and system resilience. However, like all strategies, it comes with its set of considerations. By understanding and preparing for these considerations, businesses can harness the power of this approach to create observability solutions that are both robust and cost-effective.

Will Krause is VP of Engineering at Circonus

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10 Things to Consider Before Multicasting Your Observability Data

Will Krause
Circonus
Learn more about Apica

Multicasting in this context refers to the process of directing data streams to two or more destinations. This might look like sending the same telemetry data to both an on-premises storage system and a cloud-based observability platform concurrently. The two principal benefits of this strategy are cost savings and service redundancy. ■ Cost Savings: Depending on the use-case, storing or processing data in one location might be cheaper than another. By multicasting the data, businesses can choose the most cost-effective solution for each specific need, without being locked into one destination. ■ Service Redundancy: No system is foolproof. By sending data to multiple locations, you create a built-in backup. If one service goes down, data isn't lost and can still be accessed and analyzed from another source. The following are 10 things to consider before multicasting you observability data:

1. Consistency of User Expectations

It's crucial that both destinations receive data reliably and consistently. If it is unclear to users what data resides in which platform, it will impede adoption and make this strategy less effective. A common heuristic is to keep all of your data in a cheaper observability platform and send the more essential data to the more feature rich expensive platform. Likewise if one platform has data integrity issues due to the fact that no one is using it outside of break glass scenarios, it will reduce the effectiveness of this strategy.

2. Data Consistency

While it's good to have a process for evaluating the correctness of your data, when you write data to two systems, not everything will always line up. This could be due to ingestion latency, differences in how each platform rolls up long term data, or even just the graphing libraries that are used. Make sure to set the right expectations with teams, that small differences are expected if both platforms are in active use.

3. Bandwidth and Network Load

Transmitting the same piece of data multiple times can put an additional load on your network. This is more of an issue if you're sending data out from a cloud environment where you have to pay the egress cost. Additionally, some telemetry components are aggregation points that can push the limits of vertical scaling (for example carbon relay servers). Multicasting the data may not be possible directly at that point in the architecture due to limitations in how much data can traverse the NIC. It's essential to understand the impact on bandwidth and provision appropriately.

4. Cost Analysis

While multicasting can lead to savings, it's crucial to do a detailed cost analysis. Transmitting and storing data in multiple places might increase costs in certain scenarios.

5. Security and Compliance

Different storage destinations might have different security features and compliance certifications. Ensure that all destinations align with your company's security and regulatory needs.

6. Tool Integration

Not all observability tools might natively support multicasting data. Some observability vendors' agents can only send data to their product. You may need to explore a multi-agent strategy in cases like that.

7. Data Retrieval and Analysis

With data residing in multiple locations, the way your teams will need to engage with the data may differ. If you're using a popular open source telemetry dashboarding tool, then there will be at least some degree of consistency with how to engage with the data, even if the query syntax supported by each platform is different. This becomes a little more challenging if your teams are using the UI of the higher cost observability platform.

8. Data Lifecycle Management

Consider how long you need the data stored in each location. You might choose to have short-term data in one location and long-term archival in another.

9. Maintenance and Monitoring

With more destinations come more points of potential failure. Implement robust monitoring to ensure all destinations are consistently available and performing as expected. This is a good opportunity to introduce cross monitoring, where each observability stack monitors the other.

10. Migration and Scalability

As your business grows, you might need to migrate or scale your lower cost observability platform. Ensure the chosen destinations support such migrations without significant overhead.

Conclusion

Multicasting data that is collected by your observability tools offers an innovative approach to maximize both cost efficiency and system resilience. However, like all strategies, it comes with its set of considerations. By understanding and preparing for these considerations, businesses can harness the power of this approach to create observability solutions that are both robust and cost-effective.

Will Krause is VP of Engineering at Circonus

Related Links

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When Automating Forever Becomes Faster Than Doing It Once

People want to be doing more engaging work, yet their day often gets overrun by addressing urgent IT tickets. But thanks to advances in AI "vibe coding," where a user describes what they want in plain English and the AI turns it into working code, IT teams can automate ticketing workflows and offload much of that work. Password resets that used to take 5 minutes per request now get resolved automatically ...

Smarter Systems for Disinformation: How Data-Centric Design Could Transform Online Trust

Governments and social platforms face an escalating challenge: hyperrealistic synthetic media now spreads faster than legacy moderation systems can react. From pandemic-related conspiracies to manipulated election content, disinformation has moved beyond "false text" into the realm of convincing audiovisual deception ...

The 5 Pillars of Cross-Platform Observability

Traditional monitoring often stops at uptime and server health without any integrated insights. Cross-platform observability covers not just infrastructure telemetry but also client-side behavior, distributed service interactions, and the contextual data that connects them. Emerging technologies like OpenTelemetry, eBPF, and AI-driven anomaly detection have made this vision more achievable, but only if organizations ground their observability strategy in well-defined pillars. Here are the five foundational pillars of cross-platform observability that modern engineering teams should focus on for seamless platform performance ...

What's Blocking Scalable AI?

For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale. At least, that's the picture that emerged from a recent Forrester study commissioned by Tines ...

Not All Networks Are Built for the Edge

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...

Why Observability Is the Missing Piece in Your Business Growth

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

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Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

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The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...