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10 Things to Consider Before Multicasting Your Observability Data

Will Krause
Circonus

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

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

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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