Key elements of observability data pipeline control

Key elements of observability data pipeline control

Modern IT environments are becoming increasingly distributed in nature. Distributed environments have a large number of interdependent and interconnected parts, making them more susceptible to failure. As businesses evolve, the underlying distributed technology stacks that support them take on more services, infrastructure, and other dependencies, making it ridiculously difficult to watch for and manage failures. Modern observability and monitoring platforms help solve this issue by taking in data in the form of logs, metrics, and traces from every component and endpoint in an IT environment and making it available for proactive monitoring and analysis. The trouble is, the more distributed and complex the environment, the more data there is to ingest and analyze.

With data, the more there is, the better – your analysis is more exhaustive, you discover issues and threats faster, and you’re able to comprehend the unknowns of your system. However, not all of your system data is valuable. But when you’re dealing with an extensive, distributed system and its associated data streams, how do you derive value from the information it generates? The answer is observability data pipeline control.

Controlling observability data pipelines with LogFlow

Observability data pipeline control is the ability to rein in and control the data streams that fuel your observability stack, and LogFlow does just that. LogFlow provides powerful constructs to aggregate logs from multiple sources, improve data quality and forward them to one or more destinations of your choice. We built LogFlow keeping in mind the key elements of total observability data pipeline control. 

  • Improving data quality and relevance while controlling data volume
  • Routing data between any source and any target
  • Shaping and enriching data for better analysis
  • Retaining data for as long as needed without breaking the bank
  • Keeping data searchable and recoverable in real-time
  • Visualizing and replaying data across time ranges in real-time

Let’s take a look at how LogFlow addresses each of these aspects of observability pipeline control. 

Data quality, relevance, and volume filter

Observability data is valuable. However, those who understand the content of observability data know that neither all data streams nor all portions of event data are relevant or useful. 

The cost of running observability and SIEM solutions like Splunk, Datadog, QRadar, etc., is directly influenced by the volume of data ingested. These costs primarily comprise the cost of licensing and infrastructure required to support the processing and storage of data volumes. The higher the volume, the more expensive it is to run these solutions. With LOGIQ.AI’s LogFlow, you can analyze the quality of data flowing through data pipelines into these systems while exercising complete control over the data’s quality, relevance, and volume.  With LogFlow as a sidecar to your existing observability system, organizations can instantly reduce their TCO of these systems by up to 95%. 

Reduce TCO by up to 95%

Data routing

Organizations often need to stream the same data to two or more different systems for different purposes. For example, your organization may need to ingest the same data stream into a SIEM system, a log analysis system, and a homegrown compliance system. Traditionally, organizations accomplish these scenarios by installing three separate collectors at each data source. However, doing so leads to duplication of data streams, overutilization or clogging of network bandwidth at the endpoints, and multiplication of costs through licensing and infrastructure needed to support the large volume of duplicated data at each target. 

LOGIQ.AI’s LogFlow acts as a data router redirecting the right data streams to the right data targets without the need for multiple collectors, thereby eliminating clogged networks and ballooning licensing costs from running multiple systems.

Send relevant data to the right location every time.

Data shaping

Engineers often encounter the need to modify observability data generated by their applications, services, or infrastructure to analyze better and understand the performance of their systems and services. 

Until now, data modification involved reengineering source code and the behavior of systems. Reengineering often comes at the cost of slowed development cycles, reboots, and introducing new risks and vulnerabilities into primary systems. You can now eliminate these costs and risks with observability pipeline control enabled by LogFlow. You can now optimize and transform observability data, be it adding or trimming information, on the fly without touching source systems by intercepting the data stream in-flight and applying the desired transformations.

Enable better decision making on the fly.

Data retention

While all observability data is not always valuable, you should still retain the data for as long as possible. You cannot always anticipate when you need it for correlation, historical analysis, bug or threat forensics, or even compliance. Object storage in any location, either the cloud or on-premise, is an excellent choice for storing data for long periods due to its low cost and reliability. A fundamental capability of an observability pipeline control system is to route original data streams with all of their original content to enable long-term storage and on-demand compliance, analysis, and correlations.

LogFlow’s unique capability allows organizations to retain 100% of data for long durations and ensure all data across any period is instantly available for any purpose, without the usual long delays associated with data retrieval from object storage systems.

Enable 100% compliance in real-time.

Data search and recovery

Observability pipeline control must also enable real-time search and recovery of 100% data, regardless of its age. Pipeline control shouldn’t just cover the direction, content of data, and target of data but also the speed of access. LogFlow’s unique tech allows for the storage of 100% of data on any object storage and the search and recovery of any data in real-time. Yes, real-time search and retrieval of data from object storage are now possible. Object Storage with LogFlow is not cold or archive storage anymore – it becomes your primary and hot storage. You can now avoid costly compute and fast disks and reallocate those budgets to other core and strategic initiatives.

Be insight-ready in real-time, all the time for all of your data.

Data virtualization and replay – time machine!

DevOps and SecOps teams need the capability to query and replay data at will, regardless of whether the data is new or historical. The ability to replay historical data in real-time helps them process and visualize it to better understand the performance of their systems over time, identify new vulnerabilities or performance bottlenecks, and make improvements. 

Observability pipeline systems should enable XOps teams to select portions of data from specific periods and replay the data to a processing engine of their choice. LogFlow allows time travel and data streams to be replayed and virtualized with single-click actions. It’s a critical capability that enables Day 2 teams to deliver always-on and zero-trust systems.

Time-travel!

Conclusion

Being able to control your data streams and gaining visibility into the data flowing through them using LogFlow is more beneficial that it seems. You get to not only control the volume and quality of data flowing between your systems, but also optimize the way your observability and monitoring systems work for you. LogFlow also provides teams with a single view of how your observability data pipeline flows, thereby enabling them to get their observability data from any source to any target system at any time.

If you’d like to know more about how LogFlow can help solve the problems with enterprise observability data and data pipelines, watch this quick video, and then drop us a line.

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