Observability for the Data Pipeline

 

Data Observability for Pipeline is a necessary part of the DevOps process. The data pipeline is not the same as technology systems, and its logic is often different from a technology system. Data pipelines can experience multiple failures before a process runs successfully. As a result, many APM tools are not designed to understand the logic of data pipelines, and data teams often end up with the wrong alerts.

Sensu's observability pipeline

Sensu is a powerful and flexible observability pipeline that allows users to get deep visibility into their infrastructure, applications, and services. You can use the pipeline to monitor your application or infrastructure, or combine it with a data platform such as InfluxDB. Sensu's API provides access to internal Sensu metrics in Prometheus format. This data includes information about applications, networking, and compute resources. The API also provides a Sensu Go monitoring template for InfluxDB, making it easy to collect observable metrics.

You can create pipelines to monitor external resources without installing any agent. The API backend automatically creates proxy entities when new events are created. The API also provides HTTP access to events.

Business rule enforcement

A recent PHMSA NPRM proposed revisions and new requirements related to pipeline safety. The proposed changes were aimed at preventing threats to the integrity of pipelines and improving emergency response to pipeline incidents. They also aim to promote environmental justice, particularly for minority populations. To achieve these goals, the proposed rules address 16 major topics.

Anomaly detection

Anomaly detection is an important part of pipeline data processing. Without this functionality, pipelines could become stuck delivering inaccurate data, which could infect downstream processes. Fortunately, there are several solutions to this problem, including ML-enabled data pipelines. The following article covers some of these solutions.

Using a combination of supervised and unsupervised machine learning is key to automated anomaly detection. While the vast majority of classification should be unsupervised, analysts should feed the algorithms with valuable datasets and baselines of business-as-usual behaviors. This allows analysts to automate and scale anomaly detection, while retaining the ability to make manual rules when necessary.

Anomaly detection is a powerful data science tool that identifies and alerts on outliers. By analyzing and interpreting the data, it enables businesses to detect critical incidents and opportunities for architectural optimization. But the biggest problem with anomaly detection is implementing an effective system. Often, data engineers are dealing with an inherited architecture with outdated documentation. Furthermore, pipelines are often siloed, making it difficult to report on data health, identify trends and set anomalous thresholds.

Monitoring

An observability pipeline is an integrated system for monitoring data from IT operations management tools and other sources. It gives organizations a broader view of system behavior and data flow, and enables them to identify trends and issues. However, the data can be overwhelming, and it can be difficult to determine the root cause of a problem from monitoring data alone.

Using the data from pipeline sensors, pipeline operators can analyze the performance of their pipeline and detect potential problems. They can also monitor pipeline health and efficiency by monitoring pipeline performance and resource usage. The dashboards in Cloud Monitoring provide analysis and support for troubleshooting and diagnosis.

Automated data discovery

When it comes to transforming large amounts of data into insightful insights, it can be difficult to do it manually. However, with the help of automated data discovery tools, the process of data discovery can be automated. These tools can locate data assets, characterize them, and automate preparation and visualization tasks. Using such tools, an enterprise can save up to 90% of the time it takes to conduct data discovery projects manually.

In today's increasingly complex data landscape, companies need a way to make data discoverable and usable. Automated tools help businesses determine the value and relevance of data. These tools are also helpful in governing data security and management. Ultimately, automated tools help businesses unlock the value hidden within their data landscape and manifest it in competitive advantage in markets.