Key takeaways
- Data observability enhances monitoring by providing context to metrics and indicating when intervention is required.
- Data pipelines alone cannot monitor data; a dedicated observability architecture is essential for effective oversight.
- Effective observability platforms enable tracking of downstream dependencies to identify and address root problems.
- Key components of a successful data observability platform include simple setup, end-to-end tracking, and support for distributed systems.
“Data observability goes deeper than monitoring by adding more context to system metrics, providing a deeper view of system operations, and indicating whether engineers need to step in and apply a fix,” explained Evgeny Shulman, Co-Founder of Databand.ai.
Data pipelines can move data, but they can’t monitor it. These data pipelines are complex systems that require data observability architecture for constant sleuthing and end-to-end monitoring to understand why processes fail. This, along with the lack of observable data today, partly add up to creating the backbox. The black box gives you an output without humans being able to understand its processings yet. To correct the pipelines, engineers first need to observe.
“In other words, while monitoring tells you that some microservice is consuming a given amount of resources, observability tells you that its current state is associated with critical failures, and you need to intervene,” according to Shulman.
What makes an effective data observability platform?
Observability platforms provide a way to track downstream dependencies to address the root problem.
The essential components of an observability platform according to databand.ai:
- Simple setup
- End-to-end tracking
- Observability architecture
- Threshold setting
- Administration
- Data observability open source
- Distributed systems observability
Top data observability platforms
Monte Carlo
Monte Carlo provides an end-to-end solution to prevent broken data pipelines with its observability service. This is a great tool for data engineers to ensure reliability and avoid potentially costly data downtime. Monte Carlo’s features include data catalogues, automated alerting, and observability on several criteria out of the box. “In software engineering, every team has a solution like New Relic, DataDog, or PagerDuty to measure the health of applications and ensure reliability. How come data teams are flying blind?” the startup asks. The platform just raised $80 M in a Series C round.
Databand
Databand’s objective is to allow for more efficient data engineering in complex modern infrastructure. Its AI-powered platform provides data engineering teams with tools for a smooth operation that help them gain unified visibility into their data flows. The aim is to discover where the data pipelines broke before any bad data manages to squeeze through. The company also plugs into cloud-native tools like Apache Airflow and Snowflake in the modern data stack.
Honeycomb
Honeycomb’s observability tool provides engineers with the visibility to troubleshoot problems in distributed systems. The company claims that “Honeycomb makes it easy to understand and troubleshoot complex relationships within your distributed services.” Its full-stack cloud-based observability tool supports events, logs, and traces, along with an automatic instrumented code by its agent, Honeycomb beelines. Honeycomb also supports OpenTelemetry for generating instrumentation data.
Acceldata
Acceldata provides tools for data pipeline monitoring, data reliability, and data observability. The tools are to assist data engineering teams in gaining comprehensive and cross-sectional visibility into complex data pipelines.
Acceldata’s tools synthesise signals across multiple layers and workloads on a single pane of glass to assist several teams in working together to fix data issues. In addition, Acceldata Pulse helps in performance monitoring along with observation to ensure data reliability at scale. The tool is geared towards the finance sector and payments.
Datafold
Datafold’s data observability tool assists data teams to monitor data quality through diffs, anomaly detection, and profiling. Its features allow teams to engage in data QA with data profiling, make table comparisons across databases or within a database, and create smart alerts from any SQL query with a single click. In addition, data teams can monitor ETL code changes with data transfers and integrate them with their CI/CD to instantly review the code.
SigNoz
SigNoz is a full-stack open-source APM and observability tool that captures metrics and traces. Since the tool is open-source, users can host it within their infra without sharing their data with a third party. Their full-stack tools include a generation of telemetry data, a storage backend to store the telemetry data, and a visualisation layer to consume and take actions. SigNoz generates telemetry data using a vendor-agnostic instrumentation library, OpenTelemetry.
DataDog
DataDog’s observability tool’s features include infrastructure monitoring, log management, application performance monitoring, and security monitoring. DataDog allows full visibility into distributed applications by tracing requests from end-to-end distributed systems, charting latency percentiles, instrumenting open-source libraries and seamless navigation between logs, metrics, and traces. The founders claim this to be the “essential monitoring and security platform for cloud applications.”
Dynatrace
Geared towards large scale enterprises, Dynatrace provides a SaaS enterprise tool to target a varied spectrum of monitoring needs. Its AI engine, Davis, can automate root cause analysis and anomaly detection. Additionally, the company’s tools can provide a different solution for infrastructure monitoring, application security, and cloud automation.
Grafana Labs
Grafana’s open-source analytics and interactive visualisation web layer are quite popular for supporting several storage backends for time-series data. Grafana can connect to Graphite, InfluxDB, ElasticSearch, Prometheus, and various other data sources and supports Jaeger, Tempo, X-Ray, and Zipkin for traces. Its feature offers include plugins, dashboards, alerts, and other user-level access for governance. There are two different services provided - one, Grafana Cloud that provides solutions like Grafana Cloud Logs, Grafana Cloud Metrics, and Grafana Cloud Traces. Two, Grafana Enterprise Stack that supports metrics and logs with Grafana installed within the user’s computer.
Soda
Soda’s AI-powered data observability platform is a collaborative environment for data owners, data engineers, and data analytics teams to work and solve problems collectively. Soda.ai has described the platform as “a data monitoring platform that allows teams to define what good data looks like and resolve issues swiftly before they have a downstream impact. This transparency and ease create trust — in each other, and the data.” In addition, the tools allow users to check their data immediately and create rules to test and validate data.
