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Ai tools 4 Min Read Dec 13, 2022
Eight interesting data quality monitoring platforms at scale in 2022
Observability tools enable you to monitor and link the performance of your distributed environment with business outcomes.
Published By : Dr Nivash Jeevanandam
Data observability refers to the capability of measuring the internal state of data systems solely based on their outputs. These outputs in distributed systems, such as service meshes and microservices, are telemetry data (or logs, metrics, and traces).
Data observability technologies aid in the comprehension of multilayered systems by developers. They enable them to rapidly determine what is broken, slow, and in need of change.
The following are the best data observability tools in 2022.
Acceldata provides a framework for "multidimensional" data observability in complicated situations. The software predicts and assists in resolving operational issues before they affect business outcomes, and correlates events across the data, compute, and pipeline levels. Acceldata offers three product lines: Acceldata Pulse (Compute Performance Monitoring), Torch (Data Reliability), and Flow (Flow Management) (Data Pipeline Observability). This solution is ideal for data architects, scientists, and engineers.
Datafold's data observability solution enables data teams to monitor data quality using diffs, anomaly detection, and profiling. Its capabilities would allow teams to do data quality assurance using data profiling, compare tables across or inside databases, and generate intelligent alerts from any SQL query with a single click. In addition, data teams can monitor ETL code changes during data transfers and link them with their CI/CD system for quick code review.
The data observability platform from Monte Carlo applies best practices and ideas to data pipelines. Data engineers and analysts can see all data pipelines and products. Monte Carlo also offers machine learning, which gives customers a holistic perspective of the health and reliability of an organization's data for critical business use cases.
Databand (bought by IBM) enables users to detect and manage data issues with a proactive platform that identifies flawed data before its impact. In addition, with incident notifications and routing, customers may target unknown data events, minimise the time to detection, and enhance the time to resolution. Databand consists of four steps: metadata collecting, profile behaviour, data incident detection and alerting, and automated resolution. The solution also consists of an open-source library with various data utilities.
The Soda data observability platform provides a collaborative environment for data owners, engineers, and analytics teams to work together and solve challenges. Soda.ai has defined the platform as "a data monitoring platform that enables teams to define what excellent data looks like and quickly rectify errors before they have a downstream effect." This transparency and simplicity foster confidence in one another and the facts." In addition, the tools enable users to rapidly validate their data and define rules for data testing and validation.
The visibility provided by Honeycomb's observability tool enables developers to troubleshoot faults in distributed systems. According to the business, Honeycomb makes it simple to comprehend and debug complicated relationships among your dispersed services. Its cloud-based full-stack observability solution supports events, logs, and traces, as well as code automatically instrumented by its agent, Honeycomb beelines. Honeycomb further supports OpenTelemetry for instrumentation data generation.
SigNoz is a full-stack, open-source application performance monitoring (APM) and observability solution that collects metrics and traces. Since the programme is open-source, users can host it on their infrastructure without disclosing personal data. Their full-stack solutions include:
- Telemetry data generation.
- A storage backend for storing telemetry data.
- A visualisation layer for consuming and acting on the data.
SigNoz generates telemetry data using OpenTelemetry, a vendor-agnostic instrumentation package.
Grafana's open-source analytics and interactive web layer for visualising time-series data are highly popular for supporting several storage backends. Grafana can connect to Graphite, InfluxDB, ElasticSearch, Prometheus, and other data sources, and it helps Jaeger, Tempo, X-Ray, and Zipkin for tracing. In addition, it provides plugins, dashboards, alarms, and other user-level access for governance, among other features. Two different services are offered, the first of which is Grafana Cloud, which provides solutions such as Grafana Cloud Logs, Grafana Cloud Metrics, and Grafana Cloud Traces. Two, Grafana Enterprise Stack enables metrics and logs and includes Grafana on the user's machine.
