SQL Server
Oracle
MySQL
PostgreSQL
Amazon Redshift
Azure SQL Database
DBT
Google Big Query
IBM DB2
MariaDB
SAP HANA
Snowflake
SQLite
Teradata
Vertica
Data observability tools for Amazon Redshift
Data observability tools help the company track and understand the state of its data at any given time and provide it with full insight into their data pipelines. They also allow them to identify, monitor and troubleshoot errors in order to minimize data issues and improve high data quality.
Monte Carlo
Monte Carlo's Data Observability platform uses machine learning to infer and learn what your data looks like, proactively identify data downtime, assess its impact, and notify those who need to know. It automatically and immediately identifies the root cause and lets you see all your data dependencies in one place, thereby allowing you to collaborate and resolve issues faster.
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Acceldata
Acceldata is a multi-layer data observability platform that empowers data teams with deep insights into compute, spend,
data reliability, pipelines, and users. It offers fully-automated reliability checks, which help immediately know about missing, late, or erroneous data on thousands of tables. From modern cloud data platforms to traditional databases to complex files, it helps you apply enterprise data reliability standards across your company.
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Dataedo
Dataedo is a data governance & data catalog software with data observability features such as data lineage, data profiling, and schema change tracking.
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Databand
Databand is a proactive data observability platform that ties directly into all stages of your data pipelines, starting with your source data. It automatically collects metadata from your modern data stack, builds historical baselines based on common data pipeline behavior, and lets you get visibility into every data flow from source to destination. It pinpoints unknown data incidents and reduces mean time to detection (MTTD) from days to minutes.
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Datafold
Datafold automatically imports and unifies data descriptions from all sources, including data warehouses and ELT tools such as dbt and Airflow, providing a holistic view with full-text search. It lets you get a high-level overview of your pipelines, zoom in on tables or columns, trace the flow end to end from raw data to BI dashboards. Moreover, it automatically constructs the field-level lineage graph by parsing every query ever executed in your data warehouses.
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Datadog
Datadog is a unified observability platform that provides full visibility into the health and performance of each layer of your environment. It is an easy-to-navigate observability platform to explore and analyze your data, create and customize dashboards and other visualizations for data from across your systems, and leverage observability platform features like actionable alerts, threat detection rules, and the Datadog API. Overall, it brings together end-to-end traces, metrics, and logs to make your applications, infrastructure, and third-party services entirely observable.
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Amazon CloudWatch
Amazon CloudWatch is a monitoring and observability service built for DevOps engineers, developers, site reliability engineers, and IT managers. CloudWatch provides you with data and actionable insights to monitor your applications, respond to system-wide performance changes, optimize resource utilization, and get a unified view of operational health. Moreover, it collects monitoring and operational data as logs, metrics, and events, providing you with a unified view of AWS resources, applications, and services that run on AWS and on-premises servers.
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Anodot
Anodot provides granular observability into the health of your data and identifies data quality issues in real-time. Anodot’s AI analytics can analyze 100% of the data you collect, detect anomalies and business incidents in real time and identify their root cause, enabling you to remedy problems faster and capture opportunities sooner.
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Lightstep
Lightstep is a complete, unified, and primary observability platform for the enterprise that provides complete system visibility and context at scale. It automatically detects changes to your application, infrastructure, and user experience — and surface the specific causes. Moreover, Understand and improve performance across millions of devices, users, and customers. Overall, visualize, aggregate, and analyze planet-scale application and infrastructure metrics at a fraction of the cost
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StackState
StackState is a smart observability & AIOps, powered by topology that maps changes to problems in your stack at any point in time so you can become a zero downtime enterprise. StackState’s approach tracks every change in topology and correlates it with system-wide telemetry data in real-time and over time. Moreover, it is purpose-built to integrate quickly with other data sources. It combines siloed telemetry and enriches it with time-series topology data so you can see cause and effect everywhere across your stack.
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Data observability tools help the company track and understand the state of its data at any given time and provide it with full insight into their data pipelines. They also allow them to identify, monitor and troubleshoot errors in order to minimize data issues and improve high data quality.
By monitoring data across multi-layered IT architecture, data observability tools enable identifying bottlenecks and data issues no matter where they originate. Thanks to new insights into how the data is moving through your IT infrastructure, it's possible to improve identification and resolution of the errors and search for the issues that could potentially be missed.
To help you select the best solution for monitoring the data health in your company, we've prepared a list of data observability tools that will enable your team to understand your data systems to fix and prevent data problems.