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Data observability tools for Snowflake
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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Soda
Soda is a data observability platform that automatically monitors and manages the health of your data through anomaly detection and dashboards. It allows everyone on your data team to find, analyze, and resolve data issues. It helps you use a common language to check and manage data quality across all data sources, from ingestion to consumption.
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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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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.