Telmai vs. Monte Carlo
Deep dive into the quality of your data at any step of the pipeline without slowing it down.
Monitor your data across data warehouses, data lakes, streaming sources, and more.
Use Cases
Use Telmai
When you need to monitor all your data across every stage of the pipeline.
When data accuracy and validity are critical to your business.
When you need to go deep into your actual data values to detect anomalies and drifts.
Use Monte Carlo
When you only want to monitor your data warehouse and BI reports.
When a peripheral view of data, captured through its metadata or system logs, gives you peace of mind.
When you only need a high level view to ensure you that the data warehouse is loaded, refreshed, and operational.
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Trusted by
Maria Grineva
SVP Engineering, Dun & Bradstreet
Darius Kemeklis
EVP, Myers-Holum
Kishore Damodar
AVP, Cloud & Data Engineering, Merkle
A set of predefined metrics such as value uniqueness, completeness, volumes, schema change to shorten your time to detection and time to resolution.
Interactive UI to help you investigate and identify anomalous data within a dataset to get to the root cause issues quicker.
Machine learning is used to calculate data quality metrics. With this technique anomalies in data can show up without preconceived knowledge of data.
Metadata is used to calculate data quality metrics. For example, a timestamp of a table determines freshness, or schema of a table determines new/dropped columns.
Vendor-specific set of metrics and how they are defined. Some metrics like volume/row count is universal, others like freshness differ from vendor to vendor.
Highly scalable and centralized compute layer to add any number of data sources, records, and attributes with unpredictable volume spikes; does not slow down operational databases by querying data or metadata for information.
Users-defined metrics and expectations using a human-in-loop approach to enhance or tune predefined metrics.
Detecting outliers and anomalies in all data, even semi-structured fields.
Advanced thresholds calculated using historical data, taking into account seasonality.
Ability to observe data in Google BigQuery, Amazon Redshift, Snowflake, FireBolt, PostGresSQL, and others.
Ability to observe JSON and other semi-structured schemas via parquet, BigQuery, and Snowflake.
Ability to observe data collected from API calls. This could be telemetry data, logs, and events.
Integrating with BI tools such as Looker or Tableau to show downstream reports affected by bad data.
Monitor drifts of user defined business metrics in the data aggregated over millions of dimensions; only possible with observing data values, not only the metadata.
Ability to set notification policies and receive alerts via Slack, email, or other systems.
Often already included in a data catalog, lineage allows seeing downstream dependencies or upstream sources of a data quality issue.
Invoke a remediation flow programmatically and execute various repair actions based on the alert type, context or pipeline job completion.
Integration with advanced data pipeline workflow orchestration systems to control pipelines based on monitoring outcomes.
This is one example of orchestration, where you can change the pipeline workflow based on monitoring outcomes.
Ability to start now. Try the product without going through a sales cycle.
Varies by platform. Price may increase as your number of systems/data sources monitored increases.
Ability to observe data in Amazon S3, Azure Blob, Google Cloud storage, and others.
Ability to observe data transmitted in real-time or near real-time, such as data in AWS Kinesis, Google Pub-sub, Kafta.
Ability to monitor only data added or changed since the last time.
Cost of calculating data quality metrics. This is architecture dependent. If processed in warehouse or cloud storage the costs will increase as usage increases.
Options include: public (data managed and processed on the vendor side) and private cloud (data is managed and processed in client/your account).