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 view to ensure that the data warehouse is loaded, refreshed, and operational.

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When Telmai is a Better Alternative to Monte Carlo

Monitor the Data, Not Just the Metadata

Telmai uses ML technology to detect data anomalies and predict future thresholds taking into account seasonality and historical trends.

Telmai takes both data values and metadata changes into account and doesn’t solely rely on metadata. We believe that in order to provide an accurate view of data, the data itself needs to be checked, not just its peripheral view.

A Centralized Observability for Full Pipeline Monitoring

Use Telmai to monitor and detect data anomalies at any stage of your pipeline - from ingest to downstream systems. Telmai monitors more data types and formats than any other data observability product in the market.

This includes data warehouses and analytical databases (Snowflake, BigQuery, Databricks Delta Lake, Redshift, PostGresSQL, FireBolt) and streaming sources (AWS Kinesis, Google pub-sub, and Kafta). We also cover semi-structured data (JSON, Parquet, Avro
), CSV,  flat files, and cloud storage (GCS, S3, Azure Blob).

Investigate Data Quality at Scale

Telmai has its own data quality validation and metric computation layer and does not push these calculations to your analytical systems which will slow them down and increase their compute license costs.

Monte Carlo, on the other hand, calculates metrics by querying the underlying database. For one, this limits you to the number of queries you can run and your queries may time out in cases where your metric calculation requires long-running queries.

Additionally, you constantly need to watch out not to increase your data warehouse costs or affect its performance.

Telmai on the other hand gives you unlimited power to investigate all your data without needing to sample or worry about slowing your operational systems down.

"When dealing with data sets comprising million of records, Telmai quickly becomes an incredibly useful tool, saving a lot of time and ultimately increasing data quality."

Maria Grineva

SVP Engineering, Dun & Bradstreet

"Data anomaly investigations that used to take hours now take minutes and gives us more time to focus on the business needs."

Darius Kemeklis

EVP, Myers-Holum

"I liked its simplicity, ease of integration and powerful insights to detect anomalies. It makes the life of the developers so easy without the need to build various rules for detecting anomalies and reduces lot of time and effort"

Kishore Damodar

AVP, Cloud & Data Engineering, Merkle

Telmai vs. Monte Carlo Comparison Table

Monte Carlo

Visual, no code experience

Yes

Partial
Freshness volume, and schema monitoring are visual, but Field Health Monitors require SQL coding.

ML-based metric calculation

Yes 
Out of the box

No 
Opt-in only option uses queries to read and report on data.

Metadata-based metric calculation

Yes

Yes

Out of the box metrics

Schema
Volume
Uniqueness
Completeness
Distribution Drift
Pattern Drift
Freshness (complete)
Validity
Accuracy
Custom

Schema
Volume
Uniqueness
Completeness
Distribution
Lineage
Freshness (limited)
Custom

Anomaly detection on any kind of data

Yes

No
Only on % based metrics (e.g., % of uniqueness). Uses queries that slow the database down.*

ML-based thresholds

Yes

No

Data Observability on structured data (databases, delta lakes, and data warehouses)

Yes

Yes

Data Observability on semi-structured data

Yes

Partial
Monitors schema changes in JSON data (detects metadata changes, not data changes)

Data Observability on cloud storage and data lakes

Yes

No
Requires creating an external table with Athena, Presto, and or others to transform data into a structured format before observing it.

Data Observability on streaming data

Yes

No

Data Observability on data collected from API calls

Yes

No

BI integration

No

Yes

Business metric monitoring

Yes

No

Change data capture (CDC)

Yes

No

Alerts and notifications

Yes

Yes
Only on included metrics. Requires calling support for the rest.*

Data lineage

No
Details in FAQ

Yes
Not for the entire pipeline; only within supported data warehouses to BI tools

APIs

Yes

Yes

Orchestration

Yes Integrate via API

Yes Integrate via API

Circuit breaker

Yes

Yes

Self-service trial

Yes

No

Deployment models

SaaS and Private Cloud

SaaS and Private Cloud

Pricing

By the volume of data processed in Telmai

By number of systems monitored + user count

Infrastructure costs

Fixed

Grows
Monte Carlo computes all metrics using the underlying data warehouse or database it monitors. This increases your data warehouse costs.

* Source: Monte Carlo Docs. 

FAQ

01 | How does Telmai define freshness?

Telmai defines Freshness by considering both the data and the metadata about the table. For example, a table that was updated in the last hour, and therefore carrying an updated timestamp (i.e. metadata,) may not have fresh, accurate, and up-to-date data for critical attributes or entities within the content of the table. Telmai does not solely rely on metadata. Instead, it also looks at the actual data values to determine Freshness as a metric. For more information, please refer to this article.

Telmai defines Freshness by considering both the data and the metadata about the table. For example, a table that was updated in the last hour, and therefore carrying an updated timestamp (i.e. metadata,) may have fresh, accurate and up to date data. Telmai does not solely rely on medata. Instead it also looks at the actual data values to determine Freshness as a metric.

02 | Why does Telmai not provide a column-level lineage?

Data lineage in its true form should provide end-to-end visibility of your entire data . This functionality is often best fit for a data catalog where data dependencies from ingest to downstream systems are captured along with proper metadata, ownership, change logs, and other important information. Through integrations, Telmai is able to detect and flag any data issues in your data catalog of choice and its data lineage features. 

The data lineage offered in some Data Observability tools is designed to detect problems after the fact, where the data is already in its destinations (e.g. data warehouse). In this scenario, because data has already deteriorated, lineage helps detect the root cause of the problem. Telmai on the other hand is the Data Observability platform that gets integrated across all stages of the pipeline, not just the destination. Therefore, it is used to detect data issues earlier in the data life cycle and all throughout its different stages. As a result, a separate data lineage feature is not quite needed in Telmai.

03 | Wouldn’t processing data values have a toll on operational systems?

Telmai is designed to decouple metrics calculations from the underlying databases and data pipelines. With this architecture, you will not be using the computing resources of your data warehouse and other cloud infrastructures. This will help you protect the performance of these operational systems, and at the same time give you a scalable data observability platform that detects anomalies across your entire data, without being bound to query runtime issues, data samples, or high infrastructure costs.

04 | I found issues with my data, how would I remediate them?​

Telmai integrates with channels like Slack, Email, or to help you communicate and alert the right team at the right time. Telmai notifications include outliers, drifts, and other inconsistencies in your data. Using APIs you can also auto generate a ticket and assign it to the right source owner and integrate the information Telmai provides with your ticking systems. This helps the team immediately triage by fully understanding the issue and reducing its time to resolution.

Telmai also helps you set up auto-remediation through APIs. With our REST integration, you can build logic to automatically invoke a remediation flow as your data quality drifts. Telmai can be integrated into orchestration tools such as Airflow and DBT.

05 | How does Telmai protect my data?​

Telmai offers both a SaaS and a Private Cloud option. Depending on your security requirements you can choose the right option for you. We encrypt data both in transit and at rest. Telmai honors GDPR and CCPA as reflected in our Privacy Policy. Telmai is also SOC 2 compliant. For more information see our security page and our SOC 2 type 2 announcement.

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