Telmai vs. Monte Carlo

When Telmai is better alternative than Monte Carlo Data

Telmai monitors data across the entire pipeline and detects anomalies and data drifts using the actual data values.

Monte Carlo only monitors SQL sources and is a data warehouse monitoring tool. Monte Carlo uses metadata and system logs to infer information about the data.

Use Cases

Use Telmai

When you need to monitor all your data across every stage of the pipeline.

When data accuracy and validity is critical to your business. 

When you need to rely on actual data values and detect anomalies and drifts in the data itself.

Use Monte Carlo

When you only want to monitor your data warehouse and BI reports.

When a peripheral view of data recency and volume levels gives you a piece of mind. 

When you can rely on only metadata updates to get a broad view of your data warehouse or operational data stores.

"When dealing with data sets comprising million of records, Telm.ai 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, Darius Ugam solutions

Telmai vs. Monte Carlo Comparison Table

Monte Carlo

Out of the box data metrics

Yes

Yes

Visual, no code experience

Yes

Partial
Freshness volume, and schema monitoring is 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

Metrics definitions

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

Schema
Volume
Uniqueness
Completeness
Distribution
Lineage
Freshness (limited)
Custom

Decoupled metric calculation layer

Yes

No
Performs calculations in the data warehouse resulting in additional compute costs.

Custom metrics

Yes

No
Due to the lack of a metrics calculation layer, custom metrics could run into query timeout. To avoid this, Monte Carlo suggests prior knowledge of anomalies to filter data accordingly; the tool also samples 500 rows for large data sets*.

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 such as databases, delta lakes, and data warehouses

Yes

Yes

Data Observability on semi-structured data

Yes

Partial
Monitor for schema changes in JSON data stored in a table (detects metadata changes, not data changes, and requires read from a table)

Data Observability on cloud storage and data lakes

Yes

N0
Requires creating an external table with Athena, Presto and or others to transform data into a structure 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

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 data warehouse or database it monitors, which increases compute cost.

* Source: Monte Carlo Docs. 

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Why 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, but doesn’t solely rely on metadata. We believe that in order to provide an accurate view of data, its state, uniqueness, freshness, anomalies and drifts over time the data itself needs to be checked, not just its peripheral view.

Central Observability for Full Pipeline Monitoring

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

This includes semi-structured data (JSON, Parquet, Avro
), Structured (BigQuery, Redshift, Snowflake, PostGresSQL, CSV, and flat files,) Cloud Storage (GCS, S3, Azure Blob,) Data Warehouses and Analytical Databases (Snowflake, FireBolt, Databricks, Delta lake,) and Streaming Sources (AWS Kinesis, Google pub-sub, and Kafta). Temai has built its architecture to handle any data type so data can be monitored closest to where issues occur. Other systems only provide the ability to monitor data warehouses, and while this can detect some data

Scale and Performance

Using Telmai’s metric computation layer, you have the ability to compute and observe an unlimited number of metrics. Other data observability tools calculate metrics by querying the underlying database. This limits you to the amount of queries you can run and the query timeouts in cases where your metrics require long running queries.

To overcome these limitations you are required to have prior knowledge of what portion of your data might have anomalies and you are also capped at the number of results you get (e.g. 500 rows). Telmai on the other hand gives you unlimited power to investigate all your data without needing to sample, or guess where to look for issues.

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 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 compute 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?​

Telmai integrates with channels like Slack, Email, or to help you communicate and alert the right team at the right time. Telmai notifications include the outliers, drifts, and other inconsistencies in your data. Using APIs you can also auto generate a ticket and assign 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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