Does my data leave the premises?

No, Acceldata observes your data inline so it is highly scalable and secure.

What types of data can you manage?

We observe structured, unstructured and streaming datasets.

Do you provide recommendations?

Yes, we provide recommendations on data quality rules to use based on the data context and several others.

Do you support on-prem and cloud data?

Yes, we can observe data that’s on premises and in the cloud. This makes us ideally suited for cloud migration initiatives.

Can I customize data quality rules?

Yes, you can customize data quality rules and specify exactly where they apply.

Fine grained RBAC, each feature of ADOC can be accessed by users only if they have the required permissions. RBAC also extends to applicable API operations.

Monte Carlo Data
vs Acceldata

All-in-One Enterprise Data Observability improves data quality, pipeline reliability, platform performance, and spend efficiency.

Explore Acceldata

The most comprehensive data observability platform for modern enterprises operating at petabyte scale

All-in-One Solution

Feature-rich and easy to use, Acceldata addresses all observability needs, including data quality, drift, reconciliation, pipeline and infrastructure monitoring, and FinOps. Monte Carlo lacks infrastructure monitoring and cost optimization.

Shift Left Approach

Unlike Monte Carlo, which focuses only on the consumption zone, Acceldata integrates observability from the initial landing zone to consumption. This shift-left approach can reduce the cost of bad data by up to 38x, while Monte Carlo identifies issues after costs are incurred.

Petabyte Scale Performance

Acceldata’s unique data plane architecture, horizontal scalability, and in-memory processing enable enterprises to process billions of rows of data in-line with all business rules and policies, ensuring confident implementation at scale.

Hybrid and Multi-Cloud

Acceldata offers a single platform for all data stack locations—on-premises, cloud, hybrid, or multi-cloud. Thereby providing a unified view of hybrid data and pipelines. In contrast, Monte Carlo focuses solely on the cloud.

AI-assisted Data Observability

Choose from detecting anomalies with built-in AI algorithms, rapid or cold starts using basic rules and policies, and many other capabilities to enhance enterprise observability initiatives.

Enterprise Class Security

Our unique data plane architecture, horizontal scalability, in-memory processing, and other techniques enable enterprises to process billions of data rows, checked in-line against multiple business rules and policies. This allows enterprises to confidently implement Acceldata at scale.

Acceldata is the most complete
Enterprise Data Observability solution

Cloud or on-premises, all data stacks, all data types, comprehensive integrations, petabyte scale

Data on-premises, data in the cloud, various data types

Data observability for both on-premise and cloud deployments, allowing flexibility for customers

Data observability regardless of location
Structured & unstructured data + files
Streaming data (Kafka, in production 1+year)
Adapts to your data ecosystem design

Monte Carlo only provides data observability for SaaS systems leaving gaps with on premise data systems

Data observability for cloud data sources only
Structured & unstructured data only
Streaming data (Kafka, beta)
Rigid data ecosystem design

Observability for repositories, pipelines, compute, and usage across all zones. 5 pillars of data observability as defined by Gartner

Supports all 5 pillars of data observability as defined by Gartner

Data, data pipelines, infrastructure and compute, cost, usage/users
All zones - Landing, enrichment, and consumption
Catch issues and anomalies early

Monte Carlo only looks at data once it is in your data warehouse or data lake.

No infrastructure, compute, cost or users
Consumption zone only
Identify issues in consumption zone which increases remediation costs

Depth and breadth of data observability coverage

Granular coverage of wide variety of metrics

Data: Data quality, reconciliation, lineage, data drift, schema drift, freshness, data cadence and data anomalies.
Pipeline: Auto discovery, pipeline lineage, run status, alerts, policies
Compute: Compute sizing and optimization, query fingerprinting and optimization, data partitioning and clustering optimization, best practices, etc
Users: Workload usage, alerts, recommendations
Cost: Spend by org unit, query level spend, chargebacks, forecasting, etc

Monte Carlo only looks at the data that is part of your CDW and the queries against the CDW.

Data: Freshness, volume, schema, data quality, completeness, lineage, etc
Pipeline: Freshness, volume, schema

Compute: Only Query performance
Users: Not available
Cost: Not available

Ability to create, run, and manage data checks on-prem and cloud needed for enterprise scale

Architected and field-tested to support the scale of large enterprises

Distributed architecture for parallel execution and capacity across entire data landscape
Policy execution decoupled from the data
Shift left of quality rules detect issues at source
Field proven policy capacity and performance at exabyte scale

Monte Carlo is only able to run data quality checks on cloud data sources and is limited rule scaling. They are blind to traditional on-prem data sources

Architected to run checks directly on data
Policy performance dependant on DW compute capacity
DQ checks only at consumption point
No DQ checks for on-prem sources
Field proven capacity unknown.

Create sophisticated custom business rules and policies

Business and regulatory requirements can be highly complex and disparate, having the full flexibility of programming code simplifies compliance

Create policies using OOTB rules or custom SQL
Create complex logic and checks with standard coding languages (Python, Scala, Java, JavaScript)

Policy reuse and usage analytics

Monte Carlo is unable to create rules and checks leveraging the full power of code

Create policies using OOTB rules or custom SQL
No ability to create rules using coding languages
Policy reuse and usage analytics

Automatic recommendations for data policy assignments and enforcement

Acceldata provides automatic recommendations for data rules and policies to quickly increase your data quality

Model based engine recommends policies based on data profiling
Identifies gaps for remediation based on similar fields in multiple datasets
Continuous AI/ML learning engine based on profiles, rules library and derived auto-tags
Optional human in the loop guardrails and approvals for AI/ML recommendation and rules

Provides automatic recommendations for rules and policies

No ML based recommended policies
Does not identify gaps based on similar data set fields
No AI/ML recommendation for policies and rules

Visual data lineage and deep metrics at each hop to identify data problem source and causes

Visibility into the full data lifecycle from source files in the landing zone through the consumption zone tables ensure you get fresh and complete data to meet SLAs

Data observability for files, databases, clusters, warehouses, tables, columns
On-premises and cloud data sources
Full data tracking from landing through consumption

Monte Carlo data lineage is focused on data table and column lineage and lacks visibility into the landing zone making it difficult to monitor freshness and completeness

Unable to monitor files, but tracks databases, warehouses, tables, and columns
On-premises (beta) and cloud data sources
Data tracking for the consumption zone

Observe data pipeline and infrastructure behavior and performance

Observability into the behavior, performance, and reliability of the data and infrastructure pipeline

Pipeline-metrics: efficiency, uptime/downtime, throughput, task error rate, resource utilization,
Infrastructure metrics: Latency, error rates, data loss, hw utilization, queue length

Monte Carlo does not monitor the underlying data pipeline for issues. It only observes data table changes and looks for attributes for freshness, volume, and schema changes.

No true pipeline metrics. Defines data pipeline as data freshness, volume and schema changes
No infrastructure metrics

Alert, quarantine, and circuit breaker policies isolate bad data and prevent downstream impacts

Better data quality with data lineage and trace back from the data warehouse to the data entry point (landing zone). Automatically detect and root cause issues before they become expensive and time consuming to fix.

Real-time alert, notifications, and throttling (noise)
Quarantine policy
Circuit breaker

Monte Carlo shift left capabilities requires more time and money as it starts and ends at the data warehouse, unable to observe files and input sources

Notification for email, Slack, Jira, and other channels with throttling
No data quarantine policy
Basic circuit breaker

Notification of cost and performance anomalies for both queries and infrastructure

Acceldata provides deep detailed understanding and visibility into the cost and performance of the query down to the infrastructure level. Includes maintaining historical query and budgetary trend data that can factor in seasonality

Identify and optimize long-running or inefficient queries
Identify similar poorly written queries
Find unused clusters, tables and warehouses etc.
Isolate waste from unneeded queries
Under-provisioned infrastructure

Monte Carlo is limited in capabilities. It can assign a cost center to queries and monitors if a query runtime exceeds expected behavior. No budget tracking

Long-running queries limited to percentage overage or absolute runtime.
Unable to find poorly written queries
Unable to find waste across clusters, tables, and warehouses.
Lists no read tables (30Days)
Unable to advise on infrastructure sizing

Provide automatic recommendations for query and infrastructure sizing and performance optimizations

Acceldata looks across queries and provides recommendations for optimizing queries and the underlying infrastructure.

Recommendations for right sizing cloud data warehouse and data lakehouse
AI based SQL query optimization
Codify Snowflake/Databricks best practices via automated scanning and recommendations

Monte Carlo only provides recommendations that queries should be adjusted if they exceed historical runtimes.

No recommendations for right sizing
No AI based SQL query optimization
No recommended best practices for Snowflake and Databricks

Monitor and analyze data warehouse cost including show back, charge back, and budgeting

Maintains historical spend rates across queries, teams, and tracks spends to budget allocations providing full spectrum visibility. Enables FinOps with show back and chargeback capabilities.

Directly tracks and reports spend and budget
Query show back and charge back

Monitors cost for individual queries. Lacks historical trends vs budget allocations

No cost spend and budget capabilities
No query show back and chargeback

Recommended rules and policies including AI based recommendations

Acceldata understands your data elements and automatically recommends rules and policies for use.

Monte Carlo data profiling will recommend common freshness and volume monitors

Recommended root cause analysis

Acceldata provides detailed end to end alerting across the data ecosystem, infrastructure, and data zones. This speeds root cause analysis.

Monte Carlo states that they do root cause analysis insights, but this appears to only be alerting. It is unclear how well it works or how valuable this information is as only the consumption zone is visible.

GenAI assisted rules

GenAI translates natural language inputs into data quality rules

Monte Carlo does not have GenAI created rules

AI assisted data freshness and completeness recommendations

AI supplemented Copilot streamlines validations against data tables and recommends new policy settings for use.

AI assisted freshness and volume detectors only.

Integrations - Cloud data sources

Acceldata integrates into the standard cloud data sources including Snowflake, Databricks, Athena, S3, etc

Extensive integrations into CDW and BI tools, but missing file storage and onsite systems

Integrations - On-premises data sources

Acceldata integrates into many of the the standard on-premises data sources including: Oracle, MySQL, SAP HANA, MongoDB, HDFS and more

On-premises data sources are production released
Kafka streaming

Monte Carlo does not integrate with traditional on-premises data sources

On-premises data sources at beta
No Kafka streaming

Enterprise Compliance and Certifications

SOC 2 Type 2
Encrypted data at rest and in motion

SOC 2 Type 2, SOC 3 Type 2
Encrypted data at rest and in motion

Enterprise scale and performance

Acceldata is operating at Petabyte data scale, 1000s daily rule runs, 100+ million record assets

Acceldata is operating at Petabyte data scale, 1000s daily rule runs, 100+ million record assets
Runs and policy runs do not affect the data warehouse

Unknown ability to run daily rule runs

Unknown data scale, daily rule runs, and record counts
Running rules and policies on top of the DW, impacts data warehouse compute cost and query performance

Acceldata vs
Monte Carlo Data

Why Enterprises Choose
Acceldata over Monte Carlo

xLake: The Enterprise Reasoning Engine

Agentic AI: Autonomous Intelligence

Petabyte Power: Reliability at Massive Scale

Converged AI Capabilities: One Unified Platform

Built for Humans: Multi-Persona Intelligence

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Monte Carlo Data

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Monte Carlo Data
vs Acceldata