Data observability for both on-prem and cloud deployments, allowing flexibility for customers
Data observability regardless of location
Structured & unstructured data + files
Streaming data (Kafka, in prod XX years)
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 the repositories, pipelines, compute, and usage across all zones
Supports all 5 pillars of data observability as defined by Gartners
Data, data pipelines, infrastructure and compute, cost, usage/users
Landing zone, enrichment zone, and consumption zone
Catch issues & anomalies early
Monte Carlo only looks at data once it is in your data warehouse or data lake.
Data, data pipelines
Consumption zone only
Identify issues at consumption zone which increases costs for identification and fixes
Reliability of data and data pipelines
Monitor all the pipelines and easily surface insights regarding the performance, status and reliability of executing pipelines
Direct monitoring of data pipelines infrastructure
Visibility and understanding into the pipeline infrastructure
High performance pipelines = high quality data
Full coverage policies
Monte Carlo only looks at the data that is part of your CDW and the queries against the CDW.
Indirect monitoring of data pipeline infrastructure
Leverages DQ checks to infer root cause
Infrastructure issues can go unseen
100% Data Quality coverage.
Run 1000s of unique data quality checks daily on exabyte scale data.
Ability to create, run, and manage data checks on-prem and cloud needed for enterprise scale
Acceldata is architected and field tested to support the needs of large enterprises with the ability to start observing data using standard and custom policy and rules across all three zones for both on-prem and cloud. This reduces cleanup work, allowing data engineers to better focus on high value activities.
DQ checks across all data sources and pipelines
Policy performance independant from data landscape
Field proven policy capacity and performance capabilities
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.
DQ checks on cloud data sources only
No DQ checks for on-prem sources
Policy performance dependant on DW compute capacity
Create sophisticated custom business rules and policies
Business and regulatory requirements can be highly complex and disparate, having the full flexibility of programing code simplifies compliance
Create policies using OOTB rules or custom SQL
Create complex logic and checks with standard coding languages
Policy reuse and usage analytics
Monte Carlo is unable to create rules and check leveraging the full power of code.
Create policies using OOTB rules or custom SQL
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.
Automatically recommends policies based on data profiling
Provides automatic recommendations for rules and policies.
Automatically recommends policies based on data profiling
30x reduction in data investigation time
Detecting, isolating, and resolving issues at their source
Full insight into data lineage and relationships from source files and streams to CDW and tables ensuring freshness, volume, and completeness
Visibility into the full data lifecycle from source files in the landing zone through to the consumption zone tables ensure you get fresh and complete data meeting business SLAs.
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
Data tracking for the consumption zone
Observability into the behavior, performance, and reliability of the data and infrastructure pipeline
Observability into the behavior, performance, and reliability of the data and infrastructure pipeline
Item A
Item B
Item C
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.
Item A
Item B
Item C
Automatic holistic data lineage and pipeline lineage starting at the consumption zone and shifting left to the landing zone
Get better data quality from the start with lineage and trace back from the data warehouse to the entry of data (landing zone). Automatically detect and root cause issues before they get expensive and time consuming to fix.
Item A
Item B
Item C
Monte Carlo shift left capabilities cost more time and money as it starts and ends at the data warehouse, unable to get to your files and input sources.
Item A
Item B
Item C
Optimize compute performance & control costs
Catch hidden inefficiencies
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. It includes maintaining historical query and budgetary trend data that can even factor in seasonality.
Item A
Item B
Item C
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.
Item A
Item B
Item C1
Provide automatic recommendations for query and infrastructure sizing and performance optimizations
Acceldata looks across queries and provides recommendations for optimizing queries and the underlaying infrastructure.
Item A
Item B
Item C
Monte Carlo only provides recommendations that queries should be adjusted if they exceed historical runtimes.
Item A
Item B
Item C
Monitor and analyze data warehouse cost including show back, chargeback, 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.
Item A
Item B
Item C
Monitors cost for individual queries. Lacks historical trends vs budget allocations
Item A
Item B
Item C
Integrated AI + AI Copilot
AI based data anomaly detection, recommendations, and self service
Recommended rules and policies including AI based recommendations
Acceldata understands your data elements and automatically recommends rules and policies for use.
Item A
Item B
Item C
Monte Carlo data profiling will recommend common rules and policies
Item A
Item B
Item C
Recommended root cause analysis
Acceldata provides detailed end to end alerting across the data ecosystem, infrastructure, and data zones. This speeds root cause analysis.
Item A
Item B
Item C
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 and Monte Carlo is blind to the infrastructure.
Item A
Item B
Item C
GenAI assisted rules
GenAI translates natural language inputs into data quality rules
Item A
Item B
Item C
Monte Carlo does not have GenAI created rules
Item A
Item B
Item C
AI assisted Data Freshness and completeness recommendations
AI supplemented Copilot streamlines validations against data tables and recommends new policy settings for use.
Item A
Item B
Item C
AI assisted freshness and volume detectors.
Item A
Item B
Item C
Enterprise Grade Security, Integrations and Scale
Integrations - Cloud data sources
Acceldata integrates into the standard cloud data sources including Snowflake, Databricks, Athena, S3, etc.
Item A
Item B
Item C
Extensive integrations into CDW and BI tools, but missing file storage and onsite systems
Item A
Item B
Item C
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
Item A
Item B
Item C
Monte Carlo does not integrate with traditional on-premises data sources
