Data on-premises, data in the cloud, various data types
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 production 1+ years)
Adapts to your data ecosystem design
Bigeye has basic integrations into on-prem and cloud deployment data repositories.
Data observability for cloud data sources only
Structured data only
Basic data source integrations
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
Landing zone, enrichment zone, and consumption zone
Catch issues & anomalies early
Observes data in cloud and on-premises but limited in what it observes.
Observes freshness, volume and schema
No support for pipelines, compute and usage/users.
Focus is in the consumption zone looking at how data trends or behaves over time via statistical modeling.
Consumption zone only
Identify issues at consumption zone which increases costs for identification and fixes
Depth and breadth of data observability coverage
Granular coverage of wide variety of metrics
Data: Data quality, data reconciliation, data lineage, data drift, schema drift, data 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.
Limited reliability functionality with a focus on freshness and volume support only for Snowflake, BigQuery, and Redshift
Data: Focused on monitoring freshness and volume.
Pipeline: Does not monitor
Compute: Does not monitor
Users: Does not monitor
Cost: Does not monitor
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
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
Bigeye has limited out of the box data quality checks and scaling capacity. They are focused on data drift. Most quality checks need to be custom created.
Agent on customer VPC
Policy execution decoupled for data but limited number of policies based on shape of data
Very limited ability to shift left due to fewer integrations
Scales to 50k+ tables, but quality rule count, data size and time to execute is 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
Bigeye is unable to create rules and policies leveraging the full power of code.
Create policies using OOTB rules (limited) or custom SQL
No ability to create rules using coding languages
Limited reuse capabilities around rules metrics(rules) and collections(policy).
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
Bigeye provides some recommendations, but the scope of coverage is limited and may not reflect the needs of an enterprise wide tool.
Recommended metrics are limited and basic around freshness, volume, and schema
Identifies gaps, but limited in scope and coverage
Will detect new columns and schema, but limited model capabilities
Basic human in the loop limited to approve/reject metrics and thresholds
30x reduction in data investigation time
Detecting, isolating, and resolving issues at the source
Visual data lineage and deep metrics at each hop to identify data problem source and causes
Complete data lineage and instrumentation enables easy and quick identification of the root cause of data problems. Lineage and observability metrics include:
Monitors files, databases, clusters, warehouses, tables, and columns
On-premises and cloud data sources
Full data tracking from landing through consumption
Bigeye 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 only
Not available
Not available
Observability into the behavior, performance, and reliability of the data and infrastructure pipeline
Acceldata sees and tracks the full data and infrastructure pipeline lineage across the data landscape (landing, enrichment, consumption) for both on-premise and cloud. Ensuring fresh, complete and timely data for business decisions
Does not monitor the underlying data pipeline for issues. Only observes data table changes and looks at attributes for freshness, volume, and schema changes
No true pipeline metrics. Defines data pipeline metrics as data freshness and volume changes
No infrastructure metrics
Alert, quarantine, and circuit breaker policies isolate bad data and prevent downstream impacts
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
Real-time alert, notifications, and throttling (noise)
Quarantine Policy
Circuit Breaker
Bigeye 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
Notification for email, Slack, Jira, and other channels with throttling
No data quarantine policy
No circuit breaker
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
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
Functionality is not available
Unable to see long-running or inefficient queries
Unable to find poorly written queries
Unable to find waste across clusters, tables, and warehouses.
Unable to find waste from unneeded queries
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 best practices from Snowflake and Databricks, prevents best practice drift
Bigeye 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
There is no budgeting or cost capabilities.
No cost spend and budget capabilities
No query show back and chargeback
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
Bigeye has limited recommendations only relating to data where statistical analysis and various freshness checks
Recommended root cause analysis
Acceldata provides detailed end to end alerting across the data ecosystem, infrastructure, and data zones. This speeds root cause analysis
Root cause analysis is indicating the first instance of a data anomaly without proper context as to what might be causing the failure
GenAI assisted rules
GenAI translates natural language inputs into data quality rules
Bigeye 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, but functionality may be limited
Enterprise Grade Security, Integrations and Scale
Integrations - Cloud data sources
Acceldata integrates into the standard cloud data sources (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 (Oracle, MySQL, SAP HANA, MongoDB, HDFS, etc)
Bigeye has various on-premises data sources but misses key sources like HDFS, files, streaming and others
Security Certifications & Secure Data
Acceldata meets various regulatory compliance and certification requirements including SOC-2 Type 2 and ISO 27001. The product encrypts data in motion and at rests. It also regularly undergoes security and penetration testing
Bigeye has SOC-2 Type 2 certification and encrypts data in motion and at rest
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 scaling capabilities
Advertises scaling to 50k+ tables capacity. Unknown daily rule and record count.
Running rules and policies on top of the DW, impacts data warehouse compute cost and query performance
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