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
Collibra provides data quality for on premise and cloud data sources.
Data observability for on premise and cloud
Structured & unstructured data + files
Streaming data (Kafka, Mar 2024)
Flexible 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
Collibra observes data in your repositories via a data governance approach instead of the actual data flows through pipelines, actual queries run by users, etc.
Observability for data in sources across on-premises and cloud data sources
No observability for pipelines, usage, compute, and cost.
Identify issues at consumption zone which increases costs, time, and complexity root cause analysis
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: sizing, run status, warehouse/cluster optimization, query optimization, query fingerprinting, Data partitioning and clustering optimization, best practice enforcement
Users: Workload usage, alerts, recommendations
Cost: Spend by org unit, query level spend, chargebacks, forecasting
Collibra only looks at the data that is part of your cloud data warehouse and the queries against the cloud data warehouse.
Data: behavior, schema, shape, and custom monitors
Pipeline: Indirect monitoring of data pipeline infrastructure
Compute: Not available
Users: Not available
Cost: Not available
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
Collibra data checks are based around SQL rules limiting its ability to data stored in neat tables
Distributed architecture for parallel execution and capacity
Policy execution decoupled from the data
Limited shift left capabilities
Push down and pull up policy capabilities
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
Collibra is unable to create rules and policies leveraging the full power of code.
Create policies using OOTB rules or custom SQL
No ability to create rules using coding languages
Policy templates are available
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
Automatic policies are data behavior, schema, and shape.
Automatically assigns policy for data behavior, schema and shape
Unavailable
May detect data changes but limited actions beyond alerts.
Basic human in the loop limited to approve/reject actions
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
Collibra Data Quality does not have data lineage. Must purchase the Collibra Data Lineage product as an add on.
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.
Tracks the data performance but missing infrastructure attributes.
No true pipeline metrics. Only supports data rules, behavior, schema, outliers, dupes, shapes, patterns, and records
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
Shift left capabilities will alert, but there are no methods to stop the bad data
Notifications in product and email
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
There is no budgeting or cost capabilities.
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
There are no recommendations for sizing or performance optimizations
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
No budgeting or cost capabilities.
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.
Leverages AI profiling to learn what is “normal” and create rules for data set.
Recommended root cause analysis
Acceldata provides detailed end to end alerting across the data ecosystem, infrastructure, and data zones. This speeds root cause analysis.
Not available
GenAI assisted rules
GenAI translates natural language inputs into data quality rules
Collibra AdaptiveRules automatically observes and generates alerts based on numeric changes over time.
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
Enterprise Grade Security, Integrations and Scale
Integrations - Cloud data sources
Acceldata integrates into the standard cloud data sources including Snowflake, Databricks, Athena, S3, etc
Integrates with cloud data warehouses like Snowflake and Databricks.
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.
Integrates into many on-premises data sources.
On-premises data sources
No Kafka
Security Certifications & Secure Data
Acceldata meets various regulatory compliance and certification requirements including SOC-2 Type 2 and ISO 27001. The product regularly undergoes security and penetration testing.
SOC 2 Type 2
ISO 27001
HIPAA, GDPR, CCPA
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
No public available information about policy capacity or data size..
