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.

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DQ Labs
vs Acceldata

Data Observability Product Capability Comparison

Explore Acceldata

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

DQ Labs provides very limited data quality for traditional and cloud data sources.

Limited data observability for cloud and on-premise data sources
Observes structured data only
Streaming data not observed
Very 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

Observes data in cloud and on-premises but very limited in what it observes.

Limited OOTB quality rules covering duplicate, freshness, schema, and volume.
No support for pipelines, compute and usage/users.
Very limited and rigid integrations across data landscape

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

Very limited data quality functionality with a focus on data freshness and basic data check. Missing numerous pillars of data observability

Data: Focused on data freshness, duplicates, volume, and data schema only.
Pipeline: Partial, with limited data lineage focused on the data warehouse
Compute: Does not monitor
Users: Does not monitor
Cost: Does not monitor

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

Data checks are based on imported data from limited connectors

Metadata plane available for very limited data quality and data observability checks
Policies are limited to data types attributes
Limited shift left capabilities
Field proven capacity unknown and very limited rules 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

Custom rules with SQL expressions only. No ability to leverage the full power of code

OOTB and ML-based assigned SQL rules
SQL based custom rules only
No ability to create rules using coding languages
Policies are just data type attributes

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 profiling leverages a limited OOTB rule set.

ML-based recommendations; Custom rules limited to SQL expressions
Unavailable
Limited to only learned data, no human inputs into rule building based on business requirements
Enforced dependency on ML-rules only, no human guardrails

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

Root cause analysis only shows where an anomaly has occurred in the data lineage map across tables. Does not provide insights into the cause of the issue or remediation steps.

Limited integrations and no monitoring beyond the interactions with databases or data warehouse
Limited on-premises and cloud data sources
Limited data tracking for databases and warehouses interactions

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

No monitoring of the underlying data pipeline. Only observes data table changes and looks for attributes around freshness, volume, and schema changes. It shows data lineage relationships at the table level.

No true pipeline metrics. Defines data pipeline as data freshness, volume and schema changes when interacting with tables only
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

Data quality checks; The rest of the data observability pillars (eg. Infrastructure, compute, cost, etc)

Notification for email, Slack, Jira, Teams, and other channels
No data quarantine policy
No circuit breaker capabilities

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

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 Snowflake/Databricks best practices via automated scanning and recommendations

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

There is no budgeting or cost capabilities.

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.

ML-based data profiling and recommended rules, but with limited data quality coverage

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

Converts natural language into SQL rules

Not available
Custom rules must be created via SQL or point and click.

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

Limited integrations that don't cover the entire enterprise data landscape.

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

Limited on-premises data sources.

Limited on-premises data sources
No Kafka

Enterprise Compliance and Certifications

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

SOC2 compliant

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 publicly available information about policy scaling capacity or data size

Products

DQ Labsvs Acceldata

Ready to start your data observability journey?

DQ Labs
vs Acceldata

Ready to start your
data observability journey?