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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Bigeye vs Acceldata

Data Observability Product Capability Comparison

Explore Acceldata

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

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

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

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

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

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

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

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

Products

Bigeye vs Acceldata

Explore How Acceldata Excels in Comparison