Anomalo vs Acceldata

Acceldata is the most complete
Enterprise Data Observability solution

While Anomalo offers data observability functionalities, it falls short compared to Acceldata's comprehensive approach, particularly for organizations with hybrid or on-premises deployments.

Observability across entire data landscape

All data, everywhere

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

Limited Coverage:

• Focuses on cloud data warehouses, with minimal support for on-premises systems.
• Primarily supports structured data; lacks support for unstructured data, files, and streaming sources like Kafka.

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

Incomplete Observability:

• Narrow scope: Covers only data, neglecting pipelines, infrastructure, compute, cost, and
  user observability.
• Limited user insights: Focuses mainly on data quality alerts with minimal visibility into user behavior.

Depth and breadth of data observability coverage

Granular & comprehensive coverage of wide variety of metrics

• Data: Data quality, reconciliation, lineage, data drift, schema drift, freshness, cadence & data anomalies.
• Pipeline: Auto discovery, pipeline lineage, run status, alerts, policies and trigger data quality from pipelines
• Compute: Compute & storage 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, warehouses, clusters, tables, queries etc & chargebacks, forecasting, etc

Data Quality Focus with Limited Scope:

• Reactive: Detects data quality issues post-ingestion; lacks proactive measures like lineage visualization and root cause analysis.
• Rule Creation: Supports basic and intermediate rules; limited for complex rules using standard programming languages.
• Cloud-focused: Observability is restricted to cloud data warehouses and tables.

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

Scalability Uncertainties:

• Claims scalability but performance at large scales and beyond cloud environments is unclear.
• Designed for cloud data warehouses only.
• Policy execution is tightly coupled with the data.

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

Supports basic rules and custom SQL but lacks advanced rule creation capabilities.

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

AI and Recommendations:

• Uses ML to suggest data quality checks and policies based on historical data patterns.
• Basic human-in-the-loop capabilities for recommendation approvals.

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

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

Visibility and Root Cause Analysis:

• Provides data quality visibility within cloud data warehouses only.
• Limited to no root cause analysis or lineage visualization.

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

Observability is cloud-focused with basic pipeline metrics and limited infrastructure metrics:

• Pipeline metrics: efficiency, throughput, task error rate, resource utilization
• Primarily focuses on cloud-native environments only

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

Primarily focused on improving data quality with anomaly detection and resolution:

• Real-time alerts and notifications
• No support for data quarantine or circuit breaker features.

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. 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

Cost and Performance Optimization:

• Identifies cost and performance anomalies at the query level only.
• Lacks visibility into underlying infrastructure costs.

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

Narrow AI Focus on Limited Recommendations:

• Offers basic recommendations for query performance optimization.
• Inadequate recommendations based on historical query performance data
• No infrastructure sizing recommendations

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

Anomalo lacks query-level cost analysis and cost tracking:

• No visibility into historical spend rates within cloud data warehouses
• No showback and chargeback 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 to analyze data patterns and suggest relevant data quality rules
• Continuously improves recommendations based on feedback and data changes

Anomalo provides AI-based recommendations for data quality checks only:

• Lacks advanced functionalities like natural language rule creation or broader anomaly detection beyond data quality.
• Uses ML to analyze data and suggest relevant rules

Recommended root cause analysis

Acceldata provides detailed end to end alerting across the data ecosystem, infrastructure, and data zones. This speeds root cause analysis. 

• Integrates AI to identify the root causes of data issues quickly and accurately
• Offers actionable insights to resolve detected anomalies

Anomalo offers AI-driven root cause analysis for detected data anomalies:

• Provides insights into potential causes of data issues within the cloud data warehouse environment
• Focuses on helping users understand and resolve data quality problems

GenAI assisted rules

GenAI translates natural language inputs into data quality rules

• Allows users to create complex data quality rules using simple natural language commands
• Simplifies the rule creation process and reduces the need for technical expertise

Anomalo supports creating data quality rules through a user interface:

• Limited or no support for natural language input to generate data quality rules
• Focuses on ease of use through a no-code/low-code approach

AI assisted data freshness and completeness recommendations

AI supplemented Copilot streamlines validations against data tables and recommends new policy settings for use:

• Ensures data freshness and completeness through continuous AI monitoring
• Provides proactive recommendations to maintain data quality over time

Uses AI to monitor data quality continuously and
suggest improvements:

• Focuses on maintaining high data quality standards, but within cloud environments only

Enterprise Grade Security, Integrations and Scale

Integrations - Cloud data sources  

Acceldata integrates into the standard cloud data sources including Snowflake, Databricks, Athena, S3, etc

Anomalo’s integration is limited to popular cloud data warehouses only:

• Supports cloud data sources including Snowflake, Redshift, BigQuery, and Azure Synapse Analytics

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 Support only:

• Primarily designed for cloud-native environments, hindering its suitability for hybrid environments.
• Does not natively support streaming data sources like Kafka

Enterprise Compliance and Certifications 

• SOC 2 Type 2
• Encrypted data at rest and in motion

Uncertain Compliance:

• While Anomalo likely has security measures, their compliance certifications are not readily apparent.
• SOC 2 Type 2 certified with encrypted data at rest and in motion.

Enterprise scale and performance

Acceldata is operating at Petabyte data scale, 1000s daily rule runs, 100+ million record assets

• 1000s daily rule runs, 100+ million record assets
• Rules and policy runs do not affect the data warehouse

Operates efficiently within cloud environments but lacks clarity on compliance certifications and hybrid support.