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Data Reliability for the Enterprise

Automate data quality and reliability at scale across the entire data supply chain, from ingestion to consumption.

Benefits delivered by Acceldata Torch

Eliminate Blind Spots

Go beyond basic data quality checks to predict and prevent unexpected issues

Increase Coverage for Large Environments

Automate manual tasks to expand data quality & reliability coverage to allow small teams to manage large data environments

Build Data Trust

Accelerate innovation & empower your data community with transparency and insight into performance and quality to accelerate data adoption and business value

Automatically scan, analyze, and catalog data assets

  • Data Profiling: Automated metadata capture, data profiling, and auto-tagging for context and sensitivity

  • Data Catalog: Simple, secure, faceted search with visibility into data quality & reliability metrics and historical comparisons

  • Metadata Enrichment: Crowd-source data quality with annotations, ratings & tags. Define custom views of data assets to support complex business and technical use cases. APIs to support integration with external systems.

Data Quality

Expand coverage with automated data quality monitoring

  • Data Quality Automation: Automatic data quality rule recommendations to save time, increase accuracy, and improve quality coverage. Support for complex rules, including formulas and custom code. Rule reuse across policies and assets to improve efficiency and accuracy.

  • Data Reconciliation: Comparisons between diverse data sources and targets provide flexibility. Transformations such as aggregation, data blending, and calculations can be accommodated.

  • Drift & Anomaly Detection: Data drift monitoring and anomaly detection is both automated & configurable. Schema drift monitoring can be enabled with a single click.

Pipeline monitoring

Ensure data is efficiently delivered on time,
every time

  • Instrumentation: APIs and SDKs allow any pipeline to become observable. Enhanced capabilities are available for Apache Airflow. Support for custom metadata simplifies support and maintenance.

  • Navigation & Drill-down: A single pane of glass to observe performance, quality and reliability. Drill-down for specific pipeline executions to inspect data processes and data assets, including data violations. Navigate across related data assets for faster root cause analysis

  • Pipeline Comparisons: Rapid identification of bottlenecks and trends enables faster troubleshooting and performance tuning to predict and prevent incidents. Different pipeline designs can be compared to optimize performance and/or cost.


Torch integrates with your most critical data systems

Amazon Redshift
Amazon S3
AWS Glue
Azure SQL
Google Cloud Storage
Burzin Engineer, Founder & Chief Reliability Officer, PhonePe

Burzin Engineer, Founder & Chief Reliability Officer, PhonePe

"Acceldata supports our hyper-growth and helps us manage one of the world’s largest instant payment systems. PhonePe’s biggest-ever data infrastructure initiative would never have been possible without Acceldata."
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Ashwin Prakash, PubMatic Data Analytics Engineering Leader

Ashwin Prakash, PubMatic Data Analytics Engineering Leader

"Acceldata provided the data observability tools and expertise to improve our data pipeline reliability. They helped us optimize HDFS performance, consolidate Kafka clusters, and reduce cost per ad impression, which is one of the most critical performance metrics.”
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Wanlapa Linlawan, Head of Analytics Platform, True Corporation

Wanlapa Linlawan, Head of Analytics Platform, True Corporation

"Acceldata’s tools fixed our analytics pipeline issues, improved visibility into our data systems, and recommended ways to scale and optimize our systems to meet future requirements. They helped True Corporation transition to open-source technologies, allowing us to reduce licensing costs, while delivering mission-critical analytics across the enterprise.”
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Ready to start your data observability journey?

Start a free trial or request a demo to begin your
data observability journey.