All-in-One
Enterprise Data Quality 
Monitoring

Monitor Data 

Pipelines

Drift

Quality

Anomalies

Spend

Users

Infrastructure

Models

Performance

Privacy

Acceldata enterprise data quality monitoring provides comprehensive insights into your
data stack to improve enterprise data quality, pipeline reliability, platform performance, and spend efficiency.

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TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

Gartner® Innovation Insight: Data Observability Enables Proactive Cloud Data Quality

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YOUR COSTS

Control costs while growing Databricks & Snowflake Adoption

Stop unexpected and runaway costs as you scale your cloud data environments
Optimize your cloud data platform spend by identifying poor-performing queries
Gain confidence in your spend plan with accurate chargeback across multiple teams, organizations, projects, etc.
Eliminate cost overruns with anomaly detection, codified guardrails, and alerts
Why Acceldata

Top reasons why enterprises choose Acceldata's Data Quality Monitoring Platform

Comprehensive

Only Acceldata offers the breadth and depth of enterprise data quality tools required by modern data teams. 

The Acceldata Data Observability platform includes comprehensive enterprise data quality frameworks, dashboards, and tools with multi-layer data insights across data assets, data pipelines, data infrastructure, and data users for data reliability, cost optimization, and infrastructure and pipeline performance management.

Enterprise-ready

Acceldata’s AI-driven automation and recommendations allow data teams to easily scale out their enterprise data quality coverage and continuously optimize data infrastructure. 

Automated alerts and rich data quality, pipeline, and data infrastructure dashboards keep data teams apprised and help resolve problems quickly and efficiently.

Highly Automated

Acceldata provides you with top-notch automated enterprise data quality tools and frameworks to optimally scale your enterprise data quality and infrastructure to manage costs and data engineering resources. 

Only Acceldata covers the diverse data faced by large organizations and can easily apply and execute data quality checks at an enterprise scale.

Designed for Data Teams

Acceldata is designed for enterprise data teams by delivering insights they need to work more effectively and ensure high ROI on data investments. 

Acceldata eliminates data outages and fire-fighting and frees data teams to innovate on new data products.
Integrations

Integrated with all the tools you already use

Snowflake

A cloud-based data warehousing platform that separates storage and compute resources for scalability and flexibility.

Teradata

A fully scalable relational database management system produced by Teradata Corp, primarily used to manage large data warehousing operations.

Line

Line is an app that powers instant communications on electronic devices such as smartphones, tablet computers and personal computers.

SQL Server

Microsoft's relational database management system, offering a broad set of enterprise data management, business intelligence and analytics applications.

Microsoft Teams

Microsoft Teams is a unified communication and collaboration platform that combines chat, video meetings, file storage, and application integration.

Spark

Apache Spark is an open-source, distributed computing system used for big data processing and analytics, offering ease of use and speed.

Slack

Slack is a communication and collaboration tool that offers real-time messaging, file sharing, project management, and integrations with various apps for seamless communication and productivity.

SingleStore

A distributed, relational database that delivers high performance, scalability, and concurrent transactions across a unified database.

ServiceNow

A cloud computing platform-as-a-service (PaaS) that provides enterprise service management, reporting metrics, self-service, and routing for IT service management, as well as business process automation

PostgreSQL

A powerful, open-source object-relational database system known for its proven architecture, reliability, and data integrity.

NiFi

Apache NiFi is an integrated data logistics and simple event processing platform for automating the movement of data between disparate systems.

Opsgenie

An incident management platform by Atlassian that ensures critical incidents are never missed, and actions are taken swiftly and effectively.

PagerDuty

A digital operations management platform that provides reliable incident notifications via email, push, SMS, and phone, as well as automatic escalations, on-call scheduling, and other functionality to help teams detect and fix infrastructure problems quickly.

Oracle

A powerful, object-relational database management system (ORDBMS) from Oracle Corporation, known for its scalability, reliability, and wide set of features.

OpenID

An open-standard and decentralized authentication protocol that allows users to be authenticated by certain co-operating sites (known as Relying Parties) using a third party service.

Okta

An identity management platform providing secure identity management with Single Sign-On, Multi-factor Authentication, Lifecycle Management, and more.

ADLSV2

Azure Data Lake Storage Gen2, Microsoft's scalable and secure data lake solution that combines the power of a Hadoop compatible file system with integrated hierarchical namespace.

AZURE SQL warehouse

Microsoft's cloud-based, scale-out database service that offers massively parallel processing for data warehousing.

Airflow

An open-source platform to programmatically author, schedule, and monitor workflows.

Amazon Redshift

Amazon's fully managed, petabyte-scale data warehouse service in the cloud that makes it simple and cost-effective to analyze all your data.

Amazon S3

Amazon Simple Storage Service (S3) is a scalable, high-speed, web-based cloud storage service designed for online backup and archiving of data and applications.

Athena

A serverless, interactive query service by Amazon Web Services that makes it easy to analyze data directly in Amazon S3 using standard SQL.

BigQuery

Google's serverless and highly scalable data warehouse designed to make data analysts more productive with unmatched speed for SQL queries.

DB2

IBM's family of hybrid data management solutions, designed to provide robust capabilities for transactional and analytic workloads in on-premise, cloud, or hybrid environments.

Databricks

An AI-driven, open-source platform with collaborative features for big data processing and machine learning.

Emails

Electronic mail is a method of exchanging messages ("mail") between people using electronic devices, a critical technology for personal and professional communication.

Google Cloud Storage

Google Cloud Storage is a RESTful online file storage web service for storing and accessing data on Google Cloud Platform infrastructure.

HBASE

Apache HBase is an open-source, non-relational, distributed database modeled after Google's Bigtable, designed to provide random, real-time read/write access to big data.

HDFS

Hadoop Distributed File System is a distributed, scalable, and portable file-system written in Java for the Hadoop framework, designed to scale up from single servers to thousands of machines.

Hangouts

Google Hangouts is a communication software that includes messaging, video chat, SMS, and VOIP features.

Hive

Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis, primarily providing SQL interface.

Impala

An open-source, native analytic database for Apache Hadoop, Impala provides high-performance, low-latency SQL queries on data stored in Hadoop Distributed File System.

Get a Personalized Demo

We’re excited to give you a personalized demo that will show you how to:
Reduce costs in your cloud data-warehouse by 30% or more
Improve data rules processing performance by more than 10X with automated data quality monitoring, data reconciliation and detection of schema drift, data drift and more
Reduce data costs and MTTR by 96% by monitoring data pipelines end-to-end, identifying and eliminating performance bottlenecks in data streaming, batch processing, queries, and other workloads