Bad data causing regulatory fines, churn and marketing disasters?

Let our Acceldata platform take care of your data and pipeline controlsin on-prem, cloud and hybrid environments.

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Ensuring data quality and data pipeline controls across complex data landscapes is painful.

Billions of critical data records from 1000's of data sources.Complex tech and data landscapes - on-prem, cloud and hybrid environments. Thousands of pipelines that transform critical financial and customer data.

Free yourself from managing data quality controls across complex environments

Stop juggling billions of critical data records from 100's of data sources

Don’t let bad data or data pipeline failures cause non-compliance or marketing blunders

X-ray Databricks account usage, Optimize ROI with precise tips.

Continuous Data and Pipeline Quality  Across Any Deployment Model

Your partner on your journey:
whether on-prem, on the cloud or anything in between
Retain your data quality controls:
across on-prem or hybrid environments - don’t switch vendors when you migrate to the cloud!
Multiple native integrations:
for data quality controls, cost optimization, pipelines, notifications & SSO - on-prem and in the cloud

Speed up migration to Databricks Lakehouse

Acceldata’s capabilities such as data drift, schema drift, reconciliation help speed migration to Snowflake.  Migrate thousands of pipelines across hundreds of sources with ease.
9 PB
of stagnant data eliminated
in first 48 hours
reduction in MTTR
from 14 days to 4 hours
storage consumption
cost reduction by $350K

Features of  Enterprise Data Observability

Data Profiling

Automatically scan, analyze, and catalog data assets - to understand the structure of your data before gaining multi-faceted visibility into data quality & reliability metrics and historical comparisons.

Data Quality Controls

Automated data quality monitoring and anomaly detection using intelligent recommendations and reusable policies across diverse data sources and targets.

Pipeline Controls

Ensure reliable data pipeline performance and throughput and avoid exploding costs by continuous observability across your Kafka, Apache Airflow and other data pipelines.

Finetune Compute

Get the most bang for your buck from your data  and prevent outages with continuous, real-time insights and  ML-driven recommendations -  by scaling warehouses, clusters and workloads up or down for optimal operational performance.

Reduce Spend

Proactively manage and predict future costs to maximize data ROI, through granular cost insights by warehouse, project, cluster, business unit, user and query as well as by identifying over-provisioned and unused resources.

Integrated with all the tools you already use

See all integrations


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


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


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.


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


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.


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


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


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


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


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


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.


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


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.


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


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.


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.


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


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


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.


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


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.


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.


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.


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


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


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