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What is Piperr?

Leverage Piperr's ready-made data algorithms to produce outstanding data tailored for a wide array of enterprise stakeholders, including IT, Analytics, Technology, Data Science, and various Lines of Business. If your existing data platform isn't on our list of supported systems, worry not—we will build the necessary connectors at no additional cost. Piperr™ features a standard dashboard equipped with an advanced charting system, and it seamlessly integrates with Tableau, PowerBI, and other visualization tools. You can either take advantage of our machine learning-optimized data algorithms or choose to incorporate your own developed ML models. Bid farewell to protracted DataOps cycles; while your team focuses on enhancing AI models, Piperr will efficiently oversee the data lifecycle for you. Streamline your data operations, from data acquisition to test data management, with Piperr’s readily available data applications. With Piperr™, you are provided with the vital tools required to impose structure on data disorder within your organization. Opt for Piperr™ for all your data processing needs and witness your operational efficiency reach new heights, paving the way for smarter decision-making and innovation.

What is DataKitchen DataOps Automation?

DataKitchen is a data quality, observability, and DataOps automation platform developed to help organizations manage reliable data pipelines and analytics processes. The platform combines automated testing, operational monitoring, workflow orchestration, and deployment management across existing enterprise data infrastructure. Its DataOps Data Quality TestGen product profiles database tables and columns to identify data characteristics, detect anomalies, and automatically generate validation tests. TestGen analyzes 55 column characteristics and supports 34 automatically generated test types, along with configurable business rules and custom SQL validations. Data quality monitoring helps teams identify issues involving missing values, duplicate records, schema changes, unusual data distributions, and other conditions that may affect analytics accuracy. DataOps Process Observability monitors the complete journey of data from source systems through transformation pipelines and analytical applications to downstream consumers. Its unified event monitoring capabilities collect execution statuses, operational metrics, logs, and test outcomes from connected data tools to provide visibility into pipeline health. Rule-based alerting notifies teams about failures, late data arrivals, and test regressions through integrations with email, Slack, Microsoft Teams, and Jira. DataOps Automation enables organizations to coordinate pipelines across different tools, create isolated development environments, reuse workflow components, and automate deployment from development through production. The platform supports integrations with Snowflake, Databricks, BigQuery, Amazon Redshift, PostgreSQL, Airflow, dbt, Azure Data Factory, and other data engineering technologies.

Media

Media

Integrations Supported

Integrations Supported

Docker
Git
GitHub
Google Cloud BigQuery
Jenkins
Jira
Kubernetes
PostgreSQL
SQL Server
Slack
Snowflake

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Online Training
On-Site Training

Training Options

Not specified

Company Facts

Organization Name

Saturam

Date Founded

2014

Company Location

United States

Company Website

www.piperr.io

Company Facts

Organization Name

DataKitchen

Date Founded

2013

Company Location

United States

Company Website

datakitchen.io

Categories and Features

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge

DataOps

Not specified

Categories and Features

Automated Testing

Not specified

Data Observability

Not specified

Data Pipeline

Not specified

Data Quality

Not specified

DataOps

Not specified

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