Best Data Quality Software for Google Cloud Platform

Satori is a Data Security Platform (DSP) that enables self-service data and analytics for data-driven companies. With Satori, users have a personal data portal where they can see all available datasets and gain immediate access to them. That means your data consumers get data access in seconds instead of weeks. Satori’s DSP dynamically applies the appropriate security and access policies, reducing manual data engineering work. Satori’s DSP manages access, permissions, security, and compliance policies - all from a single console. Satori continuously classifies sensitive data in all your data stores (databases, data lakes, and data warehouses), and dynamically tracks data usage while applying relevant security policies. Satori enables your data use to scale across the company while meeting all data security and compliance requirements.

Experience Semarchy’s flexible unified data platform to empower better business decisions enterprise-wide. With xDM, you can discover, govern, enrich, enlighten and manage data. Rapidly deliver data-rich applications with automated master data management and transform data into insights with xDM. The business-centric interfaces provide for the rapid creation and adoption of data-rich applications. Automation rapidly generates applications to your specific requirements, and the agile platform quickly expands or evolves data applications.

OvalEdge, a cost-effective data catalogue, is designed to provide end-to-end data governance and privacy compliance. It also provides fast, reliable analytics. OvalEdge crawls the databases, BI platforms and data lakes of your organization to create an easy-to use, smart inventory. Analysts can quickly discover data and provide powerful insights using OvalEdge. OvalEdge's extensive functionality allows users to improve data access, data literacy and data quality.

DQOps is a data quality monitoring platform for data teams that helps detect and address quality issues before they impact your business. Track data quality KPIs on data quality dashboards and reach a 100% data quality score. DQOps helps monitor data warehouses and data lakes on the most popular data platforms. DQOps offers a built-in list of predefined data quality checks verifying key data quality dimensions. The extensibility of the platform allows you to modify existing checks or add custom, business-specific checks as needed. The DQOps platform easily integrates with DevOps environments and allows data quality definitions to be stored in a source repository along with the data pipeline code.

Data visibility and control for security, compliance, privacy, and governance. BigID's platform includes a foundational data discovery platform combining data classification and cataloging for finding personal, sensitive and high value data - plus a modular array of add on apps for solving discrete problems in privacy, security and governance. Automate scans, discovery, classification, workflows, and more on the data you need - and find all PI, PII, sensitive, and critical data across unstructured and structured data, on-prem and in the cloud. BigID uses advanced machine learning and data intelligence to help enterprises better manage and protect their customer & sensitive data, meet data privacy and protection regulations, and leverage unmatched coverage for all data across all data stores.

Ataccama is a revolutionary way to manage data and create enterprise value. Ataccama unifies Data Governance, Data Quality and Master Data Management into one AI-powered fabric that can be used in hybrid and cloud environments. This gives your business and data teams unprecedented speed and security while ensuring trust, security and governance of your data.

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.

With exceptional functionality for data integration, quality and cleansing, maximize the value of all structured and unstructured data in your organization. SAP Data Services software increases the quality of enterprise data. It is part of SAP's Information Management Layer. It delivers timely, relevant, and trusted information to help drive better business outcomes. Transform your data into a reliable, always-available resource for business insights and use it to streamline operations and maximize efficiency. Get contextual insight and unlock the true potential of your data with a complete view of all your information. Access to any size data and any source. Standardizing and matching data can improve decision-making and operational efficiency. This will reduce duplicates, identify relationships and address quality issues proactively. Use intuitive tools to unify critical data whether it is on-premise, in the cloud or within Big Data.

Data management software is used mainly by enterprise business teams. Data management software must be intuitive, automated, and intelligent. Data management activities must also adhere to specific industry and data protection regulations. Data must be accurate, consistent, high quality, and easily accessible to enable business teams to make informed, data-driven strategic business decisions. Integrates data privacy, data quality management and test data management. Service engine-based architecture allows for efficient handling of growing data volumes. Uses a user-defined function framework with python adapter to handle niche data processing needs. This provides a minimal layer of governance for data quality and privacy management.

Empower enterprise data teams with the ability to prevent costly outages before they happen. With efficient time-bound queries, you can quickly scale data quality checks throughout enterprise data pipelines without compromising performance. Utilizing AI models that are specific to DQ, you can monitor and identify data anomalies without having to manually set thresholds. Lightup's solution provides you with the highest level of data quality so you can make confident decisions. Data quality intelligence will help you make confident decisions. Dashboards that are flexible and powerful provide transparency on data quality and trends. Lightup's built in connectors allow you to connect seamlessly to any data source within your data stack. Replace manual, resource-intensive data quality checks with automated ones to streamline workflows.

DataTrust accelerates test cycles and reduces the cost of delivery through continuous integration and continuous distribution (CI/CD). It is a powerful tool for data validation, data reconciliation, and data observability at a large scale. It is code-free and easy to use. Re-usable scenarios allow you to perform comparisons, validations and reconciliations. Automate your testing process and be alerted to any issues that arise. Interactive executive reports that provide insights into the quality dimension. Filters to customize drill-down reports. Compare row counts for multiple tables at the schema level. Checksum data comparisons can be performed for multiple tables. Rapid generation of business rule using ML. Flexibility in accepting, modifying, or discarding rules as required. Reconciling data across multiple sources. DataTrust solutions offer a full suite of applications for analyzing source and target datasets.

Talend Data Fabric's cloud services are able to efficiently solve all your integration and integrity problems -- on-premises or in cloud, from any source, at any endpoint. Trusted data delivered at the right time for every user. With an intuitive interface and minimal coding, you can easily and quickly integrate data, files, applications, events, and APIs from any source to any location. Integrate quality into data management to ensure compliance with all regulations. This is possible through a collaborative, pervasive, and cohesive approach towards data governance. High quality, reliable data is essential to make informed decisions. It must be derived from real-time and batch processing, and enhanced with market-leading data enrichment and cleaning tools. Make your data more valuable by making it accessible internally and externally. Building APIs is easy with the extensive self-service capabilities. This will improve customer engagement.

Geocoding and address quality software. Validate, deduplicate, and maintain accurate and deliverable address information. Data quality is the ability to verify the accuracy and completeness of certain data. Data quality and postal address verification involves integrating data into any address database to ensure it is reliable and serves its intended purpose. There are many sectors and operations that rely on postal addresses, such as shipping and data entry, such as geomarketing and statistics, such as transportation. Operations tuning is key to ensuring significant logistics and economic savings for enterprises. This add-value is important to make work easier, more efficient. Egon is an online data quality system that can be accessed via the internet.

Only Data Observability platform that allows complete control over enterprise data systems. Comprehensive, cross-sectional visibility of complex, interconnected data systems. Synthesizes signals across workloads and data quality, security, infrastructure, and security. Data processing and operational efficiency are improved. Automates data quality monitoring from start to finish for rapidly changing and mutable datasets. Acceldata offers a single window to identify, predict, and fix data problems. Complete data issues can be fixed in real-time. You can observe the flow of business data from one pane of glass. Find anomalies in interconnected data pipelines.

This machine learning monitoring tool allows you to easily monitor and troubleshoot large model volumes. Data scientists can avoid false alarms and dead ends by using an unrivaled explainability accuracy and unique analyses that aren't available anywhere else. This allows them to quickly and effectively address critical problems. So that your business runs at its best, machine learning models are optimized. TruEra's explainability engine is the result of years of dedicated research and development. It is significantly more accurate that current tools. TruEra's enterprise-class AI explainability tech is unrivalled. The core diagnostic engine is built on six years of research by Carnegie Mellon University. It outperforms all competitors. The platform performs sophisticated sensitivity analyses quickly, allowing data scientists, business users, risk and compliance teams to understand how and why a model makes predictions.

Snowflake®, a cloud data platform and enterprise-grade architecture, allows you to securely store and use your data in the cloud. Drag-and-drop visual workflow design and built-in ETL to connect, clean and transform data from 250+ sources. You can generate actionable insights and insights from your data using the latest Search and AI-driven technology. Our AI/ML environments can be used to build, tune, and deploy models for predictive analytics or forecasting. Our AI/ML environments are available to help you take your data to new heights. The PurpleCube Data Science module allows you to create, train, tune, and deploy AI models for forecasting and predictive analysis. PurpleCube Analytics allows you to create BI visualizations, search your data with natural language and use AI-driven insights and smart recommendations to provide answers to questions that you didn't know to ask.

Crux is used by the most powerful people to increase external data integration, transformation and observability, without increasing their headcount. Our cloud-native data technology accelerates the preparation, observation, and delivery of any external dataset. We can guarantee you receive high-quality data at the right time, in the right format, and in the right location. Automated schema detection, delivery schedule inference and lifecycle management are all tools that can be used to quickly build pipelines from any external source of data. A private catalog of linked and matched data products will increase your organization's discoverability. To quickly combine data from multiple sources and accelerate analytics, enrich, validate, and transform any data set, you can enrich, validate, or transform it.

Automate the automatic coverage of thousands of tables using ML-based anomaly detection. 50+ custom metrics are also available. Monitoring of metadata and data. Comprehensive mapping of all dependencies between assets from ingestion to reporting. Collaboration between data consumers and data engineers is enhanced and productivity is increased. Sifflet integrates seamlessly with your data sources and preferred tools. It can run on AWS and Google Cloud Platform as well as Microsoft Azure. Keep an eye on your data's health and notify the team if quality criteria are not being met. In a matter of seconds, you can set up the basic coverage of all your tables. You can set the frequency, criticality, and even custom notifications. Use ML-based rules for any anomaly in your data. There is no need to create a new configuration. Each rule is unique because it learns from historical data as well as user feedback. A library of 50+ templates can be used to complement the automated rules.

We are a leader for Data Quality & ML Models for enterprises adopting a modern data management ecosystem, multi-cloud and ML. Algorithms for tracking Data Anomalies in Azure GCP and AWS databases. Measure and monitor data issues across all cloud database management tools, data silos and data silos using a single centralized tool. Quality is in the eyes of the beholder. Data issues can have different implications depending where you are in the enterprise. Qualdo was the first to organize all data quality issues from the perspective of multiple enterprise stakeholders and present a unified view. Use powerful auto-resolution algorithms for tracking and isolating critical data issues. Use robust reports and alerts for managing your enterprise regulatory compliance.