Best Data Quality Software for Apache Kafka

OpenDQ is a zero-cost enterprise data quality, master and governance solution. OpenDQ is modularly built and can scale to meet your enterprise data management requirements. OpenDQ provides trusted data using a machine learning- and artificial intelligence-based framework. Comprehensive Data Quality Matching Profiling Data/Address Standardization Master Data Management 360 View of Customer Data Governance Business Glossary Meta Data Management

A platform for data management powered by AI that allows data democratization. Insights drives innovation by integrating and centralizing all data sources, facilitating collaboration, and empowering organizations for innovation. SCIKIQ, a holistic business platform, simplifies the data complexities of business users through a drag-and-drop user interface. This allows businesses to concentrate on driving value out of data, allowing them to grow and make better decisions. You can connect any data source and use box integration to ingest both structured and unstructured data. Built for business users, easy to use, no-code platform, drag and drop data management. Self-learning platform. Cloud agnostic, environment agnostic. You can build on top of any data environment. The SCIKIQ architecture was specifically designed to address the complex hybrid data landscape.

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.

Mozart Data is the all-in-one modern data platform for consolidating, organizing, and analyzing your data. Set up a modern data stack in an hour, without any engineering. Start getting more out of your data and making data-driven decisions today.

Secuvy, a next-generation cloud platform, automates data security, privacy compliance, and governance via AI-driven workflows. Unstructured data is treated with the best data intelligence. Secuvy, a next-generation cloud platform that automates data security, privacy compliance, and governance via AI-driven workflows is called Secuvy. Unstructured data is treated with the best data intelligence. Automated data discovery, customizable subjects access requests, user validations and data maps & workflows to comply with privacy regulations such as the ccpa or gdpr. Data intelligence is used to locate sensitive and private information in multiple data stores, both in motion and at rest. Our mission is to assist organizations in protecting their brand, automating processes, and improving customer trust in a world that is rapidly changing. We want to reduce human effort, costs and errors in handling sensitive data.

You can unify your data catalog, reduce communication overhead, and enable quality control for any employee of your company without having to deploy or install anything. Rudol is a data platform that helps companies understand all data sources, regardless of where they are from. It reduces communication in reporting processes and urgencies and allows data quality diagnosis and issue prevention for all company members. Each organization can add data sources from rudol's growing list of providers and BI tools that have a standardized structure. This includes MySQL, PostgreSQL. Redshift. Snowflake. Kafka. S3*. BigQuery*. MongoDB*. Tableau*. PowerBI*. Looker* (*in development). No matter where the data comes from, anyone can easily understand where it is stored, read its documentation, and contact data owners via our integrations.

A low-code no-code approach to data quality. SaaS offers flexibility, affordability, ease-of-integration, and efficient support. High standards for encryption, identity management and role-based access control. Data governance and compliance standards. Advanced ML models for detecting row-value data anomalies. The models will adapt to the business and data requirements of users. You can add any number of data sources, records, or attributes. For unpredictable volume spikes, well-equipped. Support streaming and batch processing. Data is continuously monitored to provide real-time notification, with no impact on pipeline performance. Easy boarding, integration, investigation. Telmai is a platform that allows Data Teams to detect and investigate anomalies in real-time. No-code on-boarding. Connect to your data source, and select alerting channels. Telmai will automatically learn data and alert you if there are unexpected drifts.

Identify code issues and optimize code in real-time. Prevent data incidents before deployment. Manage code changes that impact data from the operational database all the way to the dashboard. Data lineage is automated, allowing for analysis of every dependency, from the operational database to the reporting layer. Foundational automates the enforcement of data contracts by analyzing each repository, from upstream to downstream, directly from the source code. Use Foundational to identify and prevent code and data issues. Create controls and guardrails. Foundational can be configured in minutes without requiring any code changes.

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.

Data is used to inform every aspect of a plant or facility. It is the basis for most operational processes, business decisions, and environmental events. This data is often blamed for failures, whether it's operator error, bad sensor, safety or environmental events or poor analytics. APERIO can help solve these problems. Data integrity is a critical element of Industry 4.0. It is the foundation on which more advanced applications such as predictive models and process optimization are built. APERIO DataWise provides reliable, trusted data. Automate the quality of PI data and digital twins at scale. Validated data is required across the enterprise in order to improve asset reliability. Empowering the operator to take better decisions. Detect threats to operational data in order to ensure operational resilience. Monitor & report sustainability metrics accurately.

Get a clear view of your data assets: popularity, usage, and schema coverage. Get important insights into your data assets, such as popularity and utilization. Find and filter data based on tags and descriptions in metadata. Get valuable insights about your data assets, such as popularity, usage, quality, and schema cover. Drive data governance and ownership throughout your organization. Stream-lake-warehouse lineage to facilitate data ownership and collaboration. Lineage maps are automatically generated at the field level to help understand the entire data ecosystem. Anomaly detection is based on your data and seasonality patterns. It uses automatic backfilling from historical data. Machine learning thresholds are trained for each data segment and not just metadata.