Best Data Quality Software for Google Cloud BigQuery

Twilio Segment’s Customer Data Platform (CDP) provides companies with the data foundation that they need to put their customers at the heart of every decision. Using Twilio Segment, companies can collect, unify and route their customer data into any system. Over 25,000 companies use Twilio Segment to make real-time decisions, accelerate growth and deliver world-class customer experiences.

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.

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

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.

It shouldn't take long to analyze data from your business solutions. Zuar Runner allows you to automate your ELT/ETL processes, and have data flow from hundreds of sources into one destination. Zuar Runner can manage everything: transport, warehouse, transformation, model, reporting, and monitoring. Our experts will make sure your deployment goes smoothly and quickly.

Key Features of Syncari ADM: Continuous Unification & Data Quality Programmable MDM with Extensibility Patented Multi-directional Sync Integrated Data Fabric Architecture Dynamic Data Model & 360° Dataset Readiness Enhanced Automation with AI/ML Datasets, Metadata as Data, Virtual Entities Syncari’s cohesive platform syncs, unifies, governs, enhances, and provides access to data across your enterprise, delivering continuous unification, data quality, and distribution—all within a scalable, robust architecture.

You can monitor all tags in real-time, on all devices and browsers. The DataLayer Guard monitors dataLayer in real-time and catches issues before they impact your business. Real-time alerts notify you of any errors in data collection, ensuring that you do not miss any valuable data from your marketing or analytics tools.

Immuta's Data Access Platform is built to give data teams secure yet streamlined access to data. Every organization is grappling with complex data policies as rules and regulations around that data are ever-changing and increasing in number. Immuta empowers data teams by automating the discovery and classification of new and existing data to speed time to value; orchestrating the enforcement of data policies through Policy-as-code (PaC), data masking, and Privacy Enhancing Technologies (PETs) so that any technical or business owner can manage and keep it secure; and monitoring/auditing user and policy activity/history and how data is accessed through automation to ensure provable compliance. Immuta integrates with all of the leading cloud data platforms, including Snowflake, Databricks, Starburst, Trino, Amazon Redshift, Google BigQuery, and Azure Synapse. Our platform is able to transparently secure data access without impacting performance. With Immuta, data teams are able to speed up data access by 100x, decrease the number of policies required by 75x, and achieve provable compliance goals.

Coginiti is the AI-enabled enterprise Data Workspace that empowers everyone to get fast, consistent answers to any business questions. Coginiti helps you find and search for metrics that are approved for your use case, accelerating the lifecycle of analytic development from development to certification. Coginiti integrates the functionality needed to build, approve and curate analytics for reuse across all business domains, while adhering your data governance policies and standards. Coginiti’s collaborative data workspace is trusted by teams in the insurance, healthcare, financial services and retail/consumer packaged goods industries to deliver value to customers.

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.

Decube is a comprehensive data management platform designed to help organizations manage their data observability, data catalog, and data governance needs. Our platform is designed to provide accurate, reliable, and timely data, enabling organizations to make better-informed decisions. Our data observability tools provide end-to-end visibility into data, making it easier for organizations to track data origin and flow across different systems and departments. With our real-time monitoring capabilities, organizations can detect data incidents quickly and reduce their impact on business operations. The data catalog component of our platform provides a centralized repository for all data assets, making it easier for organizations to manage and govern data usage and access. With our data classification tools, organizations can identify and manage sensitive data more effectively, ensuring compliance with data privacy regulations and policies. The data governance component of our platform provides robust access controls, enabling organizations to manage data access and usage effectively. Our tools also allow organizations to generate audit reports, track user activity, and demonstrate compliance with regulatory requirements.

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.

Anomalo helps you get ahead of data issues by automatically detecting them as soon as they appear and before anyone else is impacted. -Depth of Checks: Provides both foundational observability (automated checks for data freshness, volume, schema changes) and deep data quality monitoring (automated checks for data consistency and correctness). -Automation: Use unsupervised machine learning to automatically identify missing and anomalous data. -Easy for everyone, no-code UI: A user can generate a no-code check that calculates a metric, plots it over time, generates a time series model, sends intuitive alerts to tools like Slack, and returns a root cause analysis. -Intelligent Alerting: Incredibly powerful unsupervised machine learning intelligently readjusts time series models and uses automatic secondary checks to weed out false positives. -Time to Resolution: Automatically generates a root cause analysis that saves users time determining why an anomaly is occurring. Our triage feature orchestrates a resolution workflow and can integrate with many remediation steps, like ticketing systems. -In-VPC Development: Data never leaves the customer’s environment. Anomalo can be run entirely in-VPC for the utmost in privacy & security

In 30 minutes, you can monitor your entire warehouse. Automated warehouse-to-BI lineage can identify downstream impacts. Trust can be lost in seconds and regained in months. With modern data-era observability, you can have peace of mind. It can be difficult to get the coverage you need with code-based tests. They take hours to create and maintain. Metaplane allows you to add hundreds of tests in minutes. Foundational tests (e.g. We support foundational tests (e.g. row counts, freshness and schema drift), more complicated tests (distribution shifts, nullness shiftings, enum modifications), custom SQL, as well as everything in between. Manual thresholds can take a while to set and quickly become outdated as your data changes. Our anomaly detection algorithms use historical metadata to detect outliers. To minimize alert fatigue, monitor what is important, while also taking into account seasonality, trends and feedback from your team. You can also override manual thresholds.

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.

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.

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.

Monitor your data health, and monitor your pipeline performance. Get unified visibility for all pipelines that use cloud-native tools such as Apache Spark, Snowflake and BigQuery. A platform for Data Engineers that provides observability. Data engineering is becoming more complex as business stakeholders demand it. Databand can help you catch-up. More pipelines, more complexity. Data engineers are working with more complex infrastructure and pushing for faster release speeds. It is more difficult to understand why a process failed, why it is running late, and how changes impact the quality of data outputs. Data consumers are frustrated by inconsistent results, model performance, delays in data delivery, and other issues. A lack of transparency and trust in data delivery can lead to confusion about the exact source of the data. Pipeline logs, data quality metrics, and errors are all captured and stored in separate, isolated systems.

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.

You can prevent data outages by identifying data quality issues and fixing them before they reach production. In less than a day, you can increase your test coverage for data pipelines from 0 to 100%. Automatic regression testing across billions upon billions of rows allows you to determine the impact of every code change. Automate change management, improve data literacy and compliance, and reduce incident response times. Don't be taken by surprise by data incidents. Automated anomaly detection allows you to be the first to know about them. Datafold's ML model, which can be easily adjusted by Datafold, adapts to seasonality or trend patterns in your data to create dynamic thresholds. You can save hours trying to understand data. The Data Catalog makes it easy to search for relevant data, fields, or explore distributions with an intuitive UI. Interactive full-text search, data profiling and consolidation of metadata all in one place.

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.

Enterprises can manage their data quality lifecycle proactively through contextual data checks, anomaly detection, and remediation. Expose anomalies, metadata and help teams take corrective action. Automate remediation workflows for quick and efficient error resolution. Maintain high data-quality and prevent errors from impacting business decisions. The SLA chart gives an overview of SLA. It includes the total number SLA monitoring performed and any violations. This chart will help you identify data areas that require further investigation or improvements.

Aggua is an AI platform with augmented data fabric that gives data and business teams access to their data. It creates Trust and provides practical Data Insights for a more holistic and data-centric decision making. With just a few clicks, you can find out what's happening under the hood of your data stack. You can access data lineage, cost insights and documentation without interrupting your data engineer's day. With automated lineage, data engineers and architects can spend less time manually tracing what data type changes will break in their data pipelines, tables, and infrastructure.