Best Data Quality Software for SQL Server

Looking for fast, easy solutions to protect your entire data lifecycle? Look no further. Melissa’s Web APIs offer a range of capabilities to keep your customer data clean, verified, and enriched. Our solutions work throughout the entire data lifecycle – whether in real time, at point of entry or for a batch cleanup. • Global Address: Verify & standardize addresses in 240+ countries & territories with postal authority certified coding & premise-level geocoding • Global Email: Verify email mailboxes, syntax, spelling & domains in real time to ensure they are deliverable • Global Name: Verify, standardize & parse person & business names with intelligent recognition of millions of first & last names • Global Phone: Verify phone as active, identify line type, & return geographic details, dominant language & carrier for 200+ countries • Global IP Locator: Gain a geolocation of an input IP address with lat & long, proxy info, city, region & country • Property (U.S. & Canada): Return comprehensive property & mortgage info for 140+ million U.S. properties • Personator (U.S. & Canada): USPS® CASS/DPV certified address checking, name parsing & genderizing, phone & email verification are all easily performed with this API

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.

QuerySurge is the smart Data Testing solution that automates the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Applications with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Big Data (Hadoop & NoSQL) Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise Application/ERP Testing Features Supported Technologies - 200+ data stores are supported QuerySurge Projects - multi-project support Data Analytics Dashboard - provides insight into your data Query Wizard - no programming required Design Library - take total control of your custom test desig BI Tester - automated business report testing Scheduling - run now, periodically or at a set time Run Dashboard - analyze test runs in real-time Reports - 100s of reports API - full RESTful API DevOps for Data - integrates into your CI/CD pipeline Test Management Integration QuerySurge will help you: - Continuously detect data issues in the delivery pipeline - Dramatically increase data validation coverage - Leverage analytics to optimize your critical data - Improve your data quality at speed

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.

For more four decades, IRI CoSort has defined the state-of-the-art in big data sorting and transformation technology. From advanced algorithms to automatic memory management, and from multi-core exploitation to I/O optimization, there is no more proven performer for production data processing than CoSort. CoSort was the first commercial sort package developed for open systems: CP/M in 1980, MS-DOS in 1982, Unix in 1985, and Windows in 1995. Repeatedly reported to be the fastest commercial-grade sort product for Unix. CoSort was also judged by PC Week to be the "top performing" sort on Windows. CoSort was released for CP/M in 1978, DOS in 1980, Unix in the mid-eighties, and Windows in the early nineties, and received a readership award from DM Review magazine in 2000. CoSort was first designed as a file sorting utility, and added interfaces to replace or convert sort program parameters used in IBM DataStage, Informatica, MF COBOL, JCL, NATURAL, SAS, and SyncSort. In 1992, CoSort added related manipulation functions through a control language interface based on VMS sort utility syntax, which evolved through the years to handle structured data integration and staging for flat files and RDBs, and multiple spinoff products.

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.

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.

Adverity is the fully-integrated data platform for automating the connectivity, transformation, governance and utilization of data at scale. Adverity is the simplest way to get your data how you want it, where you want it, and when you need it. The platform enables businesses to blend disparate datasets such as sales, finance, marketing, and advertising, to create a single source of truth over business performance. Through automated connectivity to hundreds of data sources and destinations, unrivaled data transformation options, and powerful data governance features, Adverity is the easiest way to get your data how you want it, where you want it, and when you need it.

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.

The Data Comparison Tool. It is easy to compare data from files (Excel and csv) and queries (MS-SQL and Oracle, Amazon Redshift and MySQL, and many more ...) Don't waste your time reading endless tutorials, spreadsheets, and formulas that are useless for just one instance. Instead, let the clicks do all the work and compare A with B without having to learn how to code. If you find yourself in any of these situations, you should not waste your time on things that are more important to you.

Mask, generate, subset, virtualize, and automate your test data with the DATPROF Test Data Management Suite. Our solution helps managing Personally Identifiable Information and/or too large databases. Long waiting times for test data refreshes are a thing of the past.

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.

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.

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.

Digna is a solution powered by AI that addresses the challenges of data quality management in modern times. It is domain agnostic and can be used in a variety of sectors, including finance and healthcare. Digna prioritizes privacy and ensures compliance with stringent regulations. It's also built to scale and grow with your data infrastructure. Digna is flexible enough to be installed on-premises or in the cloud, and it aligns with your organization's needs and security policies. Digna is at the forefront of data quality solutions. Its user-friendly design, combined with powerful AI analytics, makes Digna an ideal solution for businesses looking to improve data quality. Digna's seamless integration, real time monitoring, and adaptability make it more than just a tool. It is a partner on your journey to impeccable data quality.

Great Expectations is a standard for data quality that is shared and openly accessible. It assists data teams in eliminating pipeline debt through data testing, documentation and profiling. We recommend that you deploy within a virtual environment. You may want to read the Supporting section if you are not familiar with pip and virtual environments, notebooks or git. Many companies have high expectations and are doing amazing things these days. Take a look at some case studies of companies we have worked with to see how they use great expectations in their data stack. Great expectations cloud is a fully managed SaaS service. We are looking for private alpha members to join our great expectations cloud, a fully managed SaaS service. Alpha members have first access to new features, and can contribute to the roadmap.

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.

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.