Alternatives to Wiiisdom Ops

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.

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.

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.

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.

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.

Every day, enterprises around the globe use the Nintex Platform to manage, automate, and optimize their business processes. Nintex Platform features include process mapping, workflow automation and document generation. It also supports mobile apps, process intelligence, and forms. All of this is possible with an easy-to-use drag and drop designer. With the next generation Nintex Workflow Cloud, you can accelerate your organization's digital transformation journey. Your IT, business analysts, ops, and IT process professionals can now harness the power of process™. Start digitizing forms, workflows, etc. today. The Nintex Process Platform provides the most comprehensive platform for process management. Nintex makes it easy to automate, optimize, and manage your business processes.

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

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.

The Convertr platform gives marketers visibility and control over data processes and lead quality to create higher performing demand programs. When you take control of your lead processes in the beginning, you build more scalable operations and strategic teams that can stay focused on revenue driving activities. Improve Productivity: Weeks to months of manual lead data processing can be reallocated to revenue driving activities Focus on Performance: Teams work off trusted data to make better decisions and optimize programs Drive Data Alignment: Data moves between teams and platforms in usable, analyzable formats

Automates data cleansing & consolidates data into a clean, trustworthy data layer to feed downstream reporting. Manages suppliers' data requests. Monitors data workflow to identify bottlenecks and resolve problems. To prove quality assurance for each data row, creates an audit trail. You can customize validation and governance to fit your organization. Data analysts can focus on their insights by reducing data prep time by 60% The central KPI Dashboard provides key metrics about your data pipeline. This allows you to identify bottlenecks and resolve issues, as well as improve performance. Flexible rules engine allow users to create validation and testing that are tailored to their needs. It's easy to integrate gather360 into your existing tools or use it for setting up your cloud infrastructure.

Synthesized can help you unlock the full potential of your data projects and AI. We automate all stages of data preparation and provisioning with cutting-edge AI. The platform synthesizes data without exposing personal information or causing compliance issues. Software to prepare and provide accurate synthetic data for building better models at scale. Synthesized solves the problem of data sharing for businesses. 40% of companies that invest in AI can't report business benefits. Our easy-to-use platform allows data scientists, product, and marketing teams to focus on revealing critical insight. Without representative data, testing data-driven applications can be difficult. This can lead to problems when services go live.

Deliver tangible strategic value, quickly. With AI-driven automation, you can ensure end-to-end support of data quality requirements across users and data types. No matter what type of initiative your organization is working on--from data migration to next-gen analytics--Informatica Data Quality has the flexibility you need to easily deploy data quality for all use cases. Facilitate collaboration between IT and business stakeholders and empower business users. All use cases and all workloads require management of the quality of multicloud and on-premises data. Integrates human tasks into the workflow. Business users can review, correct, or approve exceptions during the automated process. To uncover relationships and detect problems, profile data is used to perform iterative analysis of data. AI-driven insights can automate the most important tasks and simplify data discovery to increase productivity.

The BiG EVAL platform provides powerful software tools to ensure and improve data quality throughout the entire lifecycle of information. BiG EVAL's data quality and testing software tools are built on the BiG EVAL platform, a comprehensive code base that aims to provide high performance and high flexibility data validation. All features were developed through practical experience gained from working with customers. It is crucial to ensure high data quality throughout the data lifecycle. This is essential for data governance. BiG EVAL DQM, an automation solution, supports you in all aspects of data quality management. Continuous quality checks validate enterprise data, provide a quality indicator, and support you in solving quality problems. BiG EVAL DTA allows you to automate testing tasks within your data-oriented project.

Waaila, a comprehensive application that monitors data quality, is supported by hundreds of analysts around the world. It helps to avoid disasters caused by poor data quality. Validate your data to take control of your analytics. They must be precise to maximize their potential, therefore validation and monitoring are essential. It is crucial that the data be accurate and reliable in order to serve its true purpose and allow for business growth. Marketing strategies that are more effective will be more efficient if they are of higher quality. You can rely on the accuracy and quality of your data to make data-driven decisions that will lead to the best results. Automated validation saves time and energy and delivers better results. Rapid attack discovery helps to prevent huge impacts and opens up new opportunities. The application management and navigation are simple, which allows for quick data validation and efficient processes that enable the issue to be quickly identified and solved.

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.

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.

Cleanlab Studio is a single framework that handles all analytics and machine-learning tasks. It includes the entire data quality pipeline and data-centric AI. The automated pipeline takes care of all your ML tasks: data preprocessing and foundation model tuning, hyperparameters tuning, model selection. ML models can be used to diagnose data problems, and then re-trained using your corrected dataset. Explore the heatmap of all suggested corrections in your dataset. Cleanlab Studio offers all of this and more free of charge as soon as your dataset is uploaded. Cleanlab Studio is pre-loaded with a number of demo datasets and project examples. You can view them in your account once you sign in.

Enhance monitoring, visualization, remediation, reconciliation, and reconciliation to improve the quality of your data at-rest and in-motion. Data quality should be part of your company's DNA. To get a complete view of your data's journey throughout your organization, no matter where it is located, go beyond the basic quality checks. Continuous quality monitoring and point to-point reconciliation are essential for building trust in data and providing consistent insights. Data360 DQ+ automates data quality monitoring across the entire data supply chain, starting at the point information enters your organization. This allows you to monitor data in motion. Operational data quality includes validating counts and amounts across multiple sources and tracking timeliness to meet external or internal SLAs. Also, checks to ensure totals remain within set limits are examples.

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.

According to industry experts, up to 20% of a company's contact list contains bad data. This can lead to bounced emails, returned mail, address correction fees and wasted sales and marketing efforts. The Data Quality Suite can be used to standardize, verify, and correct all contact data. This includes postal address, email address and phone number. It is essential for efficient communications and business operations. Verify, standardize and transliterate addresses from more than 240 countries. Intelligent recognition can identify 650,000+ ethnically diverse first and last names. Authenticate phone numbers and geo-data to ensure that mobile numbers are available and callable. Validate domain, syntax, spelling, & even test SMTP for global email verification. The Data Quality Suite allows organizations of all sizes to verify and maintain data in order to communicate effectively with customers via email, postal mail, or phone.

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.

Accurity, the all in one data intelligence platform, gives you a complete understanding of your company and complete trust with your data. This will allow you to make faster business-critical decisions, increase revenue, lower costs, and ensure data compliance. You can engage and satisfy your customers with accurate, timely and relevant data. This will help you to increase brand awareness and drive sales conversions. You can access everything from one interface, automate quality checks and data quality issues workflows. This allows you to lower infrastructure and personnel costs and allow you to spend more time using your data than managing it. You can unlock the true value of your data by improving your decision-making process, identifying inefficiencies and finding valuable customer and product information to increase your company's innovation.

Qbot is an automated test engine that's designed to improve data quality. It can be used to enable large, complex data platforms. However, it is not dependent on the environment or ETL or Database technology. It can be used to test ETL platforms, upgrade databases, cloud migration, and deliver trusted data at a speed never before seen. It is the most complete Data quality automation engine available. It features data security, speed, scaleability, and the largest test library. This allows the user to directly pass the SQL query while configuring the test groups. The following database servers are currently supported for source and destination database tables.

Datuum is an AI-powered data integration tool that offers a unique solution for organizations looking to streamline their data integration process. With our pre-trained AI engine, Datuum simplifies customer data onboarding by allowing for automated integration from various sources without coding. This reduces data preparation time and helps establish resilient connectors, ultimately freeing up time for organizations to focus on generating insights and improving the customer experience. At Datuum, we have over 40 years of experience in data management and operations, and we've incorporated our expertise into the core of our product. Our platform is designed to address the critical challenges faced by data engineers and managers while being accessible and user-friendly for non-technical specialists. By reducing up to 80% of the time typically spent on data-related tasks, Datuum can help organizations optimize their data management processes and achieve more efficient outcomes.

Create a scalable architecture that treats data products as first-class citizens. Automate and repurpose data products. Enable compliance and robust data governance. Control the costs of your data products and pipelines for Snowflake. This global pharmaceutical giant's data product teams can benefit from next-generation analytics using self-service data and analytics infrastructure that includes Snowflake and other tools that use a data mesh approach. The DataOps.live platform allows them to organize and benefit from next generation analytics. DataOps is a unique way for development teams to work together around data in order to achieve rapid results and improve customer service. Data warehousing has never been paired with agility. DataOps is able to change all of this. Governance of data assets is crucial, but it can be a barrier to agility. Dataops enables agility and increases governance. DataOps does not refer to technology; it is a way of thinking.

The open-source ML observability Platform. From validation to production, evaluate, test, and track ML models. From tabular data up to NLP and LLM. Built for data scientists and ML Engineers. All you need to run ML systems reliably in production. Start with simple ad-hoc checks. Scale up to the full monitoring platform. All in one tool with consistent APIs and metrics. Useful, beautiful and shareable. Explore and debug a comprehensive view on data and ML models. Start in a matter of seconds. Test before shipping, validate in production, and run checks with every model update. By generating test conditions based on a reference dataset, you can skip the manual setup. Monitor all aspects of your data, models and test results. Proactively identify and resolve production model problems, ensure optimal performance and continually improve it.

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.

With automated data quality profiling, and synthetic data generation, adopting data-centric AI is easier than ever. We help data scientists unlock the full potential of data. YData Fabric enables users to easily manage and understand data assets, synthetic data, for fast data access and pipelines, for iterative, scalable and iterative flows. Better data and more reliable models delivered on a large scale. Automated data profiling to simplify and speed up exploratory data analysis. Upload and connect your datasets using an easy-to-configure interface. Synthetic data can be generated that mimics real data's statistical properties and behavior. By replacing real data with synthetic data, you can enhance your datasets and improve your models' efficiency. Pipelines can be used to refine and improve processes, consume data, clean it up, transform your data and improve its quality.

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.

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.

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.

Customer data is everywhere: on our phones, in social media, in IoT, in CRM, ERP and marketing. The amount of data that companies collect is staggering... The data companies capture is overwhelming... Data that is uncontrolled and of low quality can cause a company to become disorganized, putting its growth at risk. All processes in a business should be synchronized around customer data. It is vital to ensure that the data is always reliable and accessible. DQE One is a solution for all departments that leverage customer data. Data of high quality ensures that every decision is made with confidence. Contact information from different sources is piled up in the company's database. The customer database needs to be maintained and qualified for use as a reliable repository, even if it contains data entry errors, incorrect information or gaps.

Over 40 years, we have built our analytics machine and worked with countless clients. We are constantly improving our approach to the modern enterprise. This means that you can see the future and have access to all data. The goal is simple. To enable informed decision-making and help you drive business results. Accessible data is the key to a sophisticated data strategy. How you see your data, its trends and patterns, will determine how useful it is. You can empower your organization to make strategic decisions by using real-time, personalized, and self-service dashboards that bring this data to life. You don't have to rely on gut feelings, or worse, bury yourself in ambiguity. Your entire company can organize around the same information, and grow with exceptional visualization and reporting.

Data plays an important role in many areas of a company, including sales, marketing, and finance. Data must be managed throughout its life cycle to get the most out of it. Uniserv's core philosophy and the products it offers is data quality. Our customized solutions make your customer master information the success factor for your company. The Data Quality Service Hub ensures that customer data quality is high at all levels of your company, including international. We can correct your address information according international standards and based upon first-class reference data. We can also check bank data, telephone numbers, and email addresses at different levels. We can search your data for redundant items based on your business rules. These items can be consolidated automatically using prescribed rules or manually sorted for reprocessing.

Our solutions monitor data 24 hours a day to detect any potential problems in the quality of data and its integration into other internal systems. This ensures that your bottom line will not be affected in any way. Verify that your data is accurate before it is transferred or shared among your systems. You'll be notified if something is not right and the data pipeline will be halted until it's resolved. Our data solutions are tailored to your industry and region to ensure regulatory compliance. Our customers are empowered to gain greater control of their data sets when we show them how easy it is to measure and meet data goals and requirements by leveraging our user interface.

DQLabs has over a decade of data-related experience for Fortune 100 clients. This includes data integration, data governance and data visualization. The platform comes with all the features necessary to enable autonomous execution without any configuration or manual intervention. This AI-powered tool can automate every aspect of scaling, governance, and automation. It can also be integrated and compatible with other tools within the data ecosystem. Machine Learning and AI make it possible to make decisions in all aspects data management. No more ETL, workflows and rules. The platform automatically learns and reconfigures rules as the business strategy changes and requires new data patterns and trends.

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.

iCEDQ, a DataOps platform that allows monitoring and testing, is a DataOps platform. iCEDQ is an agile rules engine that automates ETL Testing, Data Migration Testing and Big Data Testing. It increases productivity and reduces project timelines for testing data warehouses and ETL projects. Identify data problems in your Data Warehouse, Big Data, and Data Migration Projects. The iCEDQ platform can transform your ETL or Data Warehouse Testing landscape. It automates it from end to end, allowing the user to focus on analyzing the issues and fixing them. The first edition of iCEDQ was designed to validate and test any volume of data with our in-memory engine. It can perform complex validation using SQL and Groovy. It is optimized for Data Warehouse Testing. It scales based upon the number of cores on a server and is 5X faster that the standard edition.

Your one-touch copilot to data quality, spend, performance and usage. Your data team will not be the last one to learn about bottlenecks or broken analytics. We detect anomalies and alert users immediately. Eliminate downtimes and improve data quality. You'll be the one to know when performance starts to trend in the wrong direction. We help you make the connection between data usage, resource allocation and cost. Reduce costs and allocate resources efficiently. We can slice and dice spending by warehouse, user and query. You will be notified when spending starts to trend in the wrong direction. Get insights into the impact of underutilized data on your business. Revefi is constantly on the lookout for waste and will alert you to opportunities that can be better rationalized. With automated data monitoring built into your data warehouse, you can say goodbye to manual data checking. You can solve problems and find the root causes within minutes, before they impact your downstream users.

End-to-end DataOps built upon an agile platform that protects and improves your data assets. Arena is the leading augmented data management platform. Our active data catalog allows for self-service data enrichment to control complex data environments. You can create custom workflows to increase the reliability and accuracy of each data set. Machine-learning can be used to identify and align master assets for better data decisions. Superior security is assured with complete lineage, including detailed visualizations and masking. Data management is easy with Arena. Arena can catalog your data from any location. Our extensible connections allow for analytics across all your preferred tools. Overcome data sprawl challenges with our software. Our software is designed to drive business and analytics success, while also providing the controls and extensibility required in today's multicloud data complexity.

Pandera is a flexible, simple and extensible framework for data testing that allows you to validate not only the data, but also the functions which produce it. You can overcome the initial challenge of defining a data schema by inferring it from clean data and then fine-tuning it over time. Identify critical points in your pipeline and validate the data that enters and leaves them. Validate functions that generate your data by automatically creating test cases. You can choose from a wide range of pre-built tests or create your own rules to validate your data.

In a developer-friendly visual editor, you can design, debug, run, and troubleshoot data jobflows and data transformations. You can orchestrate data tasks that require a specific sequence and organize multiple systems using the transparency of visual workflows. Easy deployment of data workloads into an enterprise runtime environment. Cloud or on-premise. Data can be made available to applications, people, and storage through a single platform. You can manage all your data workloads and related processes from one platform. No task is too difficult. CloverDX was built on years of experience in large enterprise projects. Open architecture that is user-friendly and flexible allows you to package and hide complexity for developers. You can manage the entire lifecycle for a data pipeline, from design, deployment, evolution, and testing. Our in-house customer success teams will help you get things done quickly.

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.

Drag-and-drop rules studio allows you to profile, clean, match, and deduplicate data. Lo-code UI is a user interface that requires no programming skills. This puts power in the hands subject matter experts. You can add AI and machine learning to your existing data management process to reduce manual effort, increase accuracy, and provide full transparency on machine-led decisions. Our self-service solutions offer award-winning data quality, matching capabilities across multiple industries and are quickly configured with specialist assistance from Datactics data engineers. Datactics makes it easy to measure data against industry and regulatory standards, fix breaches in bulk, and push into reporting tools. Chief Risk Officers have full visibility and audit trails. Datactics can be used to augment data matching with Legal Entity Masters to manage client lifecycle management.

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.

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.

Snowplow is a data collection platform that is best in class for Data Teams. Snowplow allows you to collect rich, high-quality data from all your products and platforms. Your data is instantly available and delivered to your chosen data warehouse. This allows you to easily join other data sets to power BI tools, custom reporting, or machine learning models. The Snowplow pipeline runs in your cloud (AWS or GCP), giving your complete control over your data. Snowplow allows you to ask and answer any questions related to your business or use case using your preferred tools.

Visokio creates Omniscope Evo, a complete and extensible BI tool for data processing, analysis, and reporting. Smart experience on any device. You can start with any data, any format, load, edit, combine, transform it while visually exploring it. You can extract insights through ML algorithms and automate your data workflows. Omniscope is a powerful BI tool that can be used on any device. It also has a responsive UX and is mobile-friendly. You can also augment data workflows using Python / R scripts or enhance reports with any JS visualisation. Omniscope is the complete solution for data managers, scientists, analysts, and data managers. It can be used to visualize data, analyze data, and visualise it.

Data can disrupt markets and open new frontiers, but it only works when it is trusted and understood. Stakeholders in your organization can collaborate to achieve data excellence by leveraging our AI/ML-enhanced cloud-based solution, which is built with 25 years worth of best practices. With hundreds of pre-built reports and embedded best practices, you can quickly identify and remediate data quality issues. Cleanse data before or during data migration. Track data quality in real time with customizable data intelligence dashboards. Monitor data objects continuously and automatically initiate remediation workflows to direct them to the correct data owners. To accelerate future data initiatives, consolidate data on a single cloud-based platform. All data stakeholders working together in one system will reduce effort and improve results.