Best AWS Marketplace Software - Page 68

Datagaps DataOps Suite

Founded in 2010, Datagaps is a US-based enterprise software company headquartered in Herndon,VA. Datagaps is the only vendor recognized by Gartner in BOTH the DataOps Tools market guide AND the Data Observability market guide — a distinction no other data testing platform holds. WHAT WE DO Datagaps makes data trustworthy across the entire pipeline — from data ingestion and ETL/ELT transformations, through data quality monitoring, to BI dashboard validation and AI model input testing — on a single, unified DataOps Suite platform. THE PROBLEM WE SOLVE Enterprise data teams lose an average of $15M annually to poor data quality (Source: Gartner). 70% of data migration failures surface post go-live. 59% of organizations cannot quantify their own BI consistency gap. Datagaps eliminates these risks with automated, end-to-end validation that detects issues before they reach production. HOW WE'RE DIFFERENT Unlike point tools that test one stage, Datagaps covers every layer — ETL pipelines, data quality and consistency, semantic validation, BI dashboards, and AI/ML inputs — from a single platform with shared rules, one audit trail, and one vendor. Agentic AI auto-generates tests, self-heals schema drift, summarizes report differences, and recommends smart quality rules. OUTCOMES DELIVERED 500B+ Records validated across ETL & cloud pipelines 10M+ Automated test cases run with zero manual scripting 80% Faster test cycles vs. manual testing approach 60% Reduction in data errors detected before production 70% Reduction in ETL validation spend 200+ Native data source connectors Trusted by 100+ enterprise customers across Financial Services, Healthcare, Life Sciences, FMCG, Retail, Higher Education PRODUCTS AND PLATFORM • DataOps Suite — Unified platform for end to end data validation and analytics testing powered by Agentic AI • ETL Validator — Automated ETL/ELT testing with Agentic AI; 200+ native connectors • BI Validator — Automated BI report validation for Power BI, Tableau, Oracle Analytics • Data Quality Monitor — Proactive data quality with Agentic AI: Predict, Prevent, Govern • Test Data Manager — Compliant and realistic test data generation with Agentic AI TECHNICAL CREDENTIALS SOC 2 Type II | US Patent (ELV architecture) | Informatica Certified | Embedded LLM (data stays in your environment) | Gartner DataOps Tools Guide | Gartner Data Observability Guide INTEGRATIONS 200+ Snowflake, Databricks, Azure Synapse, Amazon Redshift, Google BigQuery, Salesforce, Microsoft Power BI, Tableau, Oracle Analytics, Informatica, Talend, SSIS, dbt

Average Rating: 4.5/5.0

Total Reviews: 9

Data Labeling Services by CapeStart

Data Labeling Services is a data preparation and annotation service that helps organizations create structured datasets for artificial intelligence and machine learning workflows. CapeStart supports the collection, organization, annotation, classification, and labeling of data across text, images, audio, video, and other data formats. The service supports task-specific workflows such as image annotation, text classification, entity labeling, transcription, content categorization, video annotation, and audio labeling. Annotation processes can be configured according to project requirements, labeling guidelines, data schemas, and quality standards. Data labeling can support machine learning model development, computer vision, natural language processing, speech applications, generative AI, and other AI workflows that require structured and validated datasets. Quality-control processes can be incorporated to review labeled data and identify inconsistencies before datasets are used for downstream model development or evaluation. CapeStart can support both defined annotation workflows and larger data preparation programs, with processes structured around client requirements, data types, annotation specifications, and quality criteria.

Data Labeling Services by Objectways

Our core data labeling capabilities include: 3D Point Cloud: Object Detection, Object Tracking, Semantic Segmentation Video: Object Detection, Object Tracking, Clip Classification Images: Image Classification, Object Detection, Semantic Segmentation Text: Text Classification, Named Entity Recognition Audio: Transcription, Translation

Data Labeling Services by SmartOne

SmartOne.ai offers comprehensive data labeling and annotation services designed to enhance the accuracy and efficiency of AI and machine learning models across various industries. With a team of over 1,200 skilled annotators operating from secure facilities in Madagascar and Kenya, SmartOne.ai has processed over a billion data points since its inception in 2016. Their services encompass a wide range of data types, including images, video, text, and audio, ensuring tailored solutions for each client's unique requirements. Key Features and Functionality: - Multilingual Support: Expertise in multiple languages, including Italian, French, Mandarin, Korean, and Japanese, facilitating culturally nuanced data labeling and transcription services. - Diverse Data Annotation: Proficiency in annotating various data types such as images, videos, text, and audio, catering to industries like e-commerce, retail, autonomous vehicles, and geospatial intelligence. - Integration with AWS Technologies: Seamless integration with Amazon SageMaker Ground Truth, enabling efficient and scalable data labeling workflows. - Security and Compliance: Adherence to SOC2 Type 1 compliance and AWS-prescribed security controls, ensuring data confidentiality and integrity. - Scalability and Flexibility: Ability to scale operations to meet project demands without compromising quality, accommodating both small startups and large enterprises. Primary Value and Solutions Provided: SmartOne.ai addresses the critical need for high-quality, accurately labeled datasets essential for training effective AI and machine learning models. By offering precise and culturally sensitive annotations across various data types and languages, SmartOne.ai empowers organizations to develop AI solutions that resonate with diverse target audiences. Their commitment to security, scalability, and integration with advanced technologies like Amazon SageMaker ensures that clients receive reliable and efficient data labeling services, ultimately enhancing the performance and reliability of their AI applications.

Data Labeling Services by SmartOne, Inc. - French Language

SmartOne, Inc. offers specialized French language data labeling services, providing high-quality, culturally nuanced datasets tailored for AI and machine learning projects. With a team of native French speakers, SmartOne ensures linguistic accuracy and cultural sensitivity across various industries, including e-commerce, retail, and autonomous vehicles. Operating 24/7 from secure facilities, the company adheres to stringent security protocols, including SOC2 compliance, to maintain data confidentiality. SmartOne's services are designed to enhance AI model performance by delivering precise annotations, enabling effective communication and functionality in French-speaking markets. Key Features and Functionality: - Expertise in French Language Annotation: Native French-speaking annotators provide accurate and culturally relevant data labeling. - Comprehensive Data Annotation Services: Handling a wide range of tasks, including transcription, natural language processing, and natural language understanding. - 24/7 Operational Excellence: Round-the-clock services from secure facilities ensure timely delivery and adaptability to project scales. - Strict Security Measures: Adherence to SOC2 compliance and AWS-prescribed security controls to maintain data confidentiality. - Customized Solutions: Tailored annotation guidelines and workflows, particularly with Amazon SageMaker Ground Truth, to optimize project success. Primary Value and Problem Solved: SmartOne's French language data labeling services address the critical need for accurate and culturally sensitive datasets in AI and machine learning projects targeting French-speaking audiences. By providing precise annotations, SmartOne enhances the performance and reliability of AI models, ensuring they resonate effectively with the target market. The company's commitment to security, quality, and operational excellence offers clients a trusted partner in developing AI solutions that truly understand and engage French-speaking users.

Data Labeling Services by Vivetic- Spanish Language

Vivetic Group offers specialized data labeling and annotation services tailored for Spanish-language datasets, enhancing the accuracy and performance of machine learning models. With over 30 years of experience in business process outsourcing, Vivetic has established itself as a leader in providing high-quality, customized data solutions across various industries, including E-Commerce, Retail, and Agriculture. Their team of over 700 skilled annotators operates from secure facilities, ensuring the confidentiality and precision of every project. Key Features and Functionality: - Comprehensive Data Annotation: Vivetic specializes in labeling diverse data types, including images, videos, texts, and audio, catering to the unique requirements of each project. - Customized Strategies: Each project is approached with a tailored strategy, integrating seamlessly with AWS technologies like Amazon SageMaker to enhance model efficiency and effectiveness. - Scalability and Flexibility: Vivetic's services are designed to scale according to project demands, accommodating both startups and large enterprises without compromising quality. - Security and Reliability: Operating from SOC2 Type 1 compliant facilities, Vivetic ensures the highest levels of data security and confidentiality, with a stable team that maintains consistent quality. Primary Value and Problem Solved: Vivetic's data labeling services address the critical need for accurately annotated Spanish-language datasets, which are essential for training effective machine learning models. By providing precise and reliable annotations, Vivetic enhances the quality of AI applications, leading to improved performance and outcomes. Their commitment to excellence, security, and customized solutions ensures that clients receive data services that meet their specific needs, ultimately accelerating the development and deployment of AI models.

Data Nessie

Data Nessie is an open-source version control system designed for data lakes, offering Git-like semantics to manage and track changes in data catalogs. It enables data engineers, scientists, and analysts to apply version control principles to data management, facilitating isolated data experimentation and ensuring consistent, auditable, and reversible data evolution. Key Features and Functionality: - Branching and Merging: Allows users to create branches for experimenting with data without affecting the main branch, and merge updates when ready, enhancing collaboration and flexibility. - Time Travel and Rollbacks: Provides the ability to retrieve previous versions of the data catalog, ensuring that no data change is ever truly lost and facilitating auditing and debugging. - Compatibility with Popular Data Processing Tools: Integrates seamlessly with various data processing tools and platforms, including Apache Spark, Dremio, Flink, Trino, and Presto, allowing teams to continue using their preferred tools while benefiting from Nessie's version control capabilities. Primary Value and Problem Solved: Data Nessie addresses the complexities inherent in modern data platforms by providing a robust foundation for data governance and security. By decoupling data and metadata management from the underlying storage system, it supports a wide array of storage backends, making it a versatile tool in the data engineer's toolkit. Its Git-like functionality enhances collaboration among data teams and introduces flexibility and safety not previously available in data management.

Datasentinel - Performance and Troubleshooting Tool for PostgreSQL

Datasentinel helps DBAs and Developers to quickly analyze performance issues and identify the root cause

Datasy

Datasy is a comprehensive data management solution designed to simplify the ingestion, transformation, and utilization of data on the AWS Cloud. It offers unlimited scalability and high performance, enabling users to efficiently handle their data processing needs without requiring extensive data science expertise. Datasy automates the configuration of data processing environments, the creation and management of ETL pipelines, and the deployment of machine learning models, all through a centralized web application. Key Features and Functionality: - Infrastructure Automation: Easily configure and build data processing environments using the Datasy Configurator Web Application. - ETL Pipelines Automation: Automatically create, schedule, and manage data processing pipelines for data ingestion, transformation, modeling, and export. - Automatic Machine Learning: Selects and tunes the best prediction or forecasting algorithms from AWS's state-of-the-art offerings to achieve optimal performance. - ML Pipeline Automation: Creates, manages, and schedules pipelines for predictions and forecasts without manual intervention. - Real-Time Predictors: Facilitates the creation, updating, and monitoring of real-time predictors within a unified interface. - User-Friendly Interface: Manages all models and endpoints through a centralized web application, eliminating the need for prior data science experience. Primary Value and User Solutions: Datasy addresses the complexities of data management by automating critical processes such as environment configuration, ETL pipeline management, and machine learning model deployment. This automation reduces the need for specialized data science knowledge, allowing organizations to focus on deriving insights and making data-driven decisions. By providing a scalable and high-performance platform, Datasy enables businesses to efficiently handle large volumes of data, streamline operations, and accelerate their data analytics initiatives.

Data Sync Manager (DSM) on Linux (BYOL)

Data Sync Manager (DSM on Linux is a comprehensive solution designed to streamline and secure the synchronization of data across various platforms. Tailored for Linux environments, DSM facilitates efficient data management by ensuring consistency and integrity during data transfers. Its robust architecture supports seamless integration with existing systems, making it an ideal choice for organizations seeking reliable data synchronization solutions. Key Features and Functionality: - Efficient Data Synchronization: DSM enables rapid and reliable data transfers, minimizing downtime and ensuring data consistency across platforms. - Linux Compatibility: Specifically designed for Linux systems, DSM integrates seamlessly with various distributions, providing flexibility and ease of use. - Security Measures: Incorporates advanced security protocols to protect data during synchronization, safeguarding against unauthorized access and data breaches. - Scalability: Capable of handling large volumes of data, DSM scales to meet the needs of growing organizations without compromising performance. - User-Friendly Interface: Offers an intuitive interface that simplifies the setup and management of data synchronization tasks, reducing the learning curve for users. Primary Value and Problem Solved: DSM addresses the critical need for efficient and secure data synchronization in Linux environments. By automating and streamlining data transfers, it reduces the risk of data inconsistencies and errors that can occur with manual processes. Organizations benefit from improved operational efficiency, enhanced data integrity, and robust security measures, ensuring that their data management practices align with industry standards and regulatory requirements.

Data Sync Manager (DSM) Suite

With the world-renowned Data Sync Manager (DSM) Suite, you can copy and mask data efficiently between and within any SAP® ABAP-stack landscape, and reduce your footprint for agile, business-aligned landscapes.

Datiphy Smart Data Platform

Datiphy's Smart Data Platform is a comprehensive solution designed to enhance data security and compliance across both on-premises and cloud environments. By continuously monitoring and analyzing every data transaction, SDP provides organizations with real-time insights into their data activities, enabling proactive threat detection and efficient risk management. Its scalable architecture ensures seamless integration with various cloud services, including Amazon Web Services, Microsoft Azure, Oracle Cloud, and Google Cloud Platform. Key Features and Functionality: - Continuous Data Monitoring: Audits every data transaction within the enterprise, offering a detailed view of data activities. - Adaptive Data Behavioral Model : Analyzes database transactions in real-time to establish behavioral baselines and detect anomalies. - Risk Assessment and Compliance: Generates comprehensive reports to evaluate security controls, addressing standards such as PCI, HIPAA, SOX, GLBA, GDPR, ISO, and SOC2. - Threat Detection and Forensics: Utilizes behavior analysis and threat modeling to identify potential threats and provides investigative tools for incident analysis. - Seamless Cloud Integration: Supports deployment on major cloud platforms, ensuring consistent security across diverse infrastructures. Primary Value and Problem Solved: The Datiphy Smart Data Platform addresses the critical need for comprehensive data security by offering real-time monitoring and analysis of data transactions. It empowers organizations to detect and respond to data breaches promptly, ensuring compliance with regulatory standards and safeguarding sensitive information. By providing a unified view of data activities across various environments, SDP enhances operational efficiency and fortifies an organization's security posture.

Daylight AI-Powered MDR

Daylight is a security services company delivering Managed Agentic Security Services (MASS) for SecOps, including MDR, threat hunting, incident response, and more, through a fundamentally different architecture than traditional security services providers. Daylight's architecture combines an agentic platform that runs the full cycle from detection to response with security experts from IR and threat hunting backgrounds. The platform integrates deeply across your environment - cloud, identity, SaaS, endpoints - and collects identity and business context to investigate alerts the way a senior analyst would. It continuously learns your environment to make better decisions over time. Security experts validate decisions, feed insights into the platform, optimize detections, and take over in case of an incident. The result: security teams move from firefighting mode to strategic work that improves their security posture.

dbTimer: Economize your databases

dbTimer is a cost-effective and eco-friendly solution designed to optimize the operation of your database servers by running them only when necessary. By scheduling your databases to operate during specific times, dbTimer helps organizations save on computing costs and reduce their environmental impact. It provides a centralized view of all database instances across regions, allowing for efficient management and monitoring. With dbTimer, you can set timers to start or stop databases as needed, instantly switch database states, and track the hours saved through these optimizations. Additionally, it offers insights into the reduction of CO₂ emissions achieved by utilizing cloud resources instead of traditional data centers. Key Features and Functionality: - Unified Database Management: View and manage all running and stopped database instances across different regions in a single, consolidated list. - Automated Scheduling: Set timers to automatically start or stop databases based on your operational requirements, ensuring they run only when needed. - Instant Control: Manually start or stop any database instance instantly, providing flexibility and control over your database operations. - Savings Tracking: Monitor and analyze the hours saved through scheduled operations on a daily, weekly, monthly, and yearly basis. - Environmental Impact Assessment: Calculate and understand the reduction in CO₂ emissions achieved by optimizing database operations in the cloud. Primary Value and Problem Solved: dbTimer addresses the challenge of unnecessary operational costs and environmental impact associated with continuously running database servers. By enabling precise scheduling and management of database instances, it ensures that resources are utilized only when required, leading to significant cost savings. Additionally, by reducing the operational hours of databases, dbTimer contributes to lower energy consumption and a decrease in CO₂ emissions, supporting organizations in their sustainability efforts.

Debian 10 with support by Atomized

This product has charges associated with it for seller support and maintenance. A base installation of Debian 10 that is updated quarterly.