Teradata VantageCloud
Teradata VantageCloud: The Complete Cloud Analytics and AI Platform
VantageCloud is Teradata’s all-in-one cloud analytics and data platform built to help businesses harness the full power of their data. With a scalable design, it unifies data from multiple sources, simplifies complex analytics, and makes deploying AI models straightforward.
VantageCloud supports multi-cloud and hybrid environments, giving organizations the freedom to manage data across AWS, Azure, Google Cloud, or on-premises — without vendor lock-in. Its open architecture integrates seamlessly with modern data tools, ensuring compatibility and flexibility as business needs evolve.
By delivering trusted AI, harmonized data, and enterprise-grade performance, VantageCloud helps companies uncover new insights, reduce complexity, and drive innovation at scale.
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Gearset
Gearset is an enterprise‑grade Salesforce DevOps platform designed to help teams apply best practices throughout their entire release process. It offers comprehensive tooling for metadata and CPQ deployments, automated pipelines, testing, code scanning, sandbox data management, backup and archive solutions, and deep observability, giving teams unrivaled oversight and control. More than 3,000 companies, including global leaders like McKesson and IBM, depend on Gearset to deliver securely at scale.
By providing governance features, integrated audit logs, SOX/ISO/HIPAA support, parallel workflows, embedded security scanning, and compliance with ISO 27001, SOC 2, GDPR, CCPA/CPRA, and HIPAA, Gearset delivers the security and compliance enterprises need — while staying fast to adopt and easy to use. This balance of power and simplicity makes Gearset the platform of choice for organizations in highly regulated industries.
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Collate
Collate is an AI-driven metadata platform designed to provide data teams with automated tools for tasks like discovery, observability, quality, and governance, utilizing efficient agent-based workflows. Built on OpenMetadata, it boasts a unified metadata graph and includes more than 90 seamless connectors that facilitate the collection of metadata from diverse sources, including databases, data warehouses, BI tools, and data pipelines. The platform ensures data integrity by offering in-depth column-level lineage and data profiling, along with no-code quality tests. AI agents are essential for optimizing functions such as data discovery, permission-based querying, alert notifications, and large-scale incident management workflows. In addition, the platform features real-time dashboards, interactive analyses, and a collaborative business glossary that is beneficial to both technical and non-technical users, enhancing the management of valuable data assets. Its automated governance and continuous monitoring uphold compliance with regulations like GDPR and CCPA, significantly cutting down the time required to address data issues while lowering the total cost of ownership. This holistic strategy not only boosts operational efficiency but also promotes a culture of data stewardship within the organization, encouraging all stakeholders to prioritize data quality and governance. Ultimately, Collate empowers teams to harness the full potential of their data assets effectively.
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Kylo
Kylo is an open-source solution tailored for the proficient management of enterprise-scale data lakes, enabling users to effortlessly ingest and prepare data while integrating strong metadata management, governance, security, and best practices informed by Think Big's vast experience from over 150 large-scale data implementations. It empowers users to handle self-service data ingestion, enhanced by functionalities for data cleansing, validation, and automatic profiling. The platform features a user-friendly visual SQL and an interactive transformation interface that simplifies data manipulation. Users can investigate and navigate both data and metadata, trace data lineage, and access profiling statistics without difficulty. Moreover, it includes tools for monitoring the vitality of data feeds and services within the data lake, which aids users in tracking service level agreements (SLAs) and resolving performance challenges efficiently. Users are also capable of creating and registering batch or streaming pipeline templates through Apache NiFi, which further supports self-service capabilities. While organizations often allocate significant engineering resources to migrate data into Hadoop, they frequently grapple with governance and data quality issues; however, Kylo streamlines the data ingestion process, allowing data owners to exert control through its intuitive guided user interface. This revolutionary approach not only boosts operational effectiveness but also cultivates a sense of data ownership among users, thereby transforming the organizational culture towards data management. Ultimately, Kylo represents a significant advancement in making data management more accessible and efficient for all stakeholders involved.
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