Compare SHREWD Platform vs. lakeFS

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.

Teradata VantageCloud: Open, Scalable Cloud Analytics for AI VantageCloud is Teradata’s cloud-native analytics and data platform designed for performance and flexibility. It unifies data from multiple sources, supports complex analytics at scale, and makes it easier to deploy AI and machine learning models in production. With built-in support for multi-cloud and hybrid deployments, VantageCloud lets organizations manage data across AWS, Azure, Google Cloud, and on-prem environments without vendor lock-in. Its open architecture integrates with modern data tools and standard formats, giving developers and data teams freedom to innovate while keeping costs predictable.

BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises. Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.

dbt Labs is redefining how data teams work with SQL. Instead of waiting on complex ETL processes, dbt lets data analysts and data engineers build production-ready transformations directly in the warehouse, using code, version control, and CI/CD. This community-driven approach puts power back in the hands of practitioners while maintaining governance and scalability for enterprise use. With a rapidly growing open-source community and an enterprise-grade cloud platform, dbt is at the heart of the modern data stack. It’s the go-to solution for teams who want faster analytics, higher quality data, and the confidence that comes from transparent, testable transformations.

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times. RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.

MongoDB Atlas stands out as the leading cloud database service available, offering unparalleled data distribution and seamless mobility across all major platforms, including AWS, Azure, and Google Cloud. Its built-in automation tools enhance resource management and workload optimization, making it the go-to choice for modern application deployment. As a fully managed service, it ensures best-in-class automation and adheres to established practices that support high availability, scalability, and compliance with stringent data security and privacy regulations. Furthermore, MongoDB Atlas provides robust security controls tailored for your data needs, allowing for the integration of enterprise-grade features that align with existing security protocols and compliance measures. With preconfigured elements for authentication, authorization, and encryption, you can rest assured that your data remains secure and protected at all times. Ultimately, MongoDB Atlas not only simplifies deployment and scaling in the cloud but also fortifies your data with comprehensive security features that adapt to evolving requirements.

All stakeholder engagement activities can be managed from one platform. Additional modules can be added to strengthen governance, environment, and social areas. Borealis' Stakeholder Engagement module gives you the tools to create more effective engagement plans, strengthen relationships with stakeholders, and align processes with industry best practice. To simplify your day, the Stakeholder Engagement module centralizes all stakeholder information and makes it easily accessible. Build stakeholder trust with a proven methodology Plan Connect engagement strategy to execution. The mapping tool in Borealis makes it easy to analyze stakeholders to prioritize the allocation of resources. Engage Communicate with stakeholders in a more relevant and timely manner. Borealis uses AI-driven machine learning to keep records clean and up-to-date. Measure Prove compliance with ever-evolving standards. Borealis lets you easily track and progress, generate reports and documentation, and demonstrate the impacts of your efforts.

Propel is a modern, AI-powered cloud platform built for how manufacturers work today. It unifies Product Lifecycle Management (PLM), Quality Management System (QMS), and Product Information Management (PIM) into one intelligent system, giving teams a connected, always-accurate view of their products. With AI embedded across the platform through Propel One, manufacturers can automate routine work, surface insights faster, and make better decisions with confidence. AI continuously learns from product data to proactively flag risks, highlight trends, and guide next best actions across teams. Instead of relying on spreadsheets, siloed tools, and manual handoffs, teams manage product data, changes, and quality processes in a single governed environment with full traceability. Propel creates a true digital product record that spans design, development, manufacturing, commercialization, and continuous improvement. Built-in workflows standardize change management, streamline quality events, and support compliance without slowing teams down. Trusted by medical device, high tech, and industrial manufacturers, Propel helps organizations reduce errors, shorten cycle times, and improve collaboration across engineering, quality, operations, supply chain, and product teams. The result is faster launches, higher product quality, and a more resilient foundation for growth as products and processes become more complex. Propel has been recognized by Deloitte as a fast-growing technology company and named to the Inc. 5000 list of fastest-growing private companies, reflecting strong customer adoption and momentum helping manufacturers modernize how they build, manage, and deliver products. Designed to scale with growing products, teams, and regulatory demands.

Docket is the leading agentic AI platform that improves pipeline generation and seller efficiency for marketing & sales teams. Docket unifies your organization’s GTM data into the Sales Knowledge Lake™ and activates it with powerful, pre-built AI agents. Docket’s Marketing Agent engages website visitors through human-like conversations to convert them into qualified leads & customers, while its Sales Agent provides sellers with instant access to product knowledge and solution expertise.