Best Data Warehouse Solutions - Page 6

Arraylake

Arraylake is a cloud platform that modernizes how organizations work with multidimensional scientific data. The platform combines cloud-native infrastructure with cloud-optimized data formats that use tensors (n-dimensional arrays) rather than traditional tabular structures as the core data model, enabling high-performance and cost-effective storage, analysis, and collaboration for complex weather, climate, and geospatial datasets. Built on an open source core by the leading contributors to it, the Earthmover platform offers customers the strategic benefits of building on open source combined with the convenience of mind of a turn-key solution.

Broadridge Data Management and Archival

Transform and repurpose your data across all document types and channels while supporting retention requirements in a secure, searchable and conveniently accessible environment.

Broadridge Data Management Warehouse and Reporting

Broadridge's innovative data analytics solution processes complex, sophisticated data and delivers outcomes designed to power efficient, informed decision-making.

CelerData Cloud

CelerData Cloud is the fastest, secure analytical engine that powers customer-facing and AI-driven analytics at scale, delivering consistently reliable and unbeatable performance with a future-proof architecture—ensuring real-time access to open data without ingestion delays or costly data pipelines. Powered by StarRocks, CelerData delivers 3X the performance/cost of any other solution on the market and is the only platform uniquely designed to enable users to simplify their lakehouse architecture and ditch the need for a data warehouse. CelerData is used worldwide by market-leading brands including Coinbase, Pinterest, Demandbase, and Expedia to generate critical new insights for these data-driven companies.

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DataFleets - Federated Learning and SQL

“Creating machine learning models that learn across all of our customers without aggregating any data. Now that’s a killer app.” - Lead Data Scientist at a Fortune 500 Company Introducing DataFleets. The world's first cloud platform for unified and privacy-preserving enterprise data analytics powered by Federated Learning. It's never been easier to securely bridge data silos and create new data-driven products with strong network effects. DataFleets allows data teams to ship their analytics out to data, wherever it resides, analyzing it compliantly (e.g., GDPR, CCPA) with game-changing results: 10x available data and 10x speed in accessing it. Offering enterprise-ready, cloud-agnostic analytics with unparalleled performance DataFleets' tech has first-class support for a full suite of data science and machine learning tools, allowing no change in workflow and unparalleled performance. Our flexible and open-source technology makes it easy to deploy Privacy Enhancing Technologies (PETs) such as federated learning, differential privacy, secure multi-party computation, homomorphic encryption, and attack-based privacy evaluation. You'll never need lossy data masking or tokenization again. Our integrations and partnerships span Apache Spark, Apache Arrow, Tensorflow, Keras, Scikit Learn, H20.ai, PySyft, PyTorch, Kubernetes, Amazon Web Services (AWS), Google Cloud (GCP), Alibaba Cloud, and NVIDIA. We offer first-class support for Microsoft Azure and Microsoft WhiteNoise differential privacy platform. Measurably improve your data security, privacy, and compliance DataFleets provides robust and auditable security and privacy guarantees approved by regulators. We uphold three best-practice principles: No data ever moves from its original and secure location No row-level data is ever exposed to an analyst All analytics results are anonymized to best-in-class standards like GDPR, CCPA, and HIPAA Ready to accelerate your data teams' agility and speed? Learn more at www.datafleets.com

Datagres PerfAccel

Datagres PerfAccel is a data management platform that delivers real time optimized server and storage performance for applications.

DATAmaestro

PEPITe offers a no-code web based application, DATAmaestro, used in factories and industries to deploy, at scale, advanced analytics and machine learning for production process optimization. Thanks to faster data accessibility and user friendly interfaces, process engineers and plant operators spend more quality time using data to improve manufacturing process performance, specifically: - Energy efficiency and emissions control - Yield of raw materials - Product quality - Predictive and prescriptive maintenance - Production throughput Features: - Automate data collection and efficient storage - Accelerate data merging, cleansing and preparation for analytics - Calculate features, KPIs, and analyse variability - - Quickly visualize and troubleshoot issues using data - Diagnose, predict and prescribe actions with machine learning tools tailored for manufacturing industry - Optimize operations with web based real-time dashboards showing machine learning models that support fact-based decisions - Automatically recalibrate models with new data With or without experience in data science, we can help you and your teams quickly deploy advanced analytics in your organisation, reap benefits and scale step-by-step.

Data Management Solutions

Springbord is a leading global information service provider that develops custom data acquisition & processing solutions for a broad spectrum of industries.

Data Warehouse for Insurance Company

DICEUS Insurance Data Warehouse is an enterprise-grade data platform designed for insurance companies to consolidate, manage, and analyze data across underwriting, claims, policy administration, finance, and customer systems within a single, governed environment. The platform creates a unified source of truth by integrating structured and unstructured data through real-time and batch ingestion, enabling insurers to access accurate, up-to-date information for reporting, analytics, and decision-making. Built on a multi-layer architecture (STAGE, ODS, CORE, MIS, ADM), the solution supports data validation, transformation, historical tracking, and advanced analytics, ensuring consistency, traceability, and high data quality across all business processes. Unlike traditional data warehouses, the platform combines strong data governance with flexible analytics capabilities, allowing insurers to standardize calculations for key metrics such as premiums, risk scores, and loss ratios while maintaining full control over data lineage and compliance requirements. Configurable KPI dashboards and prebuilt regulatory reporting modules enable faster insights and simplified compliance, while role-based access controls and audit trails ensure secure data management across teams. The solution is designed for scalability and can be deployed as a cloud-native, on-premise, or hybrid architecture, adapting to growing data volumes, new data sources, and evolving business needs without disrupting operations. Operational efficiency is further enhanced through automated data workflows, centralized monitoring, and integrated support tools that provide real-time visibility into data pipelines, system performance, and issue resolution. With support for modern data ecosystems and integration with BI tools, analytics platforms, and downstream systems, DICEUS Insurance Data Warehouse enables insurers to improve decision-making, optimize operations, enhance customer insights, and build a scalable foundation for advanced analytics and AI-driven initiatives.

Data Warehouse Models

A DWH model is an accelerator helping companies in setting up DWH solutions, extracting insights that support a data-driven managerial decision making process. It is a logical model that organizes data items in a way to enable fast and reliable extraction of data for various analytical purposes, machine learning, and reporting. The PI DWH Models are the world�s leading, industry standard data warehouse solutions for telecommunication, banking, insurance and retail industries. The main benefits of our clear and precise DWH models (which follow industry requirements) are to focus the work of a data analyst from the data preparation process towards data usage and data monetization. They are based on best practices, developed and applied during DWH implementations in major organizations. To this day data is not the same as information, the need to speed up the process from data to insights (information) is relentlessly growing, as well as number of different data items in your data landscape. Therefore, the good organization of data artifacts is crucial for prompt responses to market changes and/or regulatory requirements.

Deepgreen DB

Deepgreen DB is an advanced, massively parallel processing (MPP database designed to enhance data warehousing and analytics performance. Building upon the Greenplum database, Deepgreen DB offers significant optimizations, including up to 5x faster execution of TPC-H benchmarks compared to its predecessor. Its architecture supports seamless integration with various data sources and cloud storage solutions, facilitating efficient data management and analysis. Key Features and Functionality: - Enhanced Performance: Deepgreen DB delivers substantial speed improvements, enabling clusters to handle more extensive workloads without the need for costly expansions. - Broad Connectivity: The database effortlessly connects to cloud storage and diverse data sources such as HDFS, S3, Oracle, Geode, and Elasticsearch. This capability allows for dynamic querying of fresh data from external sources without prior loading. - Advanced Analytics Integration: Deepgreen DB's tight integration with TensorFlow facilitates high-bandwidth machine learning training and enables in-database inference using SQL. - True Sampling Support: The database includes built-in support for true sampling with SQL, allowing users to sample data by a specific number of rows or by percentage, enhancing analytical flexibility. - Compatibility and Ease of Transition: Deepgreen DB is 100% binary compatible with Greenplum, making the transition process straightforward: 1. Stop Greenplum 2. Swap binaries 3. Start Deepgreen Primary Value and User Solutions: Deepgreen DB addresses the critical need for high-performance, scalable, and flexible data warehousing solutions. By offering significant speed enhancements and seamless integration with various data sources, it empowers organizations to manage and analyze large datasets more efficiently. The compatibility with Greenplum ensures a smooth transition, minimizing downtime and leveraging existing infrastructure investments. Additionally, the integration with machine learning frameworks like TensorFlow positions Deepgreen DB as a comprehensive platform for advanced analytics, enabling users to derive deeper insights and drive data-driven decision-making.

DIgSILENT StationWare

DIgSILENT StationWare provides centralized asset management software for primary and secondary equipment.

Dimodelo Data Warehouse Studio for SQL Server

Dimodelo Data Warehouse Studio is a comprehensive data warehouse automation tool designed to streamline the development and deployment of data warehouses on Microsoft SQL Server. Integrated as a plug-in for Visual Studio 2015 and 2019, it offers a familiar environment for developers to design, generate, and manage data warehouse solutions efficiently. Key Features and Functionality: - Visual Data Warehouse Designer: Utilize a drag-and-drop interface to design star schemas, with automatic relationship detection and dimension table suggestions. - Source System Integration: Import schemas from various sources, including databases , ODBC connections , and file formats . - Pattern-Driven ETL Processes: Apply predefined extract patterns such as full extract, incremental extract, date range extract, and file extract to generate appropriate code. - Advanced Features: Define ColumnStore indexes, custom indexes, multiple extracts for staging tables, and manage schemas for different entity types. - View Creation: Create views in the staging layer and use them as sources for dimensions and facts, supporting complex source queries and auto-mapping of source to target columns. - Dimension and Fact Development: Import schemas, define business keys, attributes, measures, and establish relationships between facts and dimensions, including support for role-playing dimensions. - Deployment and Management: Generate and deploy changes to SQL Server, Azure SQL Database, or Azure Synapse Analytics, with support for multiple environments and integration with source control systems like Git/Azure DevOps. Primary Value and User Solutions: Dimodelo Data Warehouse Studio accelerates the data warehouse development lifecycle by automating design, code generation, and deployment processes. It reduces manual coding efforts, minimizes errors, and ensures adherence to best practices. By providing a unified platform for designing and managing data warehouses, it enables organizations to respond swiftly to changing business requirements, maintain data quality, and achieve faster time-to-insight. Its integration with Visual Studio ensures a seamless experience for developers, leveraging existing skills and tools.

Enterprise Crystal Ball

Koriv's Enterprise Crystal Ball (ECB) is a database driven, web based, secure enterprise solution built specifically to capture element relationships in an enterprise

Fujitsu Symfoware Analytics Server

Symfoware Analytics Server is Fujitsu's high-performance database software that is designed for use with data warehouses.