
High-Performance Data Engineering. Total Sovereignty.
TIMi delivers the power of a complete cloud data stack—on-premises, fully sovereign, and ridiculously fast.
We reject artificial vendor lock-in and hidden costs. Instead, we offer absolute peace of mind through engineering excellence, giving your team the freedom to experiment, innovate, and solve complex AI, analytics, and automation challenges in record time.
Why Top Enterprises Choose TIMi?
Enterprise Integration & *No-Code* ETL/Data preparation: Automate complex workflows and seamlessly link your entire stack: SAP, Salesforce, SharePoint, S3, Azure Storage, PowerBI, Tableau, etc.
Unmatched Infrastructure Efficiency: Our competitors such as Databricks, Dataiku, and MS Fabric all rely on Spark—and that makes them inherently inefficient since a single €2k TIMi server outperforms a 267-node Spark cluster. TIMi process billions of rows in seconds and manage petabyte-scale data lakes at a fraction of the cost.
Proven AI Leadership: Harness pioneering machine learning from the creators of the first Auto-ML engine (est. 2007).
Whether deployed on-premises or via our EU-Hosted Sovereign Cloud, TIMi empowers leaders in Banking, Telecoms, Manufacturing, Retail, Defense and Government.
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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.
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SOC Prime Platform
SOC Prime equips security teams with the largest and most robust platform for collective cyber defense that cultivates collaboration from a global cybersecurity community and curates the most up-to-date Sigma rules compatible with over 28 SIEM, EDR, and XDR platforms. Backed by a zero-trust approach and cutting-edge technology powered by Sigma and MITRE ATT&CK®️, SOC Prime enables smart data orchestration, cost-efficient threat hunting, and dynamic attack surface visibility to maximize the ROI of SIEM, EDR, XDR & Data Lake solutions while boosting detection engineering efficiency. SOC Prime’s innovation is recognized by independent research companies, credited by the leading SIEM, XDR & MDR vendors, and trusted by 8,000+ organizations from 155 countries, including 42% of Fortune 100, 21% of Forbes Global 2000, 90+ public sector institutions, and 300+ MSSP and MDR providers. SOC Prime is backed by DNX Ventures, Streamlined Ventures, and Rembrandt Venture Partners, having received $11.5M in funding in October 2021. Driven by its advanced cybersecurity solutions, Threat Detection Marketplace, Uncoder AI, and Attack Detective, SOC Prime enables organizations to risk-optimize their cybersecurity posture.
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AWS Lake Formation
AWS Lake Formation is a service designed to streamline the creation of a secure data lake in just a matter of days. A data lake serves as a centralized, carefully organized, and protected repository that accommodates all data, maintaining both its raw and processed formats for analytical purposes. By utilizing a data lake, organizations can eliminate data silos and integrate various analytical approaches, leading to deeper insights and more informed business choices. However, the traditional process of establishing and maintaining data lakes is often burdened with labor-intensive, complex, and time-consuming tasks. This includes activities such as importing data from various sources, overseeing data flows, configuring partitions, enabling encryption and managing encryption keys, defining and monitoring transformation jobs, reorganizing data into a columnar structure, removing duplicate records, and linking related entries. After data is successfully loaded into the data lake, it is essential to implement precise access controls for datasets and continuously monitor access across a broad spectrum of analytics and machine learning tools and services. The comprehensive management of these tasks can significantly enhance the overall efficiency and security of data handling within an organization.
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