Best Data Lake Solutions in Germany

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

Datametica's birds have unmatched capabilities, which help to eliminate business risks, time, frustration, anxiety, and cost from the entire process for data warehouse migration to cloud. Datametica's automated product suite allows you to migrate existing data warehouses, data lakes, ETL, Enterprise business intelligence, and other data to the cloud environment of choice. Designing an end to end migration strategy that includes workload discovery, assessment and planning. From the discovery and assessment of your data warehouse to the planning of the migration strategy, Eagle provides clarity on what needs to be migrated, in what order, how to streamline the process, and what the costs and timelines are. The integrated view of the workloads and planning minimizes migration risk without affecting the business.

Many customers of Amazon Web Services (AWS), require data storage and analytics solutions that are more flexible and agile than traditional data management systems. Data lakes are a popular way to store and analyze data. They allow companies to manage multiple data types, from many sources, and store these data in a central repository. AWS Cloud offers many building blocks to enable customers to create a secure, flexible, cost-effective data lake. These services include AWS managed services that allow you to ingest, store and find structured and unstructured data. AWS offers the data solution to support customers in building data lakes. This is an automated reference implementation that deploys an efficient, cost-effective, high-availability data lake architecture on AWS Cloud. It also includes a user-friendly console for searching for and requesting data.

Big data is essential for solving today's industry and enterprise problems. The ability to capture data from across your enterprise--whether generated by disparate applications, people, or IoT infrastructure-offers tremendous potential. Data Lake tools from Infor provide schema-on-read intelligence and a flexible data consumption framework that enables new ways to make key decisions. You can use leveraged access to all of your Infor ecosystem to start capturing and delivering large data to power your next generation machine learning and analytics strategies. The Infor Data Lake is infinitely scalable and provides a central repository for all your enterprise data. You can grow with your insights and investments, ingest additional content for better informed decision making, improve your analytics profiles and provide rich data sets that will enable you to build more powerful machine-learning processes.

Data Lake Governance Center (DGC) is a one-stop platform for managing data design, development and integration. It simplifies big data operations and builds intelligent knowledge libraries. A simple visual interface allows you to build an enterprise-class platform for data lake governance. Streamline your data lifecycle, use metrics and analytics, and ensure good corporate governance. Get real-time alerts and help to define and monitor data standards. To create data lakes faster, you can easily set up data models, data integrations, and cleaning rules to facilitate the discovery of reliable data sources. Maximize data's business value. DGC can be used to create end-to-end data operations solutions for smart government, smart taxation and smart campus. Gain new insights into sensitive data across your entire organization. DGC allows companies to define business categories, classifications, terms.

NewEvol is a technologically advanced product suite that uses advanced analytics and data science to identify anomalies in data. NewEvol is a powerful tool that can be used to compile data for small and large enterprises. It supports rule-based alerting, visualization, automation, and responses. NewEvol is a robust system that can handle challenging business requirements. NewEvol Expertise 1. Data Lake 2. SIEM 3. SOAR 4. Threat Intelligence 5. Analytics

The only fully-managed cloud data lakehouse that can ingest data from all of your sources in minutes, and support all of your query engines on a large scale. All for a fraction the cost. With the ease of fully managed pipelines, you can ingest data from databases and event streams in near-real-time. You can query your data using any engine and support all of your use cases, including BI, AI/ML, real-time analytics and AI/ML. Simple usage-based pricing allows you to cut your costs by up to 50% compared with cloud data warehouses and ETL software. With a fully-managed, highly optimized cloud service, you can deploy in minutes and without any engineering overhead. Unify all your data into a single source and eliminate the need for data to be copied between data lakes and warehouses. Apache Hudi, Apache Iceberg and Delta Lake all offer omnidirectional interoperability, allowing you to choose the best table format for your needs. Configure managed pipelines quickly for database CDC and stream ingestion.

Create data products in seconds from any source, without moving data. You can make them available to anyone while still maintaining total control. Deliver powerful experiences to unlock the value. Enhance your data mesh through seamless sharing, discovery, and governance of data across domains. Unified access to high-quality products will accelerate innovation and foster collaboration. Access AI models for all users. Control the way data interacts with AI in order to protect intellectual property. Automate AI workflows for rapid integration and iteration of new capabilities. Snowflake allows you to access and build data products without having to move any data. Enjoy the ease of getting even more out of your data. Allow anyone to easily analyze data, and eliminate the need for central provisioning of infrastructure and software. Data products are seamlessly integrated with tools to ensure governance and speed up outcomes.

Open, hybrid data lakes for AI and analytics can be used to put your data to use, wherever it is located. Connect your data in any format and from anywhere. Access it through a shared metadata layer. By matching the right workloads to the right query engines, you can optimize workloads in terms of price and performance. Integrate natural-language semantic searching without the need for SQL to unlock AI insights faster. Manage and prepare trusted datasets to improve the accuracy and relevance of your AI applications. Use all of your data everywhere. Watsonx.data offers the speed and flexibility of a warehouse, along with special features that support AI. This allows you to scale AI and analytics throughout your business. Choose the right engines to suit your workloads. You can manage your cost, performance and capability by choosing from a variety of open engines, including Presto C++ and Spark Milvus.

Storage that does not lock data in. Managed data lakes allow you to get up and running quickly. You don't need to be a data expert to store, retrieve, and access data. Cribl Lake prevents you from drowning in information. Store, manage, enforce policies on data, and access it when you need to. Open formats and unified policies for retention, security and access control will help you to embrace the future. Let Cribl do the heavy lifting to make data usable and valuable for the teams and tools who need it. Cribl Lake allows you to be up and running in minutes, not months. Zero configuration thanks to automated provisioning and pre-built integrations. Streamline workflows using Stream and Edge to streamline data ingestion and routing. Cribl Search allows you to get the most out of your data, no matter where it is stored. You can easily collect and store your data for long-term storage. Define specific retention periods to comply with legal and business requirements.

Talend Data Fabric's cloud services are able to efficiently solve all your integration and integrity problems -- on-premises or in cloud, from any source, at any endpoint. Trusted data delivered at the right time for every user. With an intuitive interface and minimal coding, you can easily and quickly integrate data, files, applications, events, and APIs from any source to any location. Integrate quality into data management to ensure compliance with all regulations. This is possible through a collaborative, pervasive, and cohesive approach towards data governance. High quality, reliable data is essential to make informed decisions. It must be derived from real-time and batch processing, and enhanced with market-leading data enrichment and cleaning tools. Make your data more valuable by making it accessible internally and externally. Building APIs is easy with the extensive self-service capabilities. This will improve customer engagement.

BryteFlow creates the most efficient and automated environments for analytics. It transforms Amazon S3 into a powerful analytics platform by intelligently leveraging AWS ecosystem to deliver data at lightning speed. It works in conjunction with AWS Lake Formation and automates Modern Data Architecture, ensuring performance and productivity.

Apache Hadoop is a software library that allows distributed processing of large data sets across multiple computers. It uses simple programming models. It can scale from one server to thousands of machines and offer local computations and storage. Instead of relying on hardware to provide high-availability, it is designed to detect and manage failures at the application layer. This allows for highly-available services on top of a cluster computers that may be susceptible to failures.

Azure Data Lake offers all the capabilities needed to make it easy to store and analyze data across all platforms and languages. It eliminates the complexity of ingesting, storing, and streaming data, making it easier to get up-and-running with interactive, batch, and streaming analytics. Azure Data Lake integrates with existing IT investments to simplify data management and governance. It can also seamlessly integrate with existing IT investments such as data warehouses and operational stores, allowing you to extend your current data applications. We have the experience of working with enterprise customers, running large-scale processing and analytics for Microsoft businesses such as Office 365, Microsoft Windows, Bing, Azure, Windows, Windows, and Microsoft Windows. Azure Data Lake solves many productivity and scaling issues that prevent you from maximizing the potential of your data.

Delta Lake is an open-source storage platform that allows ACID transactions to Apache Spark™, and other big data workloads. Data lakes often have multiple data pipelines that read and write data simultaneously. This makes it difficult for data engineers to ensure data integrity due to the absence of transactions. Your data lakes will benefit from ACID transactions with Delta Lake. It offers serializability, which is the highest level of isolation. Learn more at Diving into Delta Lake - Unpacking the Transaction log. Even metadata can be considered "big data" in big data. Delta Lake treats metadata the same as data and uses Spark's distributed processing power for all its metadata. Delta Lake is able to handle large tables with billions upon billions of files and partitions at a petabyte scale. Delta Lake allows developers to access snapshots of data, allowing them to revert to earlier versions for audits, rollbacks, or to reproduce experiments.

Kylo is an enterprise-ready open-source data lake management platform platform for self-service data ingestion and data preparation. It integrates metadata management, governance, security, and best practices based on Think Big's 150+ big-data implementation projects. Self-service data ingest that includes data validation, data cleansing, and automatic profiling. Visual sql and an interactive transformation through a simple user interface allow you to manage data. Search and explore data and metadata. View lineage and profile statistics. Monitor the health of feeds, services, and data lakes. Track SLAs and troubleshoot performance. To enable user self-service, create batch or streaming pipeline templates in Apache NiFi. While organizations can spend a lot of engineering effort to move data into Hadoop, they often struggle with data governance and data quality. Kylo simplifies data ingest and shifts it to data owners via a simple, guided UI.

End-to-end DataOps built upon an agile platform that protects and improves your data assets. Arena is the leading augmented data management platform. Our active data catalog allows for self-service data enrichment to control complex data environments. You can create custom workflows to increase the reliability and accuracy of each data set. Machine-learning can be used to identify and align master assets for better data decisions. Superior security is assured with complete lineage, including detailed visualizations and masking. Data management is easy with Arena. Arena can catalog your data from any location. Our extensible connections allow for analytics across all your preferred tools. Overcome data sprawl challenges with our software. Our software is designed to drive business and analytics success, while also providing the controls and extensibility required in today's multicloud data complexity.

Palo Alto Networks solutions can be enabled by integrating security data from your enterprise. Rapidly simplify security operations by integrating, transforming, and collecting your enterprise's security information. Access to rich data at cloud native scale enables AI and machine learning. Using trillions of multi-source artifacts, you can significantly improve detection accuracy. Cortex XDR™, the industry's leading prevention, detection, response platform, runs on fully integrated network, endpoint, and cloud data. Prisma™, Access protects applications, remote networks, and mobile users in a consistent way, no matter where they are. All users can access all applications via a cloud-delivered architecture, regardless of whether they are at headquarters, branch offices, or on the road. Combining Panorama™, Cortex™, and Data Lake management creates an affordable, cloud-based log solution for Palo Alto Networks Next-Generation Firewalls. Cloud scale, zero hardware, available anywhere.

A single storage platform can eliminate data silos. Tiered storage and policy management can help you reduce costs. Azure Active Directory (Azure AD), and role-based access control(RBAC) can authenticate data. You can also help protect your data with advanced threat protection and encryption at rest. Flexible mechanisms provide protection for data access, encryption, network-level control, and more. Highly secure. A single storage platform that supports all the most popular analytics frameworks. Cost optimization through independent scaling of storage, compute, lifecycle management and object-level Tiering. With the Azure global infrastructure, you can meet any capacity requirement and manage data with ease. Large-scale analytics queries run at high performance.

Varada's adaptive and dynamic big data indexing solution allows you to balance cost and performance with zero data-ops. Varada's big data indexing technology is a smart acceleration layer for your data lake. It remains the single source and truth and runs in the customer's cloud environment (VPC). Varada allows data teams to democratize data. It allows them to operationalize the entire data lake and ensures interactive performance without the need for data to be moved, modelled, or manually optimized. Our ability to dynamically and automatically index relevant data at the source structure and granularity is our secret sauce. Varada allows any query to meet constantly changing performance and concurrency requirements of users and analytics API calls. It also keeps costs predictable and under control. The platform automatically determines which queries to speed up and which data to index. Varada adjusts the cluster elastically to meet demand and optimize performance and cost.

AWS Lake Formation makes it simple to create a secure data lake in a matter of days. A data lake is a centrally managed, secured, and curated repository that stores all of your data. It can be both in its original form or prepared for analysis. Data lakes allow you to break down data silos, combine different types of analytics, and gain insights that will guide your business decisions. It is a time-consuming, manual, complex, and tedious task to set up and manage data lakes. This includes loading data from different sources, monitoring data flows, setting partitions, turning encryption on and managing keys, defining and monitoring transformation jobs, reorganizing data in a columnar format, deduplicating redundant information, and matching linked records. Once data has been loaded into a data lake, you will need to give fine-grained access and audit access over time to a wide variety of analytics and machine learning tools and services.

Data lakehouse is an open architecture that allows you to store, understand and analyze all of your data. It combines the power, richness, and flexibility of data warehouses with the breadth of open-source data technologies. A data lakehouse can easily be built on Oracle Cloud Infrastructure (OCI). It can also be used with pre-built AI services such as Oracle's language service and the latest AI frameworks. Data Flow, a serverless Spark service, allows our customers to concentrate on their Spark workloads using zero infrastructure concepts. Customers of Oracle want to build machine learning-based analytics on their Oracle SaaS data or any SaaS data. Our easy-to-use connectors for Oracle SaaS make it easy to create a lakehouse to analyze all of your SaaS data and reduce time to solve problems.

A data lake is a central repository for big data and AI computing. It allows you to store both structured and unstructured data at any size. Data Lake Formation (DLF), is a key component in the cloud-native database lake framework. DLF is a simple way to create a cloud-native database lake. It integrates seamlessly with a variety compute engines. You can manage metadata in data lakes in an centralized manner and control enterprise class permissions. It can systematically collect structured, semi-structured and unstructured data, and supports massive data storage. This architecture separates storage and computing. This allows you to plan resources on demand and at low costs. This increases data processing efficiency to meet rapidly changing business needs. DLF can automatically detect and collect metadata from multiple engines. It can also manage the metadata in a central manner to resolve data silo problems.

Our platform is the only one that offers an end-to–end platform for AI-powered innovation. It can handle everything from data cleansing and structuring to creating and deploying advanced data-science models to infusing advanced analytics algorithms, to infusing Recommendation AI, to deducing outcomes with simple-to-deduce visualization dashboards as well as Explainable AI to track how the outcomes were calculated. Our platform provides a seamless, holistic data science experience. FutureAnalytica offers key features such as a robust Data Lakehouse and an AI Studio. There is also a comprehensive AI Marketplace. You can also get support from a world-class team of data-science experts (on a case-by-case basis). FutureAnalytica will help you save time, effort, and money on your data-science and AI journey. Start discussions with the leadership and then a quick technology assessment within 1-3 days. In 10-18 days, you can create ready-to-integrate AI solutions with FA's fully-automated data science & AI platform.

Limited competition due to high barriers to entry, specialized knowledge, massive capital requirements, and long times to market. The price and performance of existing platforms are virtually identical, reducing the incentive for a switch. It takes months to migrate from one engine's SQL dialect into another engine's SQL. Interoperable with all major standards. Data leaders in enterprise are being hit by a massive surge in computing demand. They are surprised to discover that 10% of heavy, compute-intensive uses cases consume 80% the cost, engineering efforts and stakeholder complaints. Unfortunately, these workloads are mission-critical and nondiscretionary. e6data increases ROI for enterprises' existing data platforms. e6data’s format-neutral computing is unique in that it is equally efficient and performant for all leading data lakehouse formats.