RaimaDB
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.
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Google Cloud Platform
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.
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Apache Gobblin
A framework for distributed data integration that streamlines essential functions of Big Data integration, including data ingestion, replication, organization, and lifecycle management, is designed for both streaming and batch data environments. It operates as a standalone application on a single machine and can also function in an embedded mode. Additionally, it is capable of executing as a MapReduce application across various Hadoop versions and offers compatibility with Azkaban for initiating MapReduce jobs. In standalone cluster mode, it features primary and worker nodes, providing high availability and the flexibility to run on bare metal systems. Furthermore, it can function as an elastic cluster in the public cloud, maintaining high availability in this setup. Currently, Gobblin serves as a versatile framework for creating various data integration applications, such as ingestion and replication. Each application is usually set up as an independent job and managed through a scheduler like Azkaban, allowing for organized execution and management of data workflows. This adaptability makes Gobblin an appealing choice for organizations looking to enhance their data integration processes.
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Oracle Big Data Service
Oracle Big Data Service simplifies the deployment of Hadoop clusters for customers, offering a range of VM configurations from 1 OCPU up to dedicated bare metal setups. Users can select between high-performance NVMe storage or more budget-friendly block storage options, and have the flexibility to adjust the size of their clusters as needed. They can swiftly establish Hadoop-based data lakes that either complement or enhance existing data warehouses, ensuring that all data is both easily accessible and efficiently managed. Additionally, the platform allows for querying, visualizing, and transforming data, enabling data scientists to develop machine learning models through an integrated notebook that supports R, Python, and SQL. Furthermore, this service provides the capability to transition customer-managed Hadoop clusters into a fully-managed cloud solution, which lowers management expenses and optimizes resource use, ultimately streamlining operations for organizations of all sizes. By doing so, businesses can focus more on deriving insights from their data rather than on the complexities of cluster management.
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