KubeLake

This component is responsible for collecting and acquiring data from various sources, such as transactional systems, IoT devices, or web apps. Data collection and ingestion, data routing, error handling and recovery, diversified connectivity, handling large data streams in batch of real-time, all in a visual interface, are just some of the features that KubeLake offers.

Our Data Lake offers a centralized and scalable repository for all types of data, from structured to semi-structured and unfiltered data. It allows analysts and the business teams to access and explore data in a simple and efficient way, for better decision-making. With support for S3-compatible object storage, it provides the flexibility and interoperability needed in data storage and management.

The central source for the data within the platform, the catalog offers the possibility to find and quickly access the information you need. This allows you to have a panoramic view of your data resources and to avoid fragmentation and duplication of information within the company. Easily catalog and organize the metadata associated with each dataset.

Our data storage system is designed to provide fast and reliable access to data for different types of users, from analysts to managers. From real-time transaction management (OLTP), to facilitating multidimensional analysis (OLAP) and efficient storage of historical data (Warehouse), the database component is essential for critical operations at scale and complex data analysis.

This component ensures that the data is transformed and prepared for analysis and reporting in a fast and efficient way. Covering both batch and real-time processing, as well as allowing to build data processing flows and managing computing power management, this stage is crucial to ensure the quality and integrity of the data used in your company's decision-making process.

Through a simple and intuitive interface to explore and analyze data, your team of analysts and managers can quickly create and share custom visualizations and reports to extract valuable insights. Through interactive exploration and analysis, data cataloging, advanced search, dependency visualizations, and lineage, your teams can have an overview and understand data better.

Data visualization is a crucial component of the process of analyzing and interpreting information. The various types of visualizations (personalized dashboards, interactive visualizations) facilitate the understanding and identification of key trends and patterns in the data, providing valuable insights for informed decision making.

The data exposed component facilitates data access and query and offers a secure and scalable interface through which users can access and explore the data stored in the platform, through distributed or complex queries, as well as real-time and batch data query, and support for pull and push data exposure model.

The Machine Learning component allows us to build and train predictive models to better understand customer behavior, to identify market trends and to make better informed decisions in real time. Designed to be more than just a ML tool, you can explore and customize AI models in your endeavor to carry out research activities.

Through the observability component, we can effectively monitor and manage the health and performance of our entire data infrastructure, ensuring that it operates at maximum capacity to support business needs. Data traceability, as well as monitoring logs, cluster performance, and message queues, helps us ensure the operational reliability of the system.

Given the large volume and diversity of managed data, security is a necessity. The platform is designed to respect fundamental security principles, including confidentiality, integrity and data availability. A crucial aspect of security is ensuring that only authorized users have access to data, and that sensitive data is available to a limited group of users.