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Realtime LakeHouse Formation SaaS

Through the convenient visualization operation, the stream batch management and real-time synchronization of multi-source, massive realtime data management can be accomplished efficiently to form a modern realtime LakeHouse.

Efficient, real-time, low-code SaaS solution

Based on LakeSoul, the only domestic LakeSoul storage framework, a multi-source data real-time lake entry solution is created. This solution can efficiently and conveniently complete the import of massive real-time data, support the real-time synchronization of the whole database tables, and support the automatic Schema change and automatic sensing of new tables. Seamlessly interconnect with various business scenarios, realize intelligent data applications, and maximize the release of data value.

Feature-rich

Support upstream MySQL, Oracle, and Kafka multi-source data in real-time into the lake; After being imported, data can be analyzed and calculated in realtime, and snapshots query is also supported. The downstream adapts a variety of BI and AI engines to release the value of data.

Efficient and stable

High-speed updates on cloud object storages, real-time calculation in T+0 style, and rapid feedback of service effects. Checkpoint is automatically applied to achieve exactly-once data sink.

Simple and quick

Easily deployed on various public and private clouds, run on Hadoop and K8s clusters, with frontend interface for configuration and development, simplifying the development process and making it easy to get started.

From the data source to the application of a complete lake warehouse integrated ecosystem

Modern data architecture of ELT. Upstream supports a variety of data sources accessible through the integrated platform to achieve real-time data synchronization, real-time data warehouse, reports, machine learning, OLAP, and other applications. The whole process helps enterprises achieve digital transformation.

Solve the contradiction between diversified data source scenarios and traditional big data architecture, and achieve efficient, flexible, safe, and reliable data use

Many scenarios

DB, log files, and other data sources can be used for real-time lake entry analysis, machine learning sample construction, and other BI and AI applications through the LakeHouse formation platform.

Efficient automatic

Without manual intervention, it can realize automatic synchronization of the whole database, automatic table construction, automatic table structure change, reduce the burden of developers.

Low cost

Computing and storage separation, elastic expansion, unified stream batch architecture, no need for multiple sets of data links, and all data are stored on cloud object storages to reduce costs.

Example of real-time online database entry report analysis

With simple configurations, such as online data sources, the whole database synchronization and real-time entry task can be started. It supports the automatic sensing of new tables and synchronizing table structure changes without human operation and maintenance. The online data is updated to the lake warehouse in real time. The BI reports and large-screen display are seamlessly connected and updated in real time so that key business indicators can be grasped at any time to support business decisions.

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