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The Intelligent OLAP
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Kyligence was founded in 2016 by the original creators of Apache Kylin™, the leading open source OLAP for Big Data. Kyligence offers an Intelligent OLAP Platform to simplify multi-dimensional analytics for cloud data lakes. Its AI-augmented engine detects patterns from most frequently asked business queries, builds governed data marts automatically and brings metrics accountability on the data lake to optimize data pipeline and avoid excessive number of tables. It provides a unified SQL interface between the cloud object store, cubes, indexes and underlying data sources with a cost-based smart query router for business intelligence, ad-hoc analytics and data services at PB-scale.
The speed and agility with which Kyligence allows us to access data and empower our team with the right information means we're able to make sound business decisions quickly.
The Kyligence team has demonstrated unmatched technical expertise from architecture building to implementation. Together we were able to develop an AWS-based data lake architecture that met the strictest requirements of IoV to process batch and real-time streaming data.
The performance boost from Kyligence has enabled us to take full advantage of the scalability big data platforms offer.
Kyligence acts as our bridge between our data lake and data applications and meets the low latency and high concurrency requirements for our self-service analytics platform.
Finding the time-series data of a single-vehicle against hundreds of billions of levels of details has always been a huge challenge for us. Kyligence's pre-computation capability at scale, combined with time-dimensional aggregation algorithm, helped solve this Internet of Vehicles query conundrum.
Embrace Digital Transformation More Efficiently and at a Lower Cost
*The above data is retrieved from real customer cases. Please refer to our case studies.
On-Premise or Multi-Cloud, Kyligence AI Supports It All
Map complex data into business terms. Run as a service for every consumer with universal data definitions and industry-standard interfaces at the petabyte scale.
Identify valuable data assets by observing analyst behavior, data profiles, and SQL history to offer recommendations for compute and storage optimization.
Support on-premise, public, and private cloud deployments. Use a separate storage and computing architecture and allow the elastic scaling of computing resources.
Support data acquisition from different platforms including streaming data, databases, data lakes, and clouds, all while providing a unified interface for downstream applications.
OLAP on Data Lake
Data as a Service
SSAS Alternative
Excel on Big Data
Semantic Layer
BI Self-Service on Tableau
Consolidate data, eliminate data silos, reduce repetitive data development workloads, ensure data security, support flexible modification, lower model change costs, quickly respond to business needs, and shorten business operation cycles.
Eliminate data silos, reduce repetitive data development works, support flexible modification, and lower model change costs.
Deliver stable high-performance, high-concurrency big data services, and reduce IT operational and maintenance costs by providing system support.
Empower non-tech users, democratize data analytics, and enable self-service business analytics for all.
Jan. 11, 2022
Mar. 24, 2022
Mar. 08, 2022
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