Unified Data Storage

for Seamless Operations

Transform your data strategy with a lakehouse that offers real-time ingestion, high-concurrency querying, and seamless integration with existing data lakes and databases —without lengthy setups or the need for separate tools

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Your Lakehouse, Simplified:
Performance Without Complexity

Streamline your data operations with a lakehouse that scales effortlessly, queries data in real-time, and reduces operational overhead with intelligent assistants and full observability

Scalable for Any Workload

Effortlessly handle batch and streaming data, from terabytes to petabytes optimized for diverse needs

Blazing-Fast Queries

Run high-concurrency queries with sub-second response times, and extract actionable insights instantly 

Real-Time Data Ingestion

Agentic Assistant

Leverage LLM agents specialized for lakehouse operations to rapidly create queries and automate data workflows

Full Observability

Gain complete visibility into data ingestion, storage, and querying with built-in monitoring tools

Next-Gen Lakehouse for Performance, Scale, and Flexibility

Unify your data architecture with capabilities like elastic scaling, semi-structured data support, and high-concurrency analytics, all optimized for real-time and batch workload

High-Availability Clusters

Achieve uninterrupted operation with distributed clusters, fault tolerance, self-healing, and automatic failover, ensuring data reliability and minimal downtime

Blazing-Fast Ad-Hoc Queries

Leverage a columnar engine, MPP architecture, and vectorized execution for sub-second query speeds, optimized for high-concurrency and throughput

Real-Time & Batch Analytics

Handle real-time streaming and batch jobs with push-based micro-batches, streaming ingestion, and hybrid analytics on live and historical data

Elastic Scaling

Scale up or down dynamically with shared-nothing architecture and compute-storage separation, ensuring cost-effective performance at any scale

Remote Data Sources

Perform federated queries across systems like Hive, Iceberg, MySQL, and PostgreSQL without duplicating data, enabling unified analytics

Lakehouse Architecture

Combine data lake flexibility with warehouse performance, supporting structured, semi-structured, and unstructured data in one platform

Read/Write Separation

Boost concurrency by isolating read and write operations, ensuring consistent ingestion and high-performance querying without bottlenecks

High-Concurrency Workload Isolation

Isolate workloads with distributed architecture and resource pooling to ensure consistent query performance under concurrent use

Advanced Query Optimization

Use cost-based query optimization with MPP and vectorized execution to deliver faster results while minimizing resource usage

Real-World Solutions
for Modern Data Challenges

Real-Time Analytics

Organizations are shifting from traditional batch reporting to real-time dashboards that support both internal and customer-facing analytics. This transition enables decision-making to evolve from manual processes to algorithm-driven automation.

Streamline data ingestion and processing to power real-time dashboards with up-to-the-second insights. Integrate decision support analytics with automated algorithms to respond dynamically to market trends and operational demands.

Enable agile decision-making with real-time insights for both internal teams and customer-facing applications.

Ad-Hoc Analysis

Interactive ad-hoc analysis is replacing predefined reports, empowering more users to explore data independently. High-performance querying enables fast responses, making self-service analytics efficient and accessible.

Use an intuitive querying interface to extract insights instantly, regardless of data size. Foster self-service analytics across your organization to reduce bottlenecks and speed up decision-making.

Empower teams with fast, flexible analysis tools to uncover insights on demand.

Unified Data Lakehouse

Combine the openness of data lakes with the performance of data warehouses. Map external data lakes and databases directly to a high-performance query engine for seamless access and analysis.

Transform fragmented data ecosystems into a unified lakehouse architecture. Enable federated querying across external systems and achieve enterprise-wide visibility without duplication.

Leverage a unified and efficient approach to manage, analyze, and gain insights from all your data.

ELT Data Processing

The shift from traditional ETL to ELT processes allows data transformation to occur within the database itself, reducing complexity and improving efficiency. High-performance computing enables large-scale transformations and incremental data processing.

Process and transform large datasets directly in the database, simplifying workflows and reducing reliance on external tools. Support complex, high-volume queries with advanced computing capabilities.

Optimize data transformation pipelines for greater performance and simplicity.

Customer Data Platform

Gather and analyze user-specific attributes and behaviors to construct a comprehensive customer data platform. Derive actionable insights on user engagement, retention, and conversion while building precise audience segmentation.

Centralize customer data to analyze behaviors across touchpoints. Conduct in-depth segmentation to improve retention strategies and optimize user acquisition efforts.

Enhance customer understanding with a data-driven platform that enables targeted, actionable insights.

Log Analytics

Businesses require an efficient platform to store and analyze high-volume log data, whether from systems, IoT devices, or applications. High-performance indexing and retrieval make log analysis faster and more cost-effective.

Create a unified platform for structured and semi-structured log data. Enable rapid log retrieval and analytics to monitor business activities, system health, and IoT environments.

Analyze high-volume log data efficiently and cost-effectively to gain real-time operational insights.

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