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PixelsDB - Unified Storage & Compute Engine for Hybrid Data Analytics
PixelsDB is an efficient storage and compute engine designed for unified data analytics across both on-premise infrastructure and cloud-native environments. It empowers data professionals to process and analyze data seamlessly, regardless of location.
Java
Added on 2025年6月23日443
Stars127
ForksJava
LanguageProject Introduction
Summary
PixelsDB is a powerful, open-source engine that simplifies and accelerates data analytics in hybrid and multi-cloud environments. It decouples storage and compute, offering flexibility and efficiency for modern data workloads.
Problem Solved
Traditional data analytics setups often struggle with data silos across hybrid environments, leading to complex ETL processes, inconsistent performance, and increased operational overhead. PixelsDB addresses this by providing a unified, performant engine.
Core Features
Unified Data Access
Access and process data residing in various locations (cloud storage, on-prem filesystems) through a single interface.
High-Performance Compute
Leverages optimized processing techniques for high-throughput and low-latency analytical queries.
Scalable Architecture
Designed to scale horizontally to handle growing data volumes and compute demands.
Tech Stack
C++
Go
Arrow
Parquet
S3
HDFS
Use Cases (使用场景)
PixelsDB is suitable for various data-intensive applications requiring efficient processing across distributed data sources:
Hybrid Cloud Analytics
Details
Analyzing large datasets stored across both on-premise data lakes (e.g., HDFS) and cloud object storage (e.g., S3, GCS) without migrating data.
User Value
Enables seamless analytics on data wherever it resides, reducing egress costs and complexity.
Accelerated BI & Reporting
Details
Serving data for BI dashboards and reporting tools directly from raw data files in distributed storage with low latency.
User Value
Provides faster access to fresh data for reporting, improving decision-making speed.
Data Transformation Pipelines
Details
Powering ETL/ELT pipelines by efficiently reading from diverse sources and writing to analytical formats.
User Value
Simplifies and accelerates data transformation steps in modern data architectures.
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