Data Analytics
Data Analytics
Batch processing and analytics work best on Ampere processors.
Batch processing and analytics work best on Ampere processors.
DATA ANALYTICS
Efficient Analytics at Scale
Efficient Analytics at Scale
Big data solutions demand substantial compute capacity, along with persistent, high-performance storage and networking.
Designed for cloud-native workloads, the Ampere® Altra® family of processors deliver consistent, predictable performance for modern data analytics. High core counts, strong singlethreaded performance, and consistent frequency help big data workloads scale out efficiently.
With lower power consumption than legacy x86 platforms, Ampere processors can help increase core density per rack while reducing energy use. The result is a practical path to lower capital and operating costs at scale.
Big data solutions demand substantial compute capacity, along with persistent, high-performance storage and networking.
Designed for cloud-native workloads, the Ampere® Altra® family of processors deliver consistent, predictable performance for modern data analytics. High core counts, strong singlethreaded performance, and consistent frequency help big data workloads scale out efficiently.
With lower power consumption than legacy x86 platforms, Ampere processors can help increase core density per rack while reducing energy use. The result is a practical path to lower capital and operating costs at scale.
KEY BENEFITS
Analytics at Scale, Optimized Infrastructure
Analytics at Scale, Optimized Infrastructure
Ampere processors help data platforms scale efficiently as demand grows, with high core density and a practical path to expansion. Strong single-threaded performance supports responsive analytics and faster insight. Consistent frequency helps deliver predictable performance for more reliable capacity planning. Efficient power use can reduce infrastructure requirements and operating costs at scale.
Ampere processors help data platforms scale efficiently as demand grows, with high core density and a practical path to expansion. Strong single-threaded performance supports responsive analytics and faster insight. Consistent frequency helps deliver predictable performance for more reliable capacity planning. Efficient power use can reduce infrastructure requirements and operating costs at scale.
Scalability
Ampere processors support efficient, near-linear scaling as demand increases. High core density provides a practical foundation for expanding data platforms while maintaining operational simplicity.
Performance
Ampere processors combine high core density with strong single-threaded performance in a complete system-on-chip design. The result is responsive, efficient compute that helps teams move more data and deliver insights faster.
Consistency and Predictability
Ampere processors deliver consistent frequency and predictable performance, helping teams plan capacity with greater confidence. Performance remains steady as demand changes, supporting more reliable operations at scale.
Energy Efficiency
Ampere processors are designed to deliver high performance with efficient power use. Lower energy consumption and greater core density can help reduce infrastructure requirements and operating costs.
ARCHITECTURE
Built for Big Data
Built for Big Data
Big data workloads bring together compute, storage, and networking resources to run complex queries, aggregations, and business intelligence processes. Ampere processors can help these environments scale efficiently with high core density, predictable performance, and efficient power use.
Big data workloads bring together compute, storage, and networking resources to run complex queries, aggregations, and business intelligence processes. Ampere processors can help these environments scale efficiently with high core density, predictable performance, and efficient power use.
Key Characteristics
Data-Heavy: Processes large volumes of historical and real-time data from sources such as IoT devices, application logs, and transactional databases.
Read-Optimized: Emphasizes scanning, filtering, and aggregating large datasets to prepare information for analysis and decision-making.
Flexible Scaling: Requires horizontal scaling or independent compute and storage resources to accommodate changing query volumes and data growth.
Common Types
Batch Processing: Non-interactive analysis of large data stores at rest, often used to prepare and aggregate data for later consumption.
Real-Time Streaming: Continuous processing of data in motion, enabling timely analysis of events as they occur. Interactive Exploration: Ad hoc querying and data discovery that helps analysts investigate large datasets with responsive performance.
Predictive and AI Workloads: Data preparation, model training, and inference used to identify patterns and inform future decisions.
Core Components
Ingestion and Integration: Moving, transforming, and preparing raw data through pipelines for analysis.
Storage: Managing data in relational warehouses, file-based lakes, and modern lakehouse environments.
Consumption: Delivering insights through dashboards, reporting tools, and automated applications.
DATA ANALYTICS RESOURCES
Engage & Learn
Engage & Learn
Discover our rich library of resources to learn about current trends, significant advancements, and access strategies
for enacting your AI transformation.
Discover our rich library of resources to learn about current trends, significant advancements, and access strategies for enacting your AI transformation.
Ampere Computing LLC
4655 Great America Parkway Suite 601
Santa Clara, CA 95054
