Databases and Data Analytics

Why Intel for Databases?

Intel is a well-known entity in the enterprise data center, with a strong installed base for mission-critical database workloads and a reputation for leadership and innovation. Intel Xeon processors have routinely delivered application performance that is necessary to meet enterprise SLAs and generate competitive business outcomes, with leadership reliability, availability, and serviceability (RAS) capabilities to maximize database availability, system uptime, and efficiency. With a mature and robust partner ecosystem that spans the spectrum from edge to cloud, deep software optimizations, and industry-leading data security technologies, Intel is best positioned to address the growing and ever-changing needs of the modern enterprise.

Learn more about why Intel is your ideal infrastructure partner

Learn more about the AI-ready RAS features of Intel Xeon 6 processors

The Intel Xeon Database Advantage

Intel Xeon processors are excellent for demanding database workloads. Their large L3 caches, support for massive DDR5 RAM, and Error-Correcting Code (ECC) memory ensure high throughput, low latency, and rock-solid data integrity for enterprise Microsoft SQL Server, Online Transaction Processing (OLTP) environments, and in-memory analytics.

The latest Intel Xeon 6 processors bring unique, differentiated capabilities in the form of large memory support (up to 8TB) on just a 2-socket system, hardware-based memory tiering with Compute Express Link (CXL) with Intel® Flat Memory Mode, and increased memory bandwidth capabilities with Multiplexed Rank Dual In-line Memory Modules (MRDIMMs), enabling enterprises to accelerate database workloads. Additionally, HW accelerators like Intel® QuickAssist Technology (Intel® QAT) and Intel® In-Memory Analytics Accelerator (Intel® IAA) offload computationally intensive tasks such as data compression and encryption, thus freeing CPU cycles to run business-critical workloads faster, more efficiently, and potentially consuming less power. Similarly, software libraries like Intel® Scalable Vector Search (Intel® SVS) enable vector database functions to be optimized to deliver significant performance benefits within AI inference pipelines.

See more details on why Intel Xeon processors are ideally suited for database workloads.

With Intel Xeon CPUs already being the foundation of enterprise infrastructure, businesses can now refresh their hardware with Intel Xeon 6 and power an AI-ready data center—one that delivers performance, server consolidation to reduce physical and carbon footprint, lower power & cooling (OpEx) costs, and savings on core-based software licensing applications.

The latest Intel Xeon processors enable enterprises to set up an environment that is built to run the workloads of today and the future efficiently, making them more competitive, more agile, and more responsive to change.

See how Intel IT was able to lower commercial software licensing costs with 5th Gen Intel® Xeon® processors.

Global partner ecosystem offering versatility in deployment: Regardless of where enterprises deploy their databases—on-prem, cloud, hybrid cloud, or multi-cloud environments—Intel’s partnerships with CSPs, OEMs, global and national systems integrators, and builders provide a multitude of options for your underlying infrastructure needs.

What Is Data Analytics?

Data analytics uses AI techniques such as machine learning (ML) and deep learning (DL) to transform structured, semi-structured, and unstructured data into business intelligence (BI).

The exponential growth of data to petabytes and beyond continues to challenge businesses, even those with a robust analytics infrastructure. The growing number and types of data sources also lead to more disparate systems, called data silos, for collecting and processing data. To keep up, businesses need to analyze data at faster rates, and data analysts need to continually evolve their skillsets, or they risk leaving insights on the table.

Today, the combination of ML- and DL-based AI inference algorithms, agentic AI, and powerful compute capabilities provide businesses with the foundation to collect, analyze, and extract insights from ever-growing datasets to drive meaningful competitive advantages. AI helps automate key steps in the traditional data analytics workflow, facilitating faster progress and better results at every step. It can also help analysts unlock deeper insights and discern more complex patterns within data than human operators alone.

The Data Pipeline

A typical data pipeline includes three stages. Each stage helps turn raw data into usable insights, predictions, or recommendations.

1. Data Ingestion, Preprocessing, and Exploration

Data enters the pipeline from many sources, including:
 

• Customer interactions
• Social media
• Smart devices and sensors
• Audio, video, images, and other multimedia
   

This data may be structured or unstructured:
 

• Structured data follows a predefined format, such as numerical values in a spreadsheet.
• Unstructured data includes content that does not fit neatly into rows and columns, such as handwritten notes, audio files, images, or video.
   

After collection, the data must be prepared for analysis and modeling. Two common approaches are:
 

• ELT—extract, load, transform: Data is loaded first, then structured as needed.
• ETL—extract, transform, load: Data is cleaned and transformed before it is loaded for use.
   

Once the data is organized, data scientists use statistical, visualization, and AI-based tools to explore it. This step helps reveal patterns, identify relationships, locate outliers, and understand how values are distributed across the dataset.

2. Model Selection and Training

Next, data scientists select the AI model or algorithm best suited to the business objective, available data, and compute capacity. Common techniques include regression, clustering, random forests, and other machine learning approaches.

The selected model is then either:
 

• Trained from scratch using relevant business data and parameters.
• Fine-tuned from an existing foundational model to speed development.
   

Fine-tuning is often faster and simpler than building a model from scratch, helping teams streamline development and accelerate deployment.

3. Production Deployment

In the final stage, the trained model is deployed against new data to generate:
 

• Classifications
• Predictions
• Recommendations
   

These outputs can be made available to users, applications, or other systems. Over time, the model can be monitored, optimized, and refined to improve the quality and relevance of its insights.

Advanced Analytics Solutions and Big Data

Data analytics typically starts with traditional methods that help businesses understand what has already happened and why. More advanced analytics methods build on that foundation to predict what may happen next and recommend what actions to take.

The growth of smart devices and connected systems has dramatically increased the volume, speed, and variety of data businesses collect.

These large datasets, often called big data, are commonly:
 

• Unstructured or semi-structured
• Generated in high volumes
• Produced in real time
• Made up of many different formats and data types
   

This creates the need for powerful compute resources that can process data quickly and turn it into useful business insights.

Common Use Cases

Businesses use advanced analytics solutions to support demanding big data workloads, including:
 

• Fraud detection
• Sentiment analysis
• Predictive maintenance for industrial equipment
   

As AI adoption grows, data analytics continues to evolve. AI-enhanced analytics helps organizations move faster, uncover deeper insights, and make better decisions from increasingly complex datasets.