Data Analytics Tools: Streamlining the Path to Insight

Intel® technologies—including tools, frameworks, and libraries—accelerate and optimize analytics solutions.

Intel Drives Value from Data Analytics Tools

  • Intel’s ecosystem of partners spans some of the largest analytics platforms in the world, making it easy to optimize performance across a wide range of analytics tools.

  • Intel® Software Libraries, frameworks, and other tools streamline the development of analytics applications, including augmented analytics and real-time analytics.1

  • Intel® oneAPI offers flexibility to implement analytics solutions across multiple hardware architectures and programming languages.1



As data volumes swell, analytics and data science have become vital to making sense of processes, customers, and systems. And powering new trends in analytics are tools designed to make better predictions, using bigger data sets. Organizations can choose from a wide range of data analytics tools, from full-featured analytics platforms to specialty tools and libraries.

Partner Tools

Intel’s analytics roots run deep. Our partnerships center on optimization and efficiency, with a goal of making it easier and faster for today’s organizations to implement next-generation analytics strategies—from predictive and real-time analytics to augmented analytics.


For more than a decade, Intel and SAP have collaborated to deliver performance and scalability for a wide range of analytics. Intel's internal open data platform uses SAP HANA* to analyze data faster for its supply chain data analytics system.

Intel and SAP are working together to optimize SAP’s portfolio of data analysis software—from SAP HANA* and SAP Analytics to intelligent analytics from SAP Leonardo—for Intel® Xeon® Scalable processors and Intel® Optane™ persistent memory. This helps provide more memory and faster startup times for large analytics workloads.


As a leader in database management systems, Oracle enables mixed workloads for online transaction processing as well as real-time streaming analytics from a wide range of data sources. Intel’s joint efforts with Oracle, spanning over 20 years, have contributed to Oracle Database's enterprise-scale capabilities for performance and scalability, especially in the area of distributed real-time analytics. Oracle Database uses the mesh architecture of Intel® Xeon® Scalable processors to improve performance for analytics workloads.

Microsoft* SQL Server

Microsoft SQL Server offers in-database advanced analytics capabilities for real-time operational analytics and predictive analytics. Microsoft and Intel have worked together to ensure that organizations using Microsoft SQL Server can take full advantage of these capabilities. Reference designs with verified, optimized hardware and software stacks powered by Intel® Xeon® Scalable processors ensure infrastructure readiness for a range of business types and sizes.


With solutions for a full range of analytics workloads, SAS offers a robust analytics platform. SAS and Intel have joined forces to offer users faster performance and smarter decision-making across the enterprise.

The partnership works to optimize SAS in-memory analytics using Intel® Xeon® Scalable processors and Intel® Optane™ Solid State Drives. Optimized performance makes it possible for enterprises to employ a full range of analytics strategies, from data mining and forecasting to predictive and descriptive modeling.


Cloudera offers enterprise big data analytics and data management solutions powered by Apache Hadoop. Optimized for Intel®-based data center technologies, Cloudera works to bring the power of Hadoop’s open source technology to enterprise customers.

The Cloudera distribution of Hadoop (CDH) augments business intelligence, with predictive analytics that enable data-driven forecasting. Intel® hardware and software optimizations make analyzing data with Hadoop easier and faster for a wide range of applications and use cases.

Intel’s partnerships center on optimization and efficiency, with a goal of making it easier and faster for today’s organizations to implement next-generation analytics strategies.

Specialty Tools

In addition to data analytics platform technology, many organizations use specialized data analytics tools that are highly optimized to perform specific functions.

Visualization Tools

Reporting is a critical piece of the analytics puzzle. Data visualization tools streamline visual and graphical reporting from multiple data sources, making it easier to create charts, graphs, and other representations of analytical insights for business intelligence.

  • Tableau: Tableau offers a suite of data visualization tools, allowing users to quickly generate visualizations using big data. Tableau offers real-time visualization, as well as integration with languages like Python and R, as well as database management software.
  • Qlik: Qlik’s range of business intelligence solutions includes Qlikview, its advanced data visualization software, and Qlik Sense, a next-generation analytics platform using augmented intelligence.

Log Analysis

Analytics in the IT department often centers on log analysis: Understanding logs to inform data-driven decision-making. Monitoring logs with specialized data analysis software can make it easier to understand how infrastructure, systems, and applications are functioning together—and where there is room for improvement.

  • Splunk: Capable of analyzing large volumes of logs in real time, Splunk delivers integrated data analytics dashboards and troubleshooting tools that make it easier for IT departments to detect and resolve technology issues.

Machine Learning

Some of the most exciting advances in analytics and data science technology involve artificial intelligence (AI) and machine learning. Augmented analytics using automated AI engines simplify data modeling to produce insights and data-driven predictions automatically.

  • H2O: Equipped with a “driverless” AI platform, H2O works to make it easier for a wider range of workers to augment analytics with machine learning and AI. Offering Spark, R, and Python integrations, H2O is designed to work on existing infrastructure.
  • DataRobot: Designed for the enterprise, the DataRobot AI platform streamlines the creation of end-to-end AI and machine learning applications.

Intel® AI Frameworks and Tools

The most advanced analytics strategies today are based on AI technologies, including deep learning and neural networks. Intel offers multiple optimizations for popular deep learning frameworks to accelerate AI model development and training.

Intel® Optimizations for Deep Learning Frameworks

Intel works with a number of popular deep learning frameworks that provide data scientists, developers, and researchers a high-level programming language to architect, train, and validate deep neural networks.

  • The Intel® Optimization for TensorFlow* provides optimization of the popular open source TensorFlow deep learning framework for Intel® Xeon® Scalable processors. This helps data scientists and high-power computing (HPC) practitioners solve new business and research challenges.
  • The Intel® Optimization for Caffe* improves the performance of the popular Caffe framework on Intel® processors. Caffe is a deep learning framework that can be run on HPC clusters to enable AI applications.
  • The Apache MXNet framework improves computer vision models with inference optimization and enhanced quantization performance.

Intel® Optimizations for Neural Networks2

  • Intel® Software Optimizations for PyTorch allow users to take advantage of Intel® hardware and Intel® Deep Learning Boost with the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN).
  • Built using Intel® Math Kernel Library for Deep Neural Networks, PaddlePaddle provides fast performance on Intel® Xeon® Scalable processors as well as a large collection of tools to help AI developers.

Intel® Optimizations for Big Data Frameworks

  • Intel® software contributions to big data and analytics frameworks help applications run fast and easily on HPC systems. Intel® optimizations for big data analytics tools and techniques support popular frameworks such as Apache Hadoop and Apache Spark.
  • BigDL, an open source, distributed, and scalable deep learning framework for Apache Spark, provides native support for deep learning functionalities with Intel® Xeon® processor performance.

Intel® Software Libraries2

With software libraries, developers can access the compatible tools they need to create applications fast. Intel® libraries are designed to help developers of analytics solutions and applications with state-of-the-art compilers, performance libraries, parallel models, and high-performance Python solutions:

Unified Programming with Intel® oneAPI2

Analytics workloads are becoming more diverse. With the unified programming interface offered by Intel® oneAPI, organizations can support multiple hardware configurations and architectures without the need for reprogramming.

Intel® oneAPI products deliver tools to deploy applications and solutions across SVMS architectures. Its complementary toolkits, including a base kit and specialty add-ons, simplify programming and help developers improve efficiency and innovation across a range of analytics applications and workloads.