Take control of your data – and free up IT – with self-service data preparation.
Manage data – no special skills needed.
Perform data integration, data quality and data preparation tasks yourself, without having to write code or ask for specialized help. SAS Data Loader for Hadoop bridges the skills gap, giving all users access to their data regardless of technical ability.
Boost scalability and performance.
Business users find it easy to use. Data scientists and SAS coders like its speed, efficiency and agility. A code accelerator harnesses the power of Hadoop, and data quality functions run in memory on Spark for better performance. And by minimizing data movement, you increase your data's security.
Ensure big data quality.
Take control of the data within data lake environments. SAS Data Loader for Hadoop allows you to profile data to understand its overall quality. Then you can standardize, parse, match and perform other core data quality functions, resulting in quality data for business needs inside of the data lakes.
Speed data management processes with Spark.
Data quality functions run in memory in Apache Spark for improved performance. Matching and best record creation enables master data management for big data. In addition, you can read and write to Spark data sets as needed.
Load your data into or out of Hadoop and data lakes. Prep it so it's ready for reports, visualizations or advanced analytics – all inside the data lakes. And do it all yourself, quickly and easily.
Intuitive user interface
Makes it easy to access, transform and manage data stored in Hadoop or data lakes with a web-based interface that reduces training requirements.
Purpose-built to load data to/from Hadoop
Built from the ground up to manage big data on Hadoop or in data lakes; not repurposed from existing IT-focused tools.
Chaining & scheduling directives
Lets you group multiple directives to run simultaneously or one after the other. Schedule and automate directives using the exposed Public API.
Collaboration & security
Enables you to share and secure directives. Call them from SAS Data Integration Studio, uniting technical and nontechnical user activities.
Big data quality & profiling
Includes built-in directives – casing, gender and pattern analysis, field extraction, match-merge and cluster-survive. Profiling runs in-parallel on the Hadoop cluster for better performance.
In-memory analytics server
Lets you load data in memory to prepare it for high-performance reporting, visualization or analytics.
In-cluster code & data quality execution
Executes analytics and data quality processing within Hadoop for fast, budget-friendly results. Minimizes data movement for increased scalability, governance and performance.
Big data integration
Imports data from CSV, SAS/ACCESS® libraries, cloud sources, relational databases and other delimited files into Hadoop. Runs HiveQL and Impala SQL.
Explore More on SAS® Data Loader for Hadoop and Beyond
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- Технический документ Data Quality Challenges and PrioritiesFind out how organizations are addressing their most pressing data quality issues, discover the top 10 priorities for data quality solutions, and learn the best ways to engage and empower business users to improve data quality.
- Технический документ Using Next-Generation Advanced Analytics to Harness Big DataDiscover why Heavy Reading recommends using advanced analytics from proven vendors to obtain real-time intelligence from all your big data.
- Технический документ SAS and the Hadoop EcosystemA vendor profile from The Bloor Group that refers to the full report: Making Sense of Hadoop and its Ecosystem.
- Технический документ Recommendation SystemsIn this paper, Wayne Thompson, PhD, Manager of Data Sciences Technologies at SAS answers common questions about the types of recommendation systems used today, who uses them and why, and the advantages of using them for businesses today. He also delves into how the underlying technology typically works, with a special focus on SAS capabilities and benefits.
- Технический документ A Non-Geek’s Big Data PlaybookThis paper examines how a non-geek yet technically savvy business professional can understand how to use Hadoop - and how it will impact enterprise data environments for years to come. The paper serves as a playbook that demonstrates six common “plays” that illustrate how Apache Hadoop can support and extend the enterprise data warehouse (EDW) ecosystem.
- Технический документ Eight Considerations for Utilizing Big Data Analytics with HadoopThis paper focuses on eight considerations when it comes to applying big data analytics to extract value from Hadoop. Readers will learn about the importance of in-memory analytics, how to optimize the data preparation processes and the skill sets needed to derive benefits from Hadoop.
- Технический документ Data Integration Déjà Vu: Big Data Reinvigorates DIDiscover why the latest evolution of data integration delivers more value from big data.
- Технический документ Understanding Big Data Quality for Maximum Information UsabilityDiscover why data quality and data governance are so important to large-scale analytics. Learn how to balance governance with usability so you can come up with a strategic plan for managing big data.
- Технический документ Hadoop for the Enterprise: Making Data Management Massively Scalable, Agile, Feature-Rich, and Cost-EffectiveThis TDWI report accelerates users’ understanding of new products, technologies and best practices that have emerged around Hadoop. It will also help readers connect available options to use cases, with a focus on mainstream enterprise uses, while respecting proven IT practices and delivering maximum business value.
- Технический документ Making Sense of Hadoop and its EcosystemAn Investigation into the Evolution and Deployment of Hadoop
- Технический документ Managing the Analytics Life Cycle for Decisions at ScaleLet the SAS Analytics Life Cycle guide you through the iterative process of going from raw data to predictive modeling to automated decisions, faster. This paper tells you how.
- Технический документ Improving Data Preparation for Business AnalyticsThis TDWI Best Practices Report discusses the latest data preparation processes, self-service options and how to effectively integrate data prep with analytics and BI solutions.
- Технический документ Best Practices for Hadoop: A Guide From SAS CustomersEase your transition to Hadoop by following these tried-and-true best practices from SAS customers.
- Технический документ Self-Service Big Data Preparation in the Age of Hadoop: A Conversation with SASLearn how SAS Data Loader for Hadoop enables business users – along with data scientists and IT – to access, profile, transform and cleanse Hadoop data. The best part is, it requires minimal training and no need for coding.
- Технический документ Drive Next-Generation Customer Experiences with Real-Time Connected Product and Service Analytics SAS, Hortonworks and Intel can help you embrace technologies and processes to anticipate a wide range of customer needs, providing the foundation for next-generation customer experiences.
- Interview The scoop on HadoopCloudera co-founder Mike Olson talks trends, changes and outlines the formula for success with Hadoop.
Check out these products and solutions related to SAS Data Loader for Hadoop.
- SAS/ACCESS® SoftwareСчитывайте, записывайте и обновляйте данные независимо от того, в какой базе данных или на какой платформе они хранятся.
- SAS® In-Database TechnologiesСократите время, необходимое для управления ключевыми процессами качества данных и анализа. Выполняйте эти задачи в базе данных.