Azure Data Lake Analytics vs SAS Enterprise Miner comparison

Azure Data Lake Analytics is a cloud-based on-demand analytics job service that simplifies big data. It allows users to focus on running jobs rather than on the complexities involved in distributed computing.

Azure Data Lake Analytics provides a scalable and cost-efficient environment for processing big data. It enables users to develop and run massively parallel data transformation and processing programs in U-SQL, R, Python, and .NET over petabytes of data. The system's serverless architecture means users pay only for the processing power they use, thus avoiding significant upfront infrastructure costs.

What are the key features of Azure Data Lake Analytics?

• Serverless Design: Eliminates the need for infrastructure setup and configuration.
• Scalability: Handles large-scale data processing effortlessly with high elasticity.
• Multi-language Support: Enables scripting in U-SQL, R, Python, and .NET.
• Cost Management: Pay-as-you-go pricing for the computational power used.
• Integration: Seamlessly integrates with other Azure services and tools.

What are the benefits of using Azure Data Lake Analytics?

• Efficiency: Streamlines big data processing without managing infrastructure.
• Cost-Effective: Reduces expenses with a pay-per-job model.
• Flexibility: Supports multiple programming languages for diverse data transformations.
• Interoperability: Easily works with other Azure offerings for enhanced data solutions.

Azure Data Lake Analytics is implemented across industries like finance, healthcare, and retail. In finance, it is used for fraud detection and risk management by processing large datasets efficiently. Healthcare organizations utilize it for patient data analytics and research purposes. In retail, it assists in customer behavior analysis and inventory optimization, leveraging its ability to handle substantial data volumes and integrate with existing systems.

SAS Enterprise Miner is a solution to create accurate predictive and descriptive models on large volumes of data across different sources in the organization. SAS Enterprise Miner offers many features and functionalities for the business analysts to model their data. Some of the business applications are for detecting fraud, minimizing risk, resource demands, reducing asset downtime, campaigns and reduce customer attrition.