Systems | Development | Analytics | API | Testing

SAP’s business practices impact how enterprises access and integrate their data. This is the first blog in a multi-part series exploring how to access and best leverage your SAP data.

Business intelligence platforms analyze vast amounts of data, requiring visualization engines that balance performance, flexibility, and ease of use. Traditional charting libraries treat each chart type as a distinct entity, requiring separate logic and code for each. This approach leads to code duplication, limited reusability, and reduced maintainability. Additionally, it’s cumbersome to effectively layer or combine visual elements due to these libraries’ rigid composability.

Whether you're exploring trends, uncovering outliers, or designing presentations that tell a compelling story, your insights should be connected and pervasive. It's no longer enough to simply see your data. You need a fluid, intuitive experience that empowers you to find next-level insights faster. We’re thrilled to announce ThoughtSpot’s new-and-improved visualization platform, built with integrated intelligence to empower limitless interactivity.

If your BI and analytics tool isn’t powered by Agentic RAG AI, you’re missing out on advanced AI capabilities that enhance efficiency and visibility into data. Whereas agentic AI can work autonomously and without human intervention, RAGI AI combines the best information retrieval methods available with the power of AI. The result is deep knowledge of organizational data that improves over time.

Partition skew ratio is a critical metric for measuring data distribution imbalance across partitions in ETL (Extract, Transform, Load) pipelines. It represents the ratio of the maximum bytes scanned per partition to the average bytes scanned per partition. When this ratio is high, it indicates significant partition skew challenges in data engineering workflows, which can drastically reduce performance.

Schema-drift incidents create significant challenges for data engineers managing ETL pipelines. Tracking these incidents helps organizations maintain data quality and prevent downstream failures when source data structures unexpectedly change.

In today's data-driven world, organizations must efficiently manage and transform their data to gain valuable insights. Data orchestration and ETL (Extract, Transform, Load) are two popular approaches to data management, each with distinct capabilities and purposes. Data orchestration manages the entire workflow of data processes across an enterprise, while ETL focuses specifically on extracting data from sources, transforming it, and loading it into destination systems.