June 2023 Summaries

CData Virtuality provides easy access to Amazon's Advertising Data stream for Sellers and Vendors, offering a comprehensive view of campaign and ad group performance, product advertising daily performance, keyword advertising daily performance, product & keyword recommendations, advertising invoice data, brand metrics data, Amazon attribution data, and audit logs. This data can be analyzed using various approaches such as descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis to identify trends, optimize budget, improve targeting, and monitor key indicators. Key metrics include impressions, clicks, click-through rate, conversion rate, advertising cost of sales, and return on advertising spend. By leveraging these insights, Sellers can optimize their advertising strategies, make more informed decisions, and drive better performance and higher sales. CData Virtuality integrates with Amazon data to help Sellers monitor performance metrics, identify trends and patterns, enhance competitive analysis, and automate reporting processes.

This is the first in a series of blog posts where we take a look at our driver technology and how we build our products, with a focus on building connectors to 300+ data sources across various technologies such as ODBC, JDBC, ADO.NET, and more. We examine different perspectives used to understand the level of effort involved in building a driver for a new potential data source, including protocol considerations, serialization formats, data model considerations, metadata discovery, authentication models, query capabilities, and the complexities that arise from these aspects, which can impact the technical effort required to add support for a new data source. Our drivers expose a relational model, but also support non-relational data sources such as document databases, key/value stores, graph databases, hierarchical stores, analytical stores, and raw stores, each with unique challenges. We have invested heavily in metadata caching and improving performance in this area, and our product architecture and technology enable us to deliver over 300+ data sources across every major data access technology.

The world is generating a vast amount of data at an unprecedented rate, with 90% of it being created in just two years, and companies are turning to Big Data storage solutions to manage this influx of information. NoSQL databases, which offer better scalability and performance than relational databases, have become increasingly popular for storing and managing large amounts of unstructured or partially structured data. However, connecting to these databases from analytics tools such as Power BI, Tableau, or Excel can be challenging due to their lack of schema and structure. To address this challenge, CData offers connectivity solutions that translate NoSQL data into a relational view, making it possible for business users to query and report on the data in real-time.

The CData Power BI Connectors enable live connectivity to data in Power BI from a wide range of sources, including MongoDB, with features such as direct query support and built-in modeling and data flattening capabilities, allowing users to access NoSQL databases as if they were relational. The connectors provide fast and flexible support for NoSQL data integration, enabling users to interact with NoSQL databases using SQL-92 queries. With the CData Power BI Connector, users can bypass traditional data replication or virtualization steps associated with working on SaaS and NoSQL data and jump straight into building visualizations and reports in real-time. The connector allows users to create interactive dashboards that provide insights into business and sales health, using features such as custom visualization libraries, easy setup, and customizable dashboards.

Microsoft Build 2023 Highlights: A Data Driven Perspective` Microsoft's annual developer conference, Microsoft Build, was held in Seattle, where several exciting announcements were made, particularly for data-driven organizations. The era of AI in the enterprise has become a reality, with Microsoft incorporating core GPT technology into their Copilot System, which is now enabling users to write their own Copilots. This allows organizations to extend existing Copilots and access domain-specific knowledge within their organization's data. Additionally, Microsoft announced their implementation of the modern data fabric paradigm at Build, called Microsoft Fabric, a consolidated data platform for AI and analytics. The new platform enables real-time data access, which is crucial for leveraging generative AI and data manipulation tools. This development presents exciting possibilities for organizations to build innovative applications with the technologies, including seamless integration with Microsoft Fabric components and development environments.