Technologies That Assist Businesses Improving Data Integration Performance
Companies are evaluating and adopting various contributing technologies to improve their data integration performance.
FREMONT, CA: Data integration is becoming increasingly important in companies' ability to win, serve, and retain customers. Because of the growing volume of data, compliance pressure, the need for real-time information, increased data complexity, and data distribution across hybrid and multiple clouds, enterprises face increasing data integration challenges. In addition, business users want quick access to reliable, real-time data to help them make better business decisions.
Data as a Service (DaaS)
Data as a service (DaaS) – known as data as a product (DaaP) – provides a common data access layer via APIs, SQL, ODBC/JDBC, and other protocols, leveraging data platforms such as data virtualization, data mesh, integration platform as a service (iPaaS), and others. This new advanced data integration technology generation focuses on a common data access layer to accelerate various use cases.
A data mesh can optimize mixed workloads by matching processing engines, and data flows to the appropriate use cases. In addition, it connects to the event-driven architecture, allowing it to support edge use cases.
It allows developers, data engineers, and architects to be more productive and accelerate various business use cases.
A knowledge graph employs graph engines to facilitate complex data connections and integration. It aids in creating recommendation engines, the cleansing of data, the performance of predictive analytics, and the rapid connection of data. As a result, developers, data engineers, and data architects can work quickly through messy, unrelated data to speed up application development and uncover new business insights.
It uses a graph data model to store, process, and integrate connected data, creating a knowledge base to handle complex questions and provide modern insights.
Accelerator for Queries
The query accelerator market has gained traction to assist developers and data engineers quickly optimizing queries and moving to compute closer to data, thereby minimizing data movement. This technology is useful when information is stored in data lakes, object stores, or complex data warehouses where tuning queries are not always simple.
A query accelerator assists businesses in accelerating analytics and data searches by utilizing a simplified query that can be executed by business analysts, business users, and IT organizations.