Daxton can develop a pipeline, which can gather and combine data from different data sources like - ERPs, CRMs, RDBMS, flat files, big data lakes and warehouses, cloud data warehouses, streaming data sources, and more.
Seamlessly ingest data into popular data warehouses in real time using the advanced loading step. Either the automated process get data in real-time and ingests into your on-premises or cloud-based data warehouse platforms or we can set a duration for that. The transformation process; helps to standarize the data and in a single format making it easier for Data Visualizationa and helpful for different analytics.
Easily prepare and transform your data with intuitive drag-and-drop tools, complemented by advanced processing capabilities. Create efficient, scalable data pipelines by leveraging functionalities like merging, filtering, sorting, and combining datasets while integrating sophisticated methods such as custom calculations, predictive models, GIS mapping, and dynamic formulas. Build intricate relationships across multiple data sources, selectively capturing only the most relevant data points. Visualize and monitor data transformations in real-time, enabling quick insights and streamlined workflows.
Streamline the entire process from extracting raw data to transforming and loading it into the desired destination systems. Design dependable ETL workflows that execute automatically based on predefined schedules. Get real-time updates upon task completion and set up alerts to promptly identify and address any anomalies detected during the process.
Integrate disparate data sources effortlessly to create a unified virtual data warehouse. Access live data updates from multiple origins in real-time. A semantic layer simplifies querying across sources, enabling users to perform advanced data transformations for comprehensive reporting. This virtual data warehouse structure dramatically reduces query response time, enhancing efficiency and usability.
Enhance the agility of your data pipelines by creating a dedicated staging database. This approach streamlines processes such as data loading, transformation, decoupling, debugging, and failure recovery. By leveraging a scalable staging database, you can boost performance, minimize costs, and significantly reduce processing time, ensuring your workflows are both efficient and resilient.