Leverage Astera’s feature-rich dimensional modeler and visually create dimensional models from source data. Reverse engineer source schemas and create or import a data model directly for modification or enhancement. Use drag-and-drop operations to merge tables and denormalize as per business requirements. Mark tables as either Fact or Dimension, and easily edit properties.
With virtual data models, Astera creates a data virtualization layer between the user and the technical hassles of accessing data (where the data is located, method of access, login credentials, APIs, etc.). New source feeds (unstructured reports, legacy data, EDI formats, clickstream data, flat files, etc.) needed in the data warehouse to meet business objectives can be dropped directly onto the virtual model.
Change Data Capture
DWAccelerator incorporates Change Data Capture (CDC) technology for real-time data warehousing. Select CDC as a high-level data loading strategy and choose between trigger tables, field audits, diff capture, and timestamp for ongoing integration before or during the data warehousing process. A custom data loading strategy can also be created by combining elements of CDC and batch load.
Push-down optimization gives users the option to reorder the ETL process to prioritize loading over transformation, or ELT. This reordering produces the same results as traditional ETL; however, the re-prioritization cuts time spent in transformations, especially when loading fact and dimension tables with hundreds of thousands of rows.
Data Quality and Profiling
With a built-in data quality module, DWAccelerator can profile, cleanse, and validate data to ensure readiness for the data warehouse without the user having to change applications—everything is done in a single platform and as part of a single job. For backend logging to understand errors, use the built-in field profiler and record-level log, or specify business rules.
Intelligent Mapping Techniques
The code-free mapping screen offers the perfect blend of automation and flexibility. Users can either let DWAccelerator use fuzzy logic to automatically map sources entities to destination, or take control with direct mapping and writing business expressions. The modern, visual approach cuts down manual effort in mapping entities while providing complete control.
With the data models created and data loading configurations defined, DWAccelerator automatically generates integration flows and associated ETL code with one click using dimensional metadata. Schedule workflows using the built-in job scheduler and seamlessly populate target systems to keep business data as current as it needs to be. Workflow status can be monitored closely until completion.
From establishing connections to source data, to visualizing it in data visualization software for business insights, DWAccelerator delivers a complete framework to organizations looking to build a self-contained business intelligence environment. The platform supports native integration to industry-leading analytics providers: Tableau, Microsoft PowerBI, and QlikView.
How It Works