Starschema Antares iDL™ | Starschema

Starschema Antares iDL™


Starschema Antares iDL™

With its Antares iDL reference architecture and proven implementation methodology, customized to meet customer requirements, Starschema provides scalable data lake solutions to serve a wide variety of enterprise use cases.

Key Benefits

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Increased scalability
02 key benefits icons antares solbrief sschema20 performance performance
Better performance
02 key benefits icons antares solbrief sschema20 maintenance maintenance
Reduced maintenance

Key features

Flexible, scalable architecture

The Antares iDL™ data lake reference architecture provides the flexibility to process both structured data coming from traditional SQL databases and semi-structured/unstructured data ingested from IoT devices, logs, and documents without a fixed format.

Security and compliance by design

The Starschema Antares iDL™ architecture, designed ‘compliance first’, can perform anonymization on the fly for the entire data set and uses strict identity and access management (IAM) that enables administrators to govern who has access to what data. A web-based interface allows data consumers to easily configure their anonymization requirements.

Tools-based optimization

Starschema leverages multiple, proprietary tools to better integrate and optimize data lake components ensuring faster processing of massive data sets.

Data Management

The Antares iDL™ design incorporates data catalog tools like Amazon Glue Catalog that dramatically reduces data discovery times through automating much of the effort in building, maintaining, and running ETL jobs.

Automation, DevOps and Application Lifecycle Management

Built for the enterprise, Starschema Antares iDL™ leverages DevOps best practices. With agile processes built-in, integrated CI/CD tools and a distributed Source Code Management (SCM) system storing configurations for all application building blocks, Starschema Antares iDL™ ensures consistency through efficient distributed metadata management and built-in developer tools that seamlessly integrate with existing ITSM/IDM systems.


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Multi-speed layers

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Unlike data lake designs with a homogenous storage layer, the Antares iDL™ design supports a variety of use cases. Three data lake layers provide differentiated access speed/data velocity, for different purposes. This allows usage- and need-dependent resource utilization while delivering the required speed of access to all customers, creating a uniquely powerful tool for the democratization of data access within the enterprise.

The Starschema difference

Proven onboarding methodology

With standard processes for requirement gathering, customized architecture development, and knowledge transfer, Starschema ensures faster time to value.

Experience in large environments

Fortune 100 companies trust Starschema to keep their data pipelines robust, resilient, and reliable.

Platform agnostic

With a deep knowledge of major cloud platforms such as Microsoft Azure, Google Cloud, and AWS as well as technology segment leaders like HVR, MemSQL, Tableau, and Snowflake, Starschema can customize data lakes to meet customer requirements.

Tools-based approach

Starschema deploys standard and proprietary frameworks, methodologies, and tools to provide effective, accurate, and repeatable solutions and services.

Complete data lifecycle management

From ingestion to consumption, our teams of database administrators, data engineers, ETL developers, application developers, and data visualization experts provide a seamless solution for your complete data pipeline.

Flexible service models

Starschema offers platform design and management and DataOps for entire, multi-vendor data pipelines or specific components.

Ask the Expert

Anjan Banerjee 1
Lead Architect

Anjan Banerjee, a lead architect at Starschema, works with cloud technologies and data warehouses. He has extensive experience in building data orchestration pipelines, designing multiple cloud-native solutions, and solving business-critical problems for many multinational companies. He applies the concept of infrastructure as code as a means to increase the speed, consistency, and accuracy of cloud deployments. Anjan holds frequent training sessions on data munging and various self-service analytics/transformation tools like Talend and Alteryx.

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