Data Architecture Design

Solid data architecture is crucial to software applications. A well-crafted data model on the right database can speed development, anchor scalable growth, and provide flexibility to adapt to changing business conditions. A well-designed data integration pipeline can merge and cleanse data from many sources, enriching a new application with quality data.

Rooftop supports these goals with a full suite of data architecture design services: data modeling, database selection, and data integration design.

Data Modeling

Rooftop Collective takes a hands-on approach to data modeling. We dive deep, working with you to understand your domain and model the business processes that make you unique. Our work encompasses both normalized, transactional (OLTP) systems and denormalized data warehouses with star schemata (OLAP). For both scenarios, we strive for a design that is elegant, flexible, and transparent.

Database Selection

We additionally offer to guide the selection of your database platform. From proprietary, traditional databases like Oracle to open source options like MySQL and PostgreSQL to emerging NoSQL technologies like Cassandra and CouchDB, we consider the full spectrum of options to find the best fit for your application.

Data Integration

Data Integration is the process of unifying and cleansing data from multiple systems into a single, integrated target system. Often known as ETL (Extract, Transform, and Load), most data integration focuses on creating data warehouses to support Business Intelligence, though scenarios like corporate mergers, inter-organization data exchange, and data migration are also prevalent.

Rooftop Collective works with you to design a comprehensive integration plan, bringing quality data to where you need it, when you need it. We evaluate data sources, analyze complexities hidden in the data, and create an architecture and data flow specific to your needs.

We also help you make one of the most important decisions in any integration project: whether to hand-code your integration or use a pre-built toolkit. This decision depends on many factors, including data volume, complexity, cost, in-house skills, and the type of data sources involved.