Datawarehouse Modernization

At ElogicSquare, we specialize in data warehouse modernization, helping organizations transform their traditional data warehousing approaches into modern, scalable, and agile solutions. Our services encompass the entire data warehouse modernization journey, from assessment and strategy to implementation and optimization.

We begin by conducting a thorough assessment of your existing data warehouse infrastructure, processes, and technology stack. Our experts analyze the current pain points, scalability limitations, and performance bottlenecks to identify opportunities for improvement. Based on this assessment, we collaborate with you to develop a data warehouse modernization strategy aligned with your business goals.

One key aspect of data warehouse modernization is adopting cloud-based data platforms. We guide you in migrating your data warehouse to leading cloud providers such as AWS, Azure, or Google Cloud, leveraging their scalable and elastic infrastructure. This shift to the cloud enables you to benefit from increased storage capacity, improved performance, and reduced infrastructure costs.

Traditional data warehouses were built with online transaction processing (OLTP)-centric technologies and architectures that are old technology. These data warehouses were never designed to handle the volume, variety and velocity of today’s data-centric application. ElogicSquare’s current technology for Big Data allows organizations to dramatically improve return on investment (ROI) from their existing data warehouse environment.

Costs-Users seek to save money in some areas (storage, CPUs, upgrades, admin) so they can invest in others (new data platforms, analytic tools, and developing new solutions).

Speed-User need the data warehouse and related systems to operate faster because speed contributes to scale, supports agile development and discovery analytics, and brings analytics closer to real-time business operations.

Advanced analytics-Many users have invested heavily in reporting and OLAP, but now they need to invest in advanced forms of analytics to leverage big data, find new customer segments, and stay competitive.

Scale- Users need to accommodate growing numbers of concurrent users, reports, analyses, and data structures.