Data Processing and Data Engineering Services

Data Architecture

Data architecture services refer to creating a system that defines how data is collected, stored, and managed within a business. Regardless of whether you seek cloud-based or on-premise data warehousing, we can design a scalable data architecture that will form the backbone of your data strategy. With us, you can ensure that your data architecture is tailored to your business goals.

Cloud-based

In recent years, cloud-based data warehouses have been growing in popularity due to their scalability and flexibility. Choosing a cloud-based data architecture allows you to focus on efficacy and cost-efficiency of the system without worrying about having enough space for large quantities of data. You can adjust the storage capacity if the demand changes.

On-premise

Despite cloud-based data architecture becoming increasingly popular, some businesses rely on on-premise solutions that offer more infrastructure control.  For companies in need of traditional data architecture, we can design and implement data warehousing solutions using relational databases and data marts.

Big Data Architecture

With big data architecture, your business can manage the collection, processing, and analysis of data larger or more complex than the capabilities of traditional database systems. Use our big data architecture services to efficiently deal with massive data sets. 

Cloud Native

Cloud native is an approach for developing and operating scalable applications that are specifically designed to leverage cloud-based services and delivery models. By utilising cloud-native technologies, we can help you build and deploy applications that are fully optimised for cloud environments. Keep in mind that cloud-native application development might require adding DevOps (Development and operations) to the delivery process. DevOps practices that employ advanced technologies like blockchain, artificial intelligence, and machine learning can help enhance data processing.

Modern Data Stack

The modern data stack is a collection of tools and cloud data technologies used to gather, process, and store data. The modern data stack is said to be more scalable, flexible, and efficient than a legacy data stack. This is because it relies on cloud computing, while a classic data stack stores data on servers. Our experts can help you adopt a modern data stack, enabling you to mitigate IT bottlenecks and make data-driven decisions faster.

Classic Data Stack

For companies that prefer a traditional approach, we can adopt a classic data stack. This includes designing and implementing data warehousing solutions using relational databases, data marts, and ETL/ELT pipelines.

ETL and ELT

Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) are two common data integration approaches. Our experts employ ETL/ETL processes to extract raw data from various sources, turn it into usable format, and load it into your data warehouse, ensuring the data is ready for analysis. At DAC.digital, we can help you choose the right ETL or ELT strategy and implement robust data pipelines.

From an initial consultation to implementation and beyond, we offer end-to-end support for all your data processing and engineering requirements.

When you approach us with a specific need or data-related challenge, we will carefully analyse your situation. For example, we will take time to understand what data you have and whether you need real-time or batch data processing.

Or, we will determine the necessary level of data accuracy. Based on an in-depth analysis, we will suggest the best solution.

Then, we will begin the implementation phase that involves data modelling, a stage during which our experts create a visual representation of data flows and connections between data points.

During the implementation, we will also upload data to the data warehouse. 

At the end of the process, you can expect us to deliver a complete solution that you can maintain yourself with ease.