A well-designed data architecture can help your organisation to get actionable insights from your data and help you make data-driven decisions.
In the past, most data architectures were less complicated than they are now. They mostly involved structured data from transaction processing systems that was stored in relational databases. Analytics environments consisted of a data warehouse, sometimes with smaller data marts built for individual business units and an operational data store as a staging area. The transaction data was processed for analysis in batch jobs, using traditional extract, transform and load (ETL) processes for data integration.
The adoption of big data technologies in businesses has added unstructured and semistructured forms of data to many architectures. This has led to the deployment of data lakes, which often store raw data in its native format instead of filtering and transforming it for analysis upfront -- a big change from the data warehousing process. The new approach is driving wider use of ELT data integration, an alternative to ETL that inverts the load and transform steps.
The increased use of stream processing systems has also brought real-time data into more data architectures. Many architectures now support artificial intelligence and machine learning applications, too, in addition to the basic BI and reporting driven by data warehouses. The shift to cloud-based systems further adds to the complexity of data architectures.
Our data architecture solutions save time and costs, so that your engineers and scientists can focus on improving lives.
Our data architecture prioritises
Principles of our data architecture
We focus on building a balanced, performant & secure data architecture for your organisation. The 6 foundational principles that we follow are,
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