Modern data architecture on 3 clouds


The importance of data has never been more relevant than it is today. Becoming a data-driven company is an undeniable competitive advantage. Our intention is to discuss solutions to common challenges facing today’s world, allowing businesses to make the most of the opportunities offered by the data age.

Imagine that, sometimes, the system in which your customer makes a purchase is not the same as the one in which you can analyze their entire journey. Lack of access to this knowledge can be a limiting and costly factor these days.

That is why we invite you to review our webinar where we explore the solutions that the modern world offers to overcome these challenges.

Different solutions

We start with Data Warehouses, which are widespread solutions, rooted in the 80s and popularized in the 90s. They stand out for their centralized model, where transactional data is extracted and transformed through batch ETL. Dimensional modeling, with concepts such as Snowflake and Star Outline, is common in this environment. Data Marts are also used to meet the specific needs of different areas of the company.

However, as companies grappled with an increase in data diversity and volume, scalability and latency became challenges. This is the case of data lakes, which gained popularity around 2010. They allow the storage of data of any nature, structured or unstructured, and the transformation is carried out at the time of consumption, with decentralized resources that offer scalability.

Next comes the concept of Data Lake House, which combines the scalability of the Data Lake with the organization of the Data Warehouse. This solves poor data governance and reliability issues, as well as enabling acid transactions and data refresh, which were not possible in the traditional data lake format.

Stack Organization

To implement an effective solution, it is essential to consider the organization of the data stack. This involves data mining, storage strategies, process orchestration, distributed processing, and providing efficient tools to end users. Governance, including data catalog, oversight, quality and access management, also plays a crucial role.

Data layer

Data tiering in Data Lake House begins with the landing zone, where raw data is stored in its native format. Then there are transformation layers, such as the Bronze layer, where data is structured and prepared for upstream processes. The Silver level further improves the quality of the data, preparing it for use in different areas of the company. Additionally, there are additional layers, similar to Data Marts, to meet area-specific needs or create custom metrics.

This layering structure is critical to ensuring data quality and reliability in a Data Lake House environment by providing a single source of truth for the organization. It’s an approach that combines scalability with organization and governance, giving you the best of both worlds.

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