A changing data
architecture: new horizons
for companies
From traditional warehouses to flexible data lakes:
A journey through the data landscape
In an era where products are no longer just manufactured and sold, but customers are looking for unique experiences, the importance of personalized marketing strategies is shifting into the spotlight. Companies are faced with the challenge of not only using innovative platforms for marketing automation, but also developing their own customized solutions and strategies to strengthen customer loyalty.
In this blog post, we therefore dive into the world of data architecture, from traditional data warehouses to flexible data lakes. Data architecture plays a key role in optimizing marketing strategies, personalizing customer experiences and meeting the demands of the modern, data-driven business world.
From the data warehouse to the evolution of data architecture
The history of data processing began with inventory lists and intuition. In the late 1980s, the concept of the data warehouse was introduced, originally for the creation of static reports. Traditional data architectures were called into question with the advent of big data and the associated challenges.
A data warehouse serves as a central hub for different data sources and contains the standardized structure of company data. The life cycle of data includes extraction, transformation, loading, storage, metadata management and finally business intelligence and reporting.
What challenges does a data warehouse solve?
A data warehouse offers numerous advantages that have a positive impact on a company’s data management, analysis and decision-making. From reducing the workload of the production system to integrating multiple data sources, the benefits are many.
However, despite these advances, data warehouses have their limits, especially when it comes to processing non-tabular data.
The rise of the data lake
This is where the data lake comes into play, a central platform that enables companies to store and manage large volumes of structured, semi-structured and unstructured data. Unlike the data warehouse, the data lake stores data in its raw, native format without a predefined schema. This offers the flexibility to collect and store data from different sources at low cost.
In a classic data lake, the data is organized in different levels: from the raw, unprocessed data at the bronze level, to the processed data at the silver level, to the data specially optimized for specific applications at the gold level.
The introduction of data lakes has clearly improved many aspects of business efficiency. But the evolution continues and companies face the challenge of choosing the best solutions for their specific challenges. Sometimes this requires a hybrid approach that includes both data lakes and data warehouses.
A hybrid solution can be beneficial for companies under the following conditions:
- Diverse data sources: Organizations with a variety of data sources and types, including structured, unstructured and semi-structured data, can benefit from a hybrid solution. Data lakes provide the flexibility to store large and diverse data sets in their raw format, while data warehouses provide optimized structures for analyses and inquiries.
- Processing speed: In situations where fast query and analysis results are required, data warehouses can be more efficient due to their structured and indexed database structures. Data lakes, on the other hand, are good for cost-effective storage of large amounts of data, but may not be as performant for real-time analytics.
- Scalability: A hybrid solution allows companies to scale their infrastructure as and when required. They can take advantage of the horizontal scalability of data lakes to process large amounts of data while utilizing the vertical scalability of data warehouses to perform performance-intensive analytics.
- Cost management: By combining data lakes and data warehouses, companies can manage and store their data efficiently. They can reserve expensive storage capacity in data warehouses for frequently accessed, business-relevant data, while less frequently used or unstructured data is stored cost-effectively in the data lake.
- Regulatory requirements: Many industries have specific compliance requirements regarding the storage and processing of data. A hybrid solution enables companies to organize and store data in accordance with the relevant regulations.
The choice of a hybrid data architecture allows you to combine the advantages of data lakes and data warehouses to create a balanced and customized solution for your individual requirements.
Data Lakes & Co. at 4brands Reply
At 4brands Reply, we understand the complexity and potential of data lakes. We not only offer comprehensive consulting and system integration, we also provide support throughout your entire transformation process – from designing the customer experience to implementing your IT strategy.
Would you like to find out more about how a data lake can support your individual transformation process? Get in touch with us. We’re here to guide you on your journey and ensure you realize the full potential of your data.