Data is today’s most valuable asset. Companies that handle data better are able to move forward and dominate their industries faster. Data feeds decisions, defines strategy, and drives business. So, collecting, managing, and storing data are fundamental steps for successful companies.
Data-driven organisations that incorporate data in their business strategy know storage is not a purely technical issue. Data architecture must respond to the massive influx of data. Businesses need an effective management system to react faster to market needs, act according to data regulations (like GPRD), to analyse and devise their next actions. In sum, to stay competitive in a fast-paced, information-packed environment.
Two main approaches to data architecture are Data Lakes and Data Warehouses.
The definition of Data Lake could be “a massive collection of data stored in its original format”. In Data Lakes, data structuring and processing only happen at the moment of retrieval. Data Lakes are repositories that hold information used for analysis work, from Machine Learning to visualizations. It has only been recently used for Big Data.
The main feature of a Data Lake is centralization. By collecting and storing data of all kinds and at any scale, Data Lakes are a practical and low-cost solution to work with. Data Lakes store raw, unstructured, semi-structured, and structured data without prior processing. Structuring happens only at data retrieval, which offers new possibilities for Data Scientists.
Data Lakes are also very flexible and easy to manage. There are no hindrances to introducing new data types, which makes using different applications easier. And, since scaling is not a problem, it is one of the preferred architectures for Big Data.
This approach is valuable for businesses collecting data in real-time, in which every piece of information is valued equally. Businesses can use Data Lakes to handle the information and put it at the service of Marketing Departments. There is a wealth of user data, fragmented in various parameters - time, geography, preferences, demographics - that can be used to build segmented campaigns at hyper-personalized levels.
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The definition of Data Warehouse is “a data management system designed to store pre-structured data from multiple sources, in large amounts.” Their purpose is to collect and organize data through a specific categorisation process to deliver insights quickly and improve the decision-making process for businesses. This means the use for data needs to be defined before it is loaded to the Warehouse.
Data Warehouses have been in use since the 1980s.
Since there is a predetermined use for data, Data Warehouse architecture requires careful planning: what kind of data will be retrieved, which tools are going to be used in its collection, organisation, processing, and retrieval? The goal is to have a consistent body of data in defined formats, ready to be analysed.
Since it is a management system made up of different technologies and not a repository, it involves a higher level of investment. The return comes in the shape of better quality data that allows for faster decisions.
Data Warehouses pull relevant data regularly from specific applications, whether internal or external, fed by analytics, customers, and partner systems. That data is then formatted and stored to specific allocations in the warehouse, matching the format of already existing items. Then, it is processed to create outputs tailored to the decision-making process of the business.
Format consistency is one of the strong points for Data Warehouses, providing the integrity and quality of information ready to be analyzed and used without processing delays.
Let’s look at Marketing again: knowing which of the company’s products are in demand can help build a strategy purely based on predefined, structured inventory data, possibly highlighting a buying trend that hadn’t been noticed before.
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Designed for Big Data applications, the main difference between these storage management systems is that Data Lakes seem to be more “unmanaged” than Data Warehouses. But that’s not the only one.
There are a few things to consider before opting for one of them:
With Data Lakes, the purpose for data collection is not rigidly defined at intake, allowing for a wider variety of possibilities for its use. It can look disorganized, but it’s the rawness that keeps it interesting (and harder to navigate).
Data Warehouses process data specifically for a predetermined use defined by the organization. Digested data has a unique value that justifies the storage space it’s taking.
So, Data Lakes are great for hoarding data for unplanned use later; Data Warehouses are ideal for compulsive organizing with a definite objective and application.
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Sometimes it shouldn’t be one or another but both. Data Lakes can be the first source for Data Warehouses. Imagine data is water: we can take it out of the Lake and store it in the Warehouse. But, before getting into the Warehouse, it needs to be bottled and labeled to be correctly placed for easy retrieval in the most space-effective way.
Fundamentally, Data Lakes and Data Warehouses are both ways of storing and using large amounts of collected data and applying it to business development. The difference lies in how data is treated and for what purpose. Understanding how and why data is used will help define the best storage and management option for your business.
Content writer and digital media producer with an interest in the symbiotic relationship between tech and society. Books, music, and guitars are a constant.
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