"Big data," "lots of data," "giant data." Whatever you prefer. These may be translations for Big Data, but the real meaning to describe it goes beyond just a bunch of information. After all, there are other parameters involved in the process. The sources that are generating this data, the different formats, and the speed of generation are all factors that, combined, also define Big Data.

The data alone is of little relevance

The key differentiator is in the way companies treat and analyze it. Today, through analysis and other methods, Big Data is used to generate intelligence and impact areas such as production, customer experience, marketing, and sales, among others.

In short, this area of knowledge is essential for the strategic management and growth of a business. This way, it is possible to identify trends, anticipate changes, better understand consumer behavior, and evaluate their perception of your brand. 

No wonder that 91.9% of 85 large companies are increasing their investments in big data and related AI initiatives, while 96% reported successful outcomes from such projects, according to a NewVantage Partners research published in January 2021.

Also, a recent market study shows Data Analytics Market is expected to grow at a Compound Annual Growth Rate (CAGR) of 30.08% from 2020 to 2023, which would be equal to $77.6 billion.

Already in 2019, the global spending on Big Data analytics was more than $180 billion. In other words, the data revolution is already a reality for many companies. However, for other businesses, it’s been a struggle to maximize the potential of their big data environment. Only 24% of the NVP survey said they had already created a data-driven organization.

So not only Big Data has a huge relevance, but it also presents a challenge for companies nowadays.So how about we start with the basics and the main topic of this article: Data Lake or Data Warehouse?

Both Data Lake and Data Warehouse are data repositories, but they are not interchangeable terms: the similarities between them stop there. These two are part of a group of factors that make Big Data productive for companies. 

Large enterprises often generate high amounts of structured data. And this needs to be stored, kept secure, be under legislation principles, and be able to be manipulated with the help of Data WareHouse (DW) and Data Lake (DL).

Let’s go for definitions...

In short, DW is a repository of data, treated with absolute levels of security to ensure the integrity of the business and its operation. This is, today, the basis for the application of well-known processes in the market, such as Business Intelligence (BI), which further refines the data collected from DW and uses them for business intelligence routines.

With this technology, over time, a robust historical record can be created for data scientists and business analysts to have a better strategic view.

On the other hand, the DL is more of a central, low-cost repository, storing varied data from various sources, without processing or governance. Its concept was created precisely to oppose the DW, because it allows data to be in its most raw form and it tends to democratize the area if it is used correctly and with well-defined internal processes.

Once analyzed and processed, one can move the data from the data lake to data warehouses, and there you have it. The process of generating, analyzing and storing data ready to generate insights and new business.

The importance of Data Lake grows as data scientists gain new insights from unstructured data, as it stands as a new paradigm that can democratize data within organizations, allowing different departments to make use of and change data-based operations.

5 Differences Between Data Lake and Data Warehouse

1. Data lakes are designed to support all types of data, while data warehouses make use of highly structured data - in most cases.

2. Data lakes store all data that may or may not be analyzed at some point. This principle does not apply to Data Warehouses, since irrelevant data is usually eliminated due to limited storage.

3. The scale between Data Lakes and Data Warehouses is drastically different. Supporting all types of data and storing it (even if it is not immediately useful) means that Data Lakes need to have a scalable data system in case there is a change in size or volume to meet some specific need. 

4. Thanks to metadata (data about data), users working with a Data Lake can get a basic view on the data quickly. In a Data Warehouse, it is usually necessary for a member of the development team to access the data - which can create a bottleneck.

5. Last but not least, the intensive data management required for Data Warehouse means that it is usually more expensive to maintain compared to Data Lake.

Both repositories are critical

As more companies turn to Big Data for better business opportunities, the application of Data Lake will increase. After all, unstructured data, such as social media posts and phone call recordings, contain valuable information that cannot be stored in Data Warehouses. 

In short, both are widely used to store Big Data, but they are not terms that can be. A Data Lake is vast in raw data, the purpose of which still requires much study. A DataWareHouse is a repository for structured and filtered data that has already been processed for a specific purpose. 

In the healthcare industry, for example, Data Lakes can be used to combine data, the structured and the unstructured one - such as clinical notes. Now in the Education sector, data regarding student records and attendance can actually help predicting potential problems way before they happen.
In the Transport field, things such as supply chain management, predictions and cost cutting decisions are realized by examining data from dashboards with real time data from within the transport pipeline.

All in all, much of the benefit is in the ability to make predictions.

And for your company's current situation, which repository makes the most sense to invest in? If you are interested in going further with your data-driven transformation and enhance your business capabilities, speak to a Zoox specialist to request a demo. 

We are present in 23 countries with our multilingual robust platforms and data ecosystem, count on an immense inventory, with 24/7 support, visual dashboards and easy-to-navigate interface. Set up a free consultation and understand our possibilities for your brand.