There are two types of data storage architectures, namely data lakes, and data warehouses, which have distinct features and capabilities. An organization’s goals and the purpose of collecting data determine which data collection method to use.

In both cases, data is stored, but how it is handled is completely different. Comparing them will allow you to determine which is best for your business.

Why do Data Lakes and Data Warehouses matter?

The most valuable asset of today is data. Businesses that manage data effectively are able to move forward and dominate their industries more quickly. Decisions are based on data, strategies are defined by data, and business is driven by data. For a company to succeed, collecting, managing, and storing data is a fundamental step.

Organizations that have incorporated data into their business strategy understand that storage is not just about technology. As data volumes increase, data architecture must evolve to keep up. In order to respond faster to market needs, act in accordance with data regulations (like GPRD), and analyze and devise future strategies, businesses need an effective management system. The goal is to stay competitive in the fast-paced, information-packed world we live in.

The two most common options for data architecture are data lake vs data warehouse.

What is a Data Lake?

“A huge collection of unstructured data in its original format” could be considered a Data Lake. The structuring and processing of data take place only at the point of retrieval in Data Lakes. The Data Lake is a repository for data that is used for analysis work, such as machine learning and visualization.  For Big Data, it has only recently been used.

Characteristics of data lakes

Centralization is the main characteristic of a Data Lake. Data Lakes help collect and store all types of data at any scale, making them a practical and cost-effective solution. Without prior processing, data lakes store raw, unstructured, semistructured, and structured data. The Data Scientist has new possibilities since they can structure data only at the retrieval stage.

Additionally, data lakes are very flexible and easy to manage. The introduction of new data types is not hindered, making it easier to use different applications. Moreover, it is one of the most preferred architectures for Big Data because scaling is not an issue.

A real-time data collection approach that values every piece of information equally is valuable for businesses collecting data in real-time. Businesses can use Data Lakes to handle information and put it at the disposal of marketing departments. Users have valuable data, fragmented into a variety of parameters – time, geography, preferences, demographics – which can be used to create hyper-personalized campaigns.

What is a Data Warehouse?

They collect and organize data using a specific categorization process to gain insights quickly and improve decision-making processes for businesses. Data Warehouses are defined as data management systems designed to store pre-structured data from multiple sources, in large quantities. In other words, before data is loaded to the warehouse, its use must be defined. Since the 1980s, data warehouses have been in use.

Characteristics of a data warehouse

Due to the predetermined purpose for data, Data Warehouse architecture requires careful planning: what kind of data will be retrieved, and which tools will be used to collect, organize, process, and retrieve it? A consistent set of data in defined formats, ready for analysis, is the goal. Due to the fact that it is an integrated management system and not a repository, it involves a higher level of investment. Data quality improves, allowing decisions to be made faster.

Using analytics, customers, and partner systems, data warehouses gather relevant information from specific applications, whether internal or external. In the warehouse, the data is then formatted and stored to specific allocations according to the format of already existing items. Afterward, it is processed to create outputs tailored to the business decision-making process.

As one of the strong points of Data Warehouses, consistency in format provides the integrity and quality of information ready for analysis and use without processing delays.

Looking at marketing again: knowing which products are in demand can help build a strategy based purely on predefined, structured inventory data, possibly revealing an unseen buying trend.

The main differences between a data lake and a data warehouse:

These storage management systems are designed for Big Data applications, but Data Lakes seem less “managed” than Data Warehouses. There are others as well.

Silos vs. Systems- Data lakes serve as passive data repositories, which can be used for a variety of applications in the future. With an intent to use information strategically, data warehouses are a set of technologies working together to create a management system.

Types of data- Data Lakes store data in its raw, original format. Previously stored data is transformed in data warehouses. Data Lakes are also faster when it comes to accessing data, which creates a speed difference between them.

Structured data- Data Warehouses are based on structured data, defined by specific attributes, metrics, and sources. All types of data can be collected in data lakes, whether they are structured or unstructured. The schema of data is defined before it is stored in warehouses; the schema is defined after it is stored in lakes.

Data Processing- In order to load data into a Data Warehouse, data must be transformed into a structured format using the Extract-Transform-Load process (ETL). However, Data Lakes use the Extract Load Transform (ELT) process since the data transformation occurs after it is loaded.

Analyzing data- Data Warehouse data is better for operational purposes since it is already organized and formatted. After careful data processing, data lakes can also provide operational value after in-depth analysis and experimental applications.

Technology- A Data Lake can store and process large datasets efficiently because it applies schema only to some of the data at retrieval. The purpose of a data warehouse is to provide high-speed queries on very structured data using relational database technologies.

Integrated Storage and Computing- Data Warehousing integrates both storage and computing. A Data Lake primarily functions as a repository, so storage is its primary feature while computing data is not a priority.

Conclusion

If you are considering a data lake or a data warehouse, look through these categories to decide which is best suited to your needs. A new marketing term from Databricks, Data Lakehouse, addresses the limitations of both Data Lakes and Data Warehouses. A number of solutions exist, including Delta Lake from Databricks, Apache Hudi from Uber, and Apache Iceberg from Netflix.