From Data Science

Data Lake vs. Data Warehouse: What are the differences?

Data Lakes and Data Warehouses are two types of data storage architectures with distinct attributes and abilities. Choosing one or another depends on the intended use of the collected data and the organization’s goals.

Both have one thing in common - they store data - but how they handle it is completely different. Let’s compare them and see which may be the best option for your business.

Table of Contents

Data Lake vs. Data Warehouse: Why do they matter?
What is a Data Lake?
    ➤ Data Lakes’ characteristics
What is a Data Warehouse?
    ➤ Data Warehouse’s characteristics
Data Lake vs. Data Warehouse: Main differences
Data Lake vs. Data Warehouse: Which is best?
Data Lake vs. Data Warehouse: Takeaway

Data Lake vs. Data Warehouse: Why do they matter?

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 organizations 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 analyze 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.

What is a Data Lake?

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.

Data Lakes’ characteristics

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.

Read also: Data Science: What it is and how it can help your business?

What is a Data Warehouse?

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 categorization 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.

Data Warehouse’s characteristics

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, organization, processing, and retrieval? The goal is to have a consistent body of data in defined formats, ready to be analyzed.

Since it is a management system made up of differente tecnologies 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.

Read also: SQL vs NoSQL: When to use?

Data Lake vs. Data Warehouse: Main differences

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.

  • Silo vs. System- Data Lakes work as a passive data repository, which is used for different applications later. Data Warehouses are a set of technologies working together to create a management system aimed at the strategic use of information, with an intent in mind.

  • Data types - Data Lakes store data in its raw, original format. Data Warehouses transform data previously to storage. This also creates a difference in speed between them, being Data Lakes faster when it comes to data accessibility.

  • Data structure - Data Warehouses focus more on structured data, defined by specific attributes, metrics, and sources. Data Lakes collect all types of data, from structured to unstructured. Warehouses define data schema before storage; Lakes define schema after.

    With Data Lakes, this allows for more flexibility. Since there is no predetermined schema, they can be created according to the available data and specific goals and remade on a case-by-case basis.
    Data Warehouses have to define data models upfront, taking into account all the specific requirements for the application.

  • Data processing -Data Warehouses use the Extract-Transform-Load process (ETL) because data must be transformed into a structured format before being loaded into the Data Warehouse. On the other hand, Data Lakes use the Extract Load Transform (ELT) process because the Data transformation occurs after being loaded into the Data Lake.

  • Data analysis - Data Warehouse data is better for operational uses since it is already organized and formatted. Data Lakes are better for in-depth analysis and experimental applications but can also provide operational value after careful data processing.

  • Technology - Since data Data Lakes apply schema only to some of the data at the time of retrieval, it can rely on simpler frameworks to efficiently store and process large datasets. Data Warehouses use relational database technologies to provide high-speed queries against very structured data.

  • Storage & Computing - Data Warehousing is more complex because it integrates both storage and data computing. Data Lakes have a decoupled storage and compute approach: they mainly work as a repository, so storage is their main feature while computing data is not a priority.

  • Costs - Data Warehouses, as a technology package, are more expensive and less flexible to changes, requiring thorough planning. Data Lakes are more affordable and quicker to update. Both bring good ROI if well used.

  • Limits - Data Lakes allow for more freedom in data processing: data is always in its original raw format, kept forever, to be transformed and reused at will for any possible application. Data Warehouses reduce the malleability of data by forcingly transforming it at intake, but that’s their purpose: to generate preformatted information with a specific intent in mind.

  • Target - Data Lakes allow for more serendipity in data, making them ideal for Data Scientists who use deep data analysis for statistical analysis and predictive modeling. Data Warehouses are ideal for business professionals focused on operational purposes and performance metrics. Data presentations are better structured, being easier to use and understand, as the information is tailored to the users’ specific needs.

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Data Lake vs. Data Warehouse: Which is best?

Deciding which is the best option depends on the users who will be working with the data and the organization’s objectives for the collected data. There are advantages to both systems, but they cater to different needs.

There are a few things to consider before opting for one of them:

  • Type of data - How consistent is the data? Does it come in many formats? How many sources does it have? Is it meant for reuse? The more specific and rigid specifications get, the more the choice leans to Data Warehouses. The more open and flexible specifications can be, the more appealing Data Lakes become.

  • Users - Data Lakes are a playground for Data Scientists or other users that easily handle raw data. Unstructured data requires specialized tools to analyze and transform it into usable information. Data Warehouses process data into readable formats like tables, charts, spreadsheets, catering to business professionals who need specific information in a specific format.

  • Use - What is the intent behind the use of data?

    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.

    Read also: What is a Big Data Engineer and why your business needs one?

Data Lake vs. Data Warehouse: Takeaway

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.

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