Data lake vs. data warehouse: What’s the key difference? 

Knowledgeable, data-driven decision-making is the name of the game in the highly competitive business landscape of the early 21st century. Organizations across various industries go to all lengths to process data properly and ensure adequate data management for the records and dossiers they accumulate. However, high-end data analysis that provides business insights can be performed only with reliance on the best data storage solution that supports a company’s business goals. 

This article will explain the essence of data lakes and data warehouses as major types of enterprise data depots, pinpoint differences between them, give advice on choosing and integrating a storage facility into your enterprise ecosystem, and explore future trends in data warehousing. 

What is a data lake?

When we hear the word “lake,” we imagine a natural water body with irregular shorelines. These two descriptions – natural and irregular – are applicable to the information that data lakes store. They contain raw data in huge volumes obtained from multiple sources – basically all the information an organization may need to leverage further in business intelligence operations.  

Data lake architecture allows for the storage of both structured and unstructured data. The first type includes any information arranged in tabular data formats. The most typical examples of such relational data are stored in data lakes Excel files or SQL databases. Unstructured data refers to diverse data types No-SQL databases encompass (text files, social media and multimedia content, IoT data, customer feedback, etc.).  

Data lakes can also store semi-structured data, meaning that it has no tabular organization but contains metadata and tags, enabling data scientists to establish hierarchies of fields and items and separate semantic elements in them. Here belong emails, RSS feeds, log and configuration files, comma-separated values (CSV) files, HTML and JSON files, and more.  

Since there is no need to transform all that data before storing it, data lake solutions are highly scalable facilities that can hoard almost unlimited quantities of information. After being accumulated, this customer and business data is employed for building data pipelines, making it available for big data analytics and business intelligence tools instrumental in operational and strategic decision-making. 

Thus, the chief advantages of data lakes are: 

• The ability to store massive volumes of information  
• A broad range of data structures from all possible data sources 
• You can extract data fast due to its raw state 

What is a data warehouse?

When we hear the word “warehouse,” we imagine a large building where goods and items are stored in their respective sections and on shelves. This is also true of a data warehouse with records and files first classified to be placed where they can be easily stored and found. Such virtual shelves are called data marts, typically containing dossiers relevant to a certain unit of an organization (like the marketing department or product team).  

To lodge the information where it belongs, data warehouse tools perform data processing operations before storing it. This means that data warehouses contain only structured data, which is usually derived from CRMs, ERPs, and other enterprise systems. Existing data warehouse architecture types include one-tier, two-tier, and three-tier structures. The latter is the most popular by far since it is ideal for handling large amounts of data.  

In the bottom tier, data is collected, cleaned, and transformed to ensure data consistency, accuracy, and relevance. Also, metadata is created here to provide high search and query data speed. In the middle tier, the curated data is organized in the shape of online analytic processing (OLAP) cubes that allow for various data science scenarios while searching for the necessary item so that you can query and access data belonging to a separate mart or request for information from the entire warehouse. The top (or front-end) tier is employed for presenting and visualizing data-mining efforts via interactive dashboards, graphs, charts, you name it.  

Given the peculiarities of data stored in data warehouses, these repositories offer data engineers the following benefits: 

• No need for preliminary data preparation, thanks to the processed data nature in the warehouse 
• High data quality allows for the swift turning of information into meaningful insights 
• Warehouse data accuracy and data reliability serve as an excellent foundation for this storage type, becoming a single source of truth for the company and ruling out departmental data silos. 

Now that you understand the main principles of data lakes’ and data warehouses’ organization, it makes sense to compare both storage facilities along several parameters.  

Key differences between data lakes and data warehouses

DICEUS’ long-time experience in crafting data management solutions allows us to pinpoint six aspects where the contrast between a data lake and a data warehouse is the most conspicuous. 

Purpose 

In traditional data warehouses, information is honed to be utilized for specific use cases – sales reporting, customer segmentation, transactional data assessment, predictive analytics, event management, and more. Data lakes act just as repositories of information, the use for which can be found later when the enterprise decides to dig into historical data and discover trends or unlock hidden growth opportunities. 

Data schema 

Data lakes don’t impose a particular structure on the information kept within it, allowing all your data to be accumulated, whether it is unstructured, semi-structured, or structured. Only when data enters the lake is its schema defined to make it suitable for further processing (the ELT approach). In data warehouses, all pre-processing operations are performed before data is fed into the system (the ETL method). It takes a longer time, but eventually, you can analyze the data you need immediately without waiting for it to be prepared for handling.  

Agility/accessibility 

Data lakes’ flexible nature allows for easy data integration, including adding and storing new data. Also, data experts can perform no-sweat data model configuration and enable analytics tools of various complexity. By contrast, data warehouses typically rely on a “read-only” data format, which means you can scan the storage for relevant data but can’t alter records in the system or track data versioning dynamics. 

Security 

The sheer amount of data, the absence of preliminary data curation, and its extraction from multiple sources make data lakes inherently less secure. In data warehouses, data integrity and security measures are a notch higher when files undergo rigid security checks and filtering before they are allowed to enter the system. 

Users 

Since data in a warehouse is cut and dried, it doesn’t require advanced technical expertise for further handling. Consequently, it can be used by both managers and rank-and-file personnel looking for data-driven insights. Large volumes of unstructured data make the employment of data lakes prohibitive for business analysts. Instead, companies hire data science consultants and engineers whose professional competence enables them to process and interpret data lake dossiers. 

Cost 

Putting all the data into one place without any filtration or pre-processing is certainly cheaper, which gives data lakes an edge over warehouses. The latter require more time and effort for management plus they are more expensive to set up and maintain.  

As you see, each data storage type has its merits and demerits. How to select the one that will fit you to a tee? 

Choosing between a data lake and a data warehouse

While opting for either a data lake or a data warehouse, you should be aware of the trade-offs you will have to make and steer by future use cases of the system and the data it contains.  

A data lake is preferable if: 

• You don’t have a clear vision concerning the data you will extract and store 
• You don’t know how data points will relate to each other 
• The amount of data to be accumulated is exceedingly large 
• Data to be collected is going to have various formats and come from multiple sources 
• The prospective use cases for the data are predictive analytics, data exploration, or training for machine learning projects 

A data warehouse is what the doctor ordered if: 

• Data to be kept in it is carefully selected and precise 
• Data formats aren’t expected to change 
• Data must be compliant with business and regulatory requirements 
• Data requires special handling because of security or auditing considerations 
• The anticipated data use cases are fast querying and generating business reports 

Modern achievements in the data science domain are intensely blurring the borderline between data lakes and data warehouses, ushering in data lakehouses – open architecture solutions that combine the flexibility and cost-efficiency of data lakes with advanced data management capabilities that data warehouses display. Merging the two classical organizational principles, data lakehouses present a single system where large volumes of raw data can be stored, whereas data structuring and management functions are available as well.  

When you have decided on the type of data storage that aligns with your technical and business requirements, it’s time to integrate it into your digital infrastructure.  

Integration of data lakes and data warehouses: A roadmap outlined

The processes of data warehouse and data lake implementation are essentially identical. In our data warehouse projects, we apply a four-step algorithm for integrating a data storage solution into an organization’s IT ecosystem. 

Step 1. Data requirements definition 

To begin with, you should understand several crucial points concerning the kind and volume of data you have, its format and quality, the purpose and frequency of usage, and the period of storing it. These issues will not only determine the storage facility type (data lake or data warehouse) but also help you identify the best ways to store, process, and access data in it. 

Step 2. Choosing data architecture 

No matter what solution type you will eventually opt for, you should design its architecture carefully. It is especially important with a data warehouse where you should establish the number of tiers and data marts it will have. Alternatively, you may decide to have both a lake and a warehouse, each with its own scope of functions. However, in such a case, you should ensure a seamless exchange of data between the two systems. Or, you can create a data lakehouse, enjoying the best of the two worlds.  

Step 3. Implementing data integration 

This is when data from various sources (or your previous storage system) is moved to the new environment. If the new solution is a data lake, you will leverage the extract, load, transform (ELT) scheme, whereas, in data warehouses, the basic approach presupposes data extraction, transforming, and only then loading (ETL). For hybrid solutions, the ETLT method is usually employed. 

Step 4. Data access optimization 

Depending on the nature of the storage facility and your data management needs, you should determine what mechanisms you will leverage for querying, analyzing, and visualizing data. For data lakes, these include NoSQL and BI tools, while SQL instruments are a better fit for data warehouses. 

Since data storage facilities are meant to last, their implementation should be conducted with an eye to the future trends in the data science industry. 

Data storage solutions: What’s in the offing?

As a vetted expert in the data warehousing field, DICEUS keeps its fingers on the pulse of the sector, so we are aware of the four major trends in it that will dominate the realm in the foreseeable future. 

Increasing AI involvement 

Artificial intelligence is penetrating an ever-widening scope of domains, and the data storage and management industry is no exception. Gartner predicts that, in the next year, 50% of data centers will leverage AI and ML-fueled robots. AI-driven tools will be increasingly employed for data engineering, storage infrastructure monitoring, capacity planning, storage provisioning, backup implementation, workload migration, and other tasks. It will not only boost the solutions’ efficiency but also allow businesses to automate a large share of low-value and routine jobs, thus freeing personnel for more complex or creative tasks. 

Introduction of DNA data storage 

As the amount of data generated globally continues to spike exponentially, the dearth of storage facilities will be felt more acutely over time. The solution to this problem is the nascent DNA data storage trend. Being able to understand a four-letter code (whereas computers use binary code), DNA-based systems allow for storing vast data bulks on tiny-size carriers that have indefinite life spans.  

The main factor that limits the swift spread of this technology is its high cost. However, the DNA data storage market is predicted to manifest a mind-blowing CAGR of 65.8% until 2028, offering mouth-watering revenue prospects for potential investors. And a large influx of investments into the niche will reduce DNA data storage costs down the line, making such facilities available for commercial use one day.  

Reduction of data storage costs 

The increasing demand for data storage facilities is accompanied by the rapid growth of associated expenditures. Realizing this, organizations seek new ways to save on such big-ticket items. The most promising approaches to storage cost minimization are software-defined storage and object storage that optimize capacity utilization and remove redundant data, whether in the cloud or on-premises. 

Implementation of zero-trust architecture 

Cybersecurity threats are among the most pressing issues across contemporary digitally driven businesses, with 61% of decision-makers particularly concerned about data protection. Yet, when put to the test, each enterprise data storage device was reported to have, on average, 15 security-related problems, which is especially alarming given the 80% surge in ransomware attacks and other cybercrime instances. Traditional security models often turn out to be inadequate for stemming this onslaught, as once granted initial access, cybercriminals can roam the system freely, wreaking havoc along the way. 

To counter such dangers, companies launch zero-trust architecture solutions that require user authentication, authorization, and validation at each interaction. They are augmented by AI-powered ransomware detection tools and enhanced by creating immutable backup data copies, with files locked in place for a set period. 

Choosing a lake or a warehouse as a data repository and implementing it is a serious task that should be delegated to vetted experts in the field. Qualified and certified data experts at DICEUS possess the necessary skills and experience to handle such projects and consult with you on the details. Contact us to access a robust data storage facility that meets your business requirements and delivers maximum value to your organization.  

To sum it up

Data lakes and data warehouses are two basic data storage models used by enterprises today. While data lakes accumulate information from any source and in any format, data warehouses are honed for storing only structured and pre-processed data. To onboard an efficient storage solution, consider your requirements, follow a clear implementation strategy, pay attention to dominant trends in the niche, and enlist high-profile data experts to tackle such projects. 

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