Data Lake vs Data Warehouse: Understanding the Differences

Although data lakes and warehouses commonly store large amounts of data, the phrases are not equivalent. Understanding the differences between data lake vs data warehouse is beneficial in today’s data-driven world.

The two methods of data storing are sometimes mistaken, yet they are vastly different. In reality, they have only in common that they both store information. A data lake is a store of unstructured data with no specific objective. A data warehouse is where organized, screened information previously handled for a particular function can be stored.

Since they serve various objectives and demand multiple sets of vision be effectively tuned, differentiation is critical. A data lake will be helpful in one organization, but a data warehouse will be valid for another.

What is a Data Lake?

Data lakes are a centralized repository for enormous amounts of native, unstructured data. A data lake stores data using a flat design and object storage. Object storage saves data with metadata tags and a unique identifier, which increases efficiency and makes it easier to identify and retrieve data across locations. Using affordable object storage and open formats, data lakes allow other applications to utilize the data.

Data lakes are frequently used to store an organization’s data in a single, centralized location without imposing a schema (i.e., a formal structure for how the data is structured) in advance, as a data warehouse does.

Raw data can be ingested and stored alongside an organization’s structured, tabular data sources (such as database tables) and intermediate data tables developed during the refining process of raw data.

Benefits of a Data Lake

• At the time of intake, a data lake removes the requirement for data modeling. Instead, data modeling can be done when analyzing data for analytics. This approach provides unrivaled versatility in asking and receiving answers to any business or domain issue
• When scalability is factored into the equation, a data lake is affordable compared to a data warehouse
• A data lake can contain data that is multi-structured and comes from a variety of resources. A data lake can have logs, XML, video, location information, binaries, engagement metrics, conversation, and individuals’ data, to name a few things
• Conventional data warehouse infrastructure mainly handles SQL, which is acceptable for basic analysis, but we need other ways to examine data for sophisticated applications. For analytics, a data lake gives various tools and language support
• In contrast to a data warehouse, a data lake combines massive amounts of consistent content with deep learning techniques. It aids in the analysis of real-time decisions

What is a Data Warehouse?

A Data Warehousing (DW) is a method for collecting and organizing data from diverse sources to offer insightful business intelligence. Typically, a data warehouse is used to connect and analyze corporate data from disparate sources.

The data warehouse is the heart of the business intelligence system, which is designed for data processing and reporting.

It is a combination of technology and components that facilitate the strategic utilization of data. It is the electronic storing of voluminous amounts of data by a business that focuses its activities on query and analysis as opposed to transaction processing. To make an impact, data must be transformed into information and made available to consumers on time.

Benefits of a Data Warehouse

• Decision-makers will no longer have to depend on restricted data or their intuition because they will have accessibility to data from multiple resources from a unified interface
• Because all users can access crucial data, they can make informed judgments on essential issues
• A data warehouse transforms data from several resources into a standardized structure. This will result in more precise data, serving as the foundation for informed choices
• Organizations that have invested in a data warehouse see more profits and cost reductions than those that have not
• Data warehouses assist firms in gaining a complete perspective of their current position and evaluating possibilities and challenges, giving them a competitive edge

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

The development of data lakes was a response to the constraints of data warehouses. While data warehouses provide businesses with highly performant and scalable analytics, they are costly, proprietary, and incapable of handling most modern use cases.

Unlike most databases and data warehouses, data lakes can process all data kinds, including unstructured and semi-structured data such as photos, video, audio, and documents, which are essential for machine learning and advanced analytics use cases in the present day.

Data Handling

Before data is recorded and maintained, it is organized, categorized, and provided metadata in a data warehouse. This method is known as ‘schema on write.’

A data lake absorbs all data types, even those unsuitable for a data warehouse. Data is maintained in its raw state; content is stored in the schema as data is taken from the data source rather than transferred to memory. This is referred to as a “schema on read.”

Data Storage

An organization’s information is stored in a crude, disorganized pattern in a data lake, which can store the data for an indefinite period for instant or prospective usage.

A data warehouse stores structured information that has been checked and analyzed and is fully prepared for competitive strategy depending on predetermined business requirements.

Users

Data scientists and engineers who want to analyze data in its native format to acquire fresh, innovative business perspectives generally use data from a data lake containing a massive quantity of unprocessed information.

Managers and business-end users who want to understand business KPIs generally use data from a data warehouse because the data has already been prepared to offer responses to predetermined queries for examination.

Processing

The information is retrieved from its origin for placement in the data lake and only organized when required in the data lake process.

Data is retrieved from its source(s), cleansed, and then formatted in the data warehouse procedure for business-end analysis.

Cost

Compared to a data warehouse, storage expenses in a data lake are pretty low. Data lakes are also easier to administer, resulting in lower operational costs.

Data warehouses are costlier than data lakes, and managing them takes more work, resulting in higher operational costs.

Storage and Retention of Data

Data engineers spend a lot of time analyzing data and figuring out how to use it for strategic planning before storing it in a data warehouse. They create modifications to synthesize and change data to extract valuable insights.

Data that doesn’t answer specific market queries are excluded from the data warehouse to save storage capacity and enhance efficiency. A traditional data warehouse is a costly and precious corporate commodity.

Data retention is more straightforward in a data lake because it saves unprocessed, organized, and unclassified information. Data is never erased, allowing for studying past, present, and prospective data. Data lakes are simple to develop and grow up to Petabytes in size.

They run on low-cost storage devices and commodity servers, easing storage constraints.

Agility

Historical data is stored in data warehouses. The format of incoming information is predetermined. This comes in handy when dealing with particular business questions.

On the other hand, data warehouses are insufficient if business questions change or the company wants to keep all data for in-depth analysis. Adapting the data warehouse and ETL process to new business issues is a significant development endeavor.

A data lake stores the data in its original format and makes it immediately accessible for analysis. It may be retrieved and reused using a structured schema, storing and exchanging data. The copy can be destroyed without harming the data lake if the data is no longer required. All of this is achieved without any effort from the developer.

Security, Usage, and Maturity

Data warehouses are a safe, enterprise-ready solution operating for decades. In contrast, Data lakes are still a relatively new concept, but they’re fresher and have a smaller proven record in the industry.

A large company can’t just buy and install a data lake as it can a data warehouse; it has to think about which technologies to use, whether free software or proprietary and how to put them together to satisfy demands.

Data Lake vs Data Warehouse: Which One Should You choose?

A data warehouse will integrate nicely into your corporate setting if you employ SQL databases, CRM, ERP, or HRM applications. A data warehouse is ideal for firms that cope with well-organized or organized data.

If your information comes from a variety of origins (e.g., IoT logs including telemetry, executable code, analysis), data lakes are likely to be a preferable option, as the ETL (extract, transform, and load) process in a data warehouse will end in considerable information degradation.

A data warehouse will undoubtedly work if you can cope with statistics produced by performing a preset set of information on the table(s) that is routinely maintained.

Depending on your company’s demands, establishing the correct data lake or data warehouse will be critical to its development. Choosing which one should be preferred depends upon your organization, and each comes with its own benefits.

• Healthcare: The healthcare industry has utilized data warehouses for years, but they’ve never proven profitable. Data warehouses aren’t appropriate for healthcare because of the unstructured nature of much of the data (physician notes, clinical data, etc.) and the necessity for real-time insights.

Data lakes let healthcare companies combine structured and unstructured data.

• Education: Big data’s relevance in educational reform has grown recently. Grades, attendance, and other data can assist failed students in getting back on track and forecasting possible concerns. Big data has helped schools streamline billing, enhance fundraising, and more.

Because much of this data is enormous and raw, educational institutions benefit from data lakes’ versatility.

• Finance: In banking and other business environments, a data warehouse is often the optimal storage option because the entire firm can access it.

Data warehouses have helped the financial services industry progress considerably. A financial services organization may avoid this strategy because it’s more cost-efficient but less effective overall.

• Transportation: In the transportation industry, primarily supply chain management, flexible data in a data lake can have tremendous benefits, namely cost-cutting gains from reviewing transport pipeline form data.

Frequently, organizations require both. Data lakes arose from the need to leverage big data and benefit from the raw, granular organized, and unstructured data for machine learning. Yet, business customers still require data warehouses for analytics.

Summary: Data Lake vs Data Warehouse

We can identify numerous differences when comparing data lakes vs data warehouses. The primary differentiators are data structure, desired consumers, processing methods, and the ultimate purpose of the data.

• Data Lakes store all data, regardless of its source or format, whereas the Data Warehouse maintains quantitative metrics alongside their properties.
• Data Lakes are a repository for storing massive amounts of structured, semi-structured, and unstructured data. In contrast, Data Warehouse is a combination of technologies and components that enables the strategic use of data.
• Data Warehouses define the schema before data storage, whereas Data Lake defines the schema after data storage.
• Data Lakes employ the ELT (Extract Load Transform) process, whereas the Data Warehouse uses the ETL (Extract Load Transform) approach.
• Data Lakes are great for people who desire in-depth analysis, while Data Warehouse is ideal for operational users.

If you’re engaging with more exploratory situations, such as machine learning, IoT, or prescriptive modeling, it’s best to keep raw data in its original format in the data lake.