Data Lake Vs Data Warehouse: Understanding the 4 Key Differences
By Brian Laleye · April 19, 2022 · 2 min read
Big data should be used to leverage your existing expertise, streamline your business, and address known pain points. The big data environment has multiple choices and terminologies that are related to the different stages of processing. The Data Store is one of these terminologies. This can be done through a data lake or a data warehouse — but which one is better? When you have a lot of data, you need to know whether a data lake or a data warehouse is right for you. Get the answers to your questions to make an informed decision that works for your organization.
What Is a Data Warehouse?
In the late 1980s, IBM researchers Barry Devlin and Paul Murphy introduced the “business data warehouse”, marking the emergence of the concept of data warehousing.
A data warehouse is a type of relational database that prioritizes query and analysis over transaction processing. It typically stores historical data from transactions, as well as other sources. By separating the analysis workload from transactional tasks, organizations can consolidate data from various sources and gain insights into trends and patterns.
The structure of the database supports ad hoc queries when compared with a database optimized for online transaction processing (OLTP), which would contain denormalized data in fewer tables, but it is structured for more efficient transactions. Data warehouses often use a schema on write strategy — storage is optimized after the information has been loaded into the database, not at the time of creation.
A data warehouse may provide an architectural foundation for a business intelligence system and can become an important source of data for reporting and analytics.
Data warehouse structure from IBM
What Is a Data Lake?
A data lake is a recent concept, it was created in 2011. by James Dixon, chief technology officer at Pentaho.
A data lake is a storage system that stores raw data in its original format until it’s needed. Unlike hierarchical data warehouses that store data in files or folders, a data lake uses a flat architecture to store data. Each piece of data in the lake is assigned a unique identifier and labeled with metadata tags. This makes it easier for users to find and retrieve the specific files they need from the lake when they have business questions.
Because they are not limited by fixed-schema definitions, lakes are extremely flexible and can support any type of file including unstructured, semi-structured, and structured data. The ability to easily add new sources of information makes the lake an ideal repository for organizations that want to tap into new sources of information for competitive advantage.
Data lakes have become widely popular because they allow organizations to store all their data—including structured, semi-structured, and unstructured data—in one centralized repository which is more secure and less expensive than other storage solutions. By storing diverse types of data in their native format within a single repository, organizations can more easily mine all their information for insights that lead to a competitive advantage.
What is a data lake? From AWS
Both data lakes and data warehouses are central repositories of company data, but they have their differences. They both have their use cases and the choice of which one to use often depends on the business requirements.
4 Key Differences between Data Lakes and Data Warehouses
The difference between a data lake and a data warehouse starts with the structure of the stored data.
The differences between the two approaches are straightforward: A data lake stores data in its original format. A data lake is a large repository of raw data whose purpose has not yet been defined. Raw data refers to data that has not been processed for use, and it can be either structured or unstructured.
A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose. This might be analytics or machine learning (ML), for example.
Theoretically speaking, a data lake can be used to store any type of information, while a data warehouse is usually reserved for structured information such as customer relationship management (CRM) or enterprise resource planning (ERP) systems.
Purpose of Data
The purpose of a data lake is to store as much of the company’s raw data as possible while a data warehouse stores refined versions of operational systems’ data in a format that can be easily queried and analyzed by business users. As a result, data lakes have less data structure and filtration than their counterparts.
Raw data that has been transformed for a specific purpose is known as processed data. All of the data in a data warehouse has been used for a specific purpose within the organization because data warehouses only store processed data. Consequently, storage space is not squandered on data that may never be accessed.
Data lakes are mainly used by technical users like data engineers or data scientists who understand the structure of raw data while data warehouses are used by business analysts and other business stakeholders who don’t understand the structure of raw, unprocessed data.
A common point of confusion is that a single enterprise may have both a data warehouse and a data lake. That’s because the two serve different purposes: it is easier for end-users to access and analyze the stored and processed data in a traditional data warehouse than it is for them to access and analyze the raw and unprocessed data stored in a data lake.
How to choose between Data Lake and Data Warehouse
Companies should choose their data management solution based on their needs, resources, and goals. If a company wants a one-time analysis of historical data to make a single business decision, a data lake is probably the best option. But if the company needs to run queries, do machine learning, or analyze data for any other reason, it’s best to go with a warehouse.
If you need to store cold data in your warehouse, it’s possible to create separate tables and move cold data from hot tables into them. You can also move cold data into a separate database as long as you have an ETL tool that supports that kind of movement.
Alternative to the Data Lake and Data Warehouse use: Data Mesh
Organizations today are looking for ways to evolve their approach to processing and storing data. This is not surprising, as the volume of data available continues to grow, as does its complexity. Data lakes and data warehouses are two approaches to managing this influx of data. Each has advantages, but they also have limitations, which can be overcome by a technology called the Data Mesh.
The concept of the data mesh is relatively new, it was first proposed in 2019 by Zhamak Dehghani, a principal consultant at Thoughtworks.
Data mesh is a new take on data storage and management. Instead of an ocean of data storing individual swim lanes, data mesh allows for cross-channel interaction between multiple data sources. It’s not a traditional database and it can be thought of as something akin to a data lake by utilizing a flat “no hierarchy” structure. But then again, it’s also similar to a data warehouse in the sense that it’s relational and not only stores “raw” data in its mesh. If you’re confused by those conflicting descriptions, then this guide will clear things up!
If you need help implementing your data stack, check out the RestApp website or book a demo.
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