Data Lake vs. Data Warehouse (Plus Data Management Skills)
By Indeed Editorial Team
Published June 6, 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Data warehouses and data lakes are common ways organizations manage and store data. Typically, a company opts for either of them depending on its current storage needs and industry. Understanding data lakes and data warehouses can help you learn their distinct features and what informs various organizational decisions regarding which to use.
In this article, we compare data lake vs. data warehouse, discuss industries that employ both data types, and highlight examples of other data management skills.
Data lake vs. data warehouse
Here are some of the features to consider when comparing data lake vs. data warehouse:
A data lake is a digital storage location that commonly preserves data that's without a specific purpose and unfiltered at significant scales. They enable users to store data without categorizing or organizing the data beforehand. Conversely, users can apply machine learning or analytics to the data lakes to reach informed and effective data management conclusions. For example, an organization can employ a data lake to store all their client data and then evaluate it to attract consumer attention correctly.
A data warehouse is also a digital storage location. Still, it manages considerable amounts of structured and filtered data with specific purposes. The data in these warehouses are usually from different systems or databases, and it updates itself regularly. Businesses may preserve several databases within a warehouse and use several organizational tiers to handle and retrieve data.
The data organizations store within data warehouses and data lakes differ because warehouses leverage analyzed data while lakes use raw data. Data lakes commonly have larger storage capacities because of their data type. This ensures that there's sufficient space for all the unprocessed information. Data lakes also combine excellently with machine learning, which uses algorithms to identify trends in raw data. Still, data lakes may require frequent maintenance to avoid losing the unorganized raw data.
In contrast, a data warehouse structure commonly organizes its data automatically. The data source may also influence the specifications for these organizational systems. Commonly, this includes groups like operational data, enterprise data, and general data. Any general data may also be in independent, dependent, and hybrid subcategories.
Data that businesses store in data lakes commonly has undetermined purposes, while warehouses exclusively preserve data that has determined functions. For example, unfiltered data may remain unused in a data lake for extended durations until organizations assess its use. Meanwhile, the data in data warehouses may store automatically in a structured system depending on its predetermined function. For example, data warehouses can automatically categorize data pertaining to website analytics. This organizational ability may result in less storage capacity.
The professionals who employ data lakes or warehouses commonly differ depending on the data's function. This is as data lakes' unstructured nature is usually more challenging to navigate. It's commonly easier for people to understand the structured information that organizations store in data warehouses. Lake users may include data professionals who can filter data, including data developers or data scientists. In contrast, warehouse users may include business analysts or finance professionals who leverage the structured data to reach informed conclusions.
Data accessibility within the storage locations varies. For example, individuals can easily access data lakes and make alterations or modifications as there's no precise structure to the lake's structure. Conversely, data warehouses have precise architectural structures, making their resident data more accessible to various users. Still, modifying the data in a data warehouse is typically challenging because it contains structured data.
Data warehouses and data lakes have different data quality due to their curation levels. This is as warehouses primarily emphasize storing highly curated and organized data, having higher-quality data. The data's quality enables instant use and complete evaluations. Data lakes store lower-quality data as it's largely unfiltered.
Professional industries that use data lakes
Here are three examples of professional industries that employ data lakes:
Health care organizations may benefit from using data lakes as they handle unprocessed information, such as medical notes. Doctors and nurses may require real-time understanding and the ability to modify information manually. Data lakes can offer a mixture of unprocessed and processed data, which may provide an efficient alternative for evaluating and preserving patient data.
These professionals employ data lakes to track grades, attendance, or administrative duties. Educators may also use this information to assist students with resources or upgrade individual attendance values and grades. This flexibility requirement makes data lakes an adequate storage alternative for schools or educational companies.
Transportation companies can also gain insights from using data lakes to predict supply chain management operations. Flexible data ensures the organization can assess various outcomes carefully. This enables them to evaluate all operational aspects, including fuel expenses, delivery periods, and export or import fees when necessary.
Industries that use data warehouses
Here are three examples of professional industries that employ data warehouses:
Data warehouses are relevant for finance companies due to their easy-to-understand format and organizational structure. This enhances accessibility for finance professionals who don't possess data science techniques for manually classifying and processing information. Financial staff may use warehouses to keep essential data or information, including business reports. Organizations in this sector may also want warehouse storage's cost-effectiveness compared to other alternatives.
Manufacturing organizations commonly require analyzing and comparing trends to reach informed business conclusions regarding product pricing and sales. The data they analyze may be from several business locations and require frequent routine assessments to ensure reasonable profits or return on investments (ROI). Data warehouses enable organizations to review complex data sets easily and distribute less complex results.
Data warehouses may also be appropriate for research-based organizations that require data and information reports storage for future applications. For example, a drug research organization may require referencing data from an initial trial during subsequent trials with the same product. Data warehouses can offer them a storage location that enables easy identification of their reports when necessary.
Examples of data management skills
Here are some examples of data managing skills to facilitate analysis and use of data:
It's essential to be able to assess data and identify trends to reach various insightful conclusions. This involves getting familiar with the dataset you can access and recognizing innovative solutions regarding how to employ them in achieving their goals. You can improve your data analysis skills by undergoing various offline and online training courses, applying for internships, and constantly practising to increase proficiency in the skill.
Account and file management
It's essential to be familiar with organizing files and folders on your computer or network. This also includes knowing how to download, upload, copy, or move files and photos. The ability to effectively track your online accounts and assist others in tracking their passwords, accounts, and usernames is also vital. You can develop your account and file management skills through the constant use of file management software. You can also improve your organizational skills to help you leverage your resources more effectively.
Managing database software
Another database management skill involves effectively navigating software databases. It includes understanding record locating and using other software functionalities, such as reports and build-in forms in a database. Excellent technical programming skills are helpful, but they aren't essential for a job in the field. You can develop these skills by taking related short courses or completing a relevant bachelor's degree.
Database design and planning
In-depth knowledge of database design concepts is also essential for developing excellent data management skills. It's vital to understand the benefits and limits of various database types. Likewise, feeling comfortable while participating in the short and long-term preparations for database activities and understanding multiple data analysis and storage approaches is essential. You can develop your database design and planning skills by completing a bachelor's degree in database management or data science.
Communication skills are an essential aspect of data management. It's essential to develop clear communication routes with your colleagues to assess data to ensure its accuracy. Communication skills can also help you discuss with vendors to explain your requirements clearly. The skill is also essential when working in a group as it facilitates the easy transfer of accurate information and instruction across the entire team. You can develop your communication skills through constant practice.
This skill involves the ability to comprehend what a particular data set means to an organization. Usually, before beginning the data gathering process, it's essential to determine why you're collecting it and its connection to the organization's needs. This enables you to employ strategic approaches to data gathering to ensure you collect accurate and relevant data. You can develop your business intelligence skills by taking consulting or data analysis courses.
Data integrity involves acknowledging possible weaknesses when you're evaluating and applying data. It also means understanding the definitions, program guidelines, and data sources. You can develop data integrity by conducting routine assessments of your progress during data projects to evaluate their integrity.
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