In this article, you are going to learn the big difference between data lakes vs data warehouse and the usability of each one for different business data.
It’s no secret that the world is going digital, and the growth of this data is giving organizations a wealth of new opportunities to be more efficient and productive. A data lake helps enterprises manage all of this big data on a single platform.
Data Lakes vs Data Warehouse
Data lake: you can manage a large amount of data from various sources by implementing database capabilities like ad-hoc querying, updating, and adding.
Data Lakes vs Data Warehouse,
Data warehouse: It is built for data analysis and reporting processes for core business intelligence (BI).
An enterprise data warehouse system is governed by a well-defined metadata model, with reference data islands that store the metadata and referential integrity information to ensure consistency.
What are Data lakes?
Data lakes provide an easy way to manage a large amount of data from various sources by implementing database capabilities.
A data lake holds a variety of raw data and coordinates with the existing applications to perform sophisticated analysis and generate insights in real-time.
A data lake stores raw files that contain multiple formats of data.
Because a conventional database system cannot handle the various types of files, many companies choose to use a separate system that’s optimized for unstructured information.
Data Lake is a kind of database where Big amount of data is poured in without taking into any consideration of specific schema.
Related Article: What is Data Lakes in AWS?
What is a data warehouse?
It is a single, central storehouse for data from disparate sources and a data lake is a software system used for managing a large amount of data from different sources that exist in various forms and from various origins.
A data warehouse is a system that is considered the core component of business intelligence that helps in reporting and data analysis.
A data warehouse is a group of data that act as structure and function as a central data repository for an organization, where it allows users to query, report, and analyze data at scheduled times.
Related Article: Data Warehouse Concepts In Modern World.
Data Lakes vs Data Warehouse
Data warehouses are used for data analytics and improving performance. Data lakes provide a single source of information and are used when analyzing
Data warehouses and Data Lake are 2 different IT solutions used to analyze data. The data warehouse is the top tier strategy for integrating data for business intelligence. Data Lake is the more flexible & scalable solution for non-analytical use.
Data Lakes are more suited for analyzing unstructured data. Data Warehouses on the other hand are used for structured data and are built using special-purpose databases such as Microsoft SQL Server
Data warehouse is where the processed data and statistical output are produced. However, both of these terms are business terms, unique to the particular business field.
Data warehouses are large repositories of integrated data from disparate sources. Data lakes are used to manage a large amount of data from various data sources by implementing database capabilities and business intelligence tools.
Benefits of Data Lake
A data lake is a storage repository designed specifically for large volumes of semi-structured or unstructured data. Management of data lake typically focuses on treatment, governance, and continuity-of-access.
A data lake is a storage mechanism in cloud computing that stores raw files to process them later.
One of the organizations’ biggest concerns is the data from the various sources and how to manage it quickly and efficiently.
A Data Lake helps organizations in multiple ways in managing and making sense out of the data.
The data lake is rapidly gaining momentum as a preferred method of big data management and analytics.
Benefits Data Warehouse
Data warehouses are primarily analytic technologies that can handle transactional data, but typically at lower volumes than native operational systems.
A data warehouse is a database specifically built to support online analytical processing (OLAP) tools.
A Data Warehouse is created using the process of Extract, Load, Transform (ELT) and Data Lakes that are not compatible with ELT.
A data warehouse is designed to handle structured rather than unstructured data.
Data Management Using DL and DW
When it comes to managing data, there are two main strategies, the Data Lake and the Data Warehouse. Both of these can be used for big data analysis, but with a distinct difference.
A Data Lake helps to manage a large amount of data from various sources by implementing database capabilities.
Data lakes in different industries are used for different processes and a data warehouse is a system used for reporting and data analysis and is considered a core component of business intelligence.
A data warehouse is a central repository of integrated data from disparate sources and the implementation of DWs can be time-consuming and expensive, so some organizations take a “data lake” approach to data management.
The two have different purposes and uses. A data lake can be regarded as the parent of a data warehouse, more general and expansive in scope, but more difficult to manage.
Data warehouse for Business
Data warehouses are systems that provide companies with detailed views of their business operations.
They can be used for reporting purposes, especially by mid-to-upper management to track the overall performance of the company.
This is typically done through various forms of analysis that enable the data warehouse to classify and organize data properly.
Data warehouses are used for storing transactional data, and a data warehouse captures data from multiple sources in an organization.
Data Lake for Business
Data lakes, on the other hand, are large repositories of raw data that can be used by any user or organization in its original format.
They are especially popular among data analysts working in quantitative fields like economics, statistics, and information sciences.
A data lake is a large storage repository that is available for fast access, retrieval, and analysis at any point in time.
To illustrate the importance of data lakes, let us compare it to a warehouse – when we walk into a supermarket or departmental store, we are faced with a huge range of products.
The moment we enter the store, we know exactly what product we need by looking at its shelves or racks.
In comparison to this scenario, data lakes store all the gathered data in an organized way. This can be beneficial for people who are responsible for managing and analyzing various types of information to make important business decisions.
Used of DL and DW in Organizations
These days, large companies rely on data lakes and data warehouses for marketing, CRM, and analytics activity.
Both with their own merits, there are a number of distinct differences that set these two systems apart.
Data lakes provide real-time access to raw data while data warehouses focus on historical analyses.
With their different methods of storing and analyzing data, each can have its specific role in the business.
Conclusion
Data lakes and Data warehouses both are very different from each other on the basis of several things which we have learned above.
A data warehouse is designed to provide accurate measures for reporting and financial analysis.
On the other hand, Data lakes contain data from a variety of sources that can be used at a later moment to develop new products or discover patterns in data.
Recommended Articles:
Data Warehouse Concepts In Modern World.
What Is The Process Of Big Data Management? – In Detail
What Is Big Data? In Modern World
Presenting the Data Engineer Team, a dedicated group of IT professionals who serve as valuable contributors to analyticslearn.com as authors. Comprising skilled data engineers, this team consists of adept technical writers specializing in various data engineering tools and technologies. Their collective mission is to foster a more skillful community for Data Engineers and learners alike. Join us as we delve into insightful content curated by this proficient team, aimed at enriching your knowledge and expertise in the realm of data engineering.
