In the world of data management, data lake and data warehouse are not merely two terms but methodologies to store big data. Since these are used for data storage purposes, they are often confused with each other and used interchangeably, which is not true. Both data management techniques are used for storing high-level data, but there is no other similarity between them.
Today, we will cover the key differences between data lake vs data warehouse, what they mean and what the benefits each of them offers. Moreover, this guide also provides insights into choosing the right data management technique for your organization.
Data Warehouse Vs Data Lake: 8 Key Differences
While there can be many differences between a data lake and a data warehouse, we’ll walk you through the most important ones.
· Storage of Data
A data lake is used to store data in unstructured and raw forms. The data in a data lake can be stored for a continued or indefinite period and can be used immediately and whenever there’s a need for it in the future. This is the reason why data lakes require much larger storage space or capacity.
It is also suited for multiple analysis needs using machine learning, deep learning, and more. However, the raw data, which is often not in the best form, turns into data swamps due to the unavailability of proper policies in place.
On the other hand, in a data warehouse, the data is stored in a structured form. The data here is already cleaned and processed and is ready for different analysis cycles depending on the business requirements and needs.
By storing only the processed or structured data, a data warehouse helps save costs by not storing the data that is of no use. Furthermore, the processed data is much easier to comprehend, and hence, it is favorable to have it when dealing with large audiences.
Data lakes are usually used by engineers and data scientists who have a deeper understanding of data, its structure, and other key factors. Because there is a large amount of unstructured data, non-technical individuals don’t use data lakes, and experts with technical knowledge prefer studying raw data to gain a deeper understanding of the data, work on it and extract meaningful information from it.
On the flip side, data warehouses are mainly used by business professionals and managers using the methodology to extract useful information from structured data. The data here is already treated and cleaned and can be used to answer pre-known questions and analyze the insights accordingly. Structured data is usually represented using data tables, spreadsheets, and charts.
The accessibility of data management techniques pertains to using them as a data repository and not just using them. As data lakes are unstructured, they are easy to play with and modify. Moreover, any changes can be promptly reflected because data lakes have very few limitations in this regard.
On the contrary, the data warehouses are structured. By their design, they have the advantage that whenever there is a need to understand, it is easy and convenient to do so. However, the structure of a data warehouse makes it extremely tough to modify or manipulate.
Data lakes are used for a variety of purposes, including predictive analytics, machine learning, business intelligence (BI), big data analytics, data visualization, and more. The unstructured form of the data makes it ideal to use for this analysis. Data lakes provide more options for data engineers and data scientists because of the leverage to work with data.
However, the data warehouse offers much fewer options to work with, mainly because the data is already processed, and there is little to no room for anyone to play around with it. Still, business intelligence (BI), data analytics, and data visualization techniques can be used on the data.
All the data pieces stored in a data lake don’t necessarily have a clear purpose. Raw data is added to a data lake, sometimes with a purpose in mind and sometimes just to store it. This means that the data has less filtration and organization than the data warehouse.
On the other hand, the data stored in a data warehouse is processed, which means it has been treated and cleaned with a specific purpose in mind. Here, the purpose is clear and necessitates that the data stored is necessary and that no space is wasted.
· Definition of Schema
The schema of any data management or storage system is defined after the data has been stored in a data lake. This not only makes the whole processing of storing the data faster but also ensures that it is accessible seamlessly based on the schema.
While in a data warehouse, the schema is defined prior to the storage of data. This increases the time needed to process the data. However, once the process is completed, the data is fully available to be used with confidence and consistency throughout the organization.
· Data Processing
The data lake uses the ELT (Extract, Load, Transform) technique to work around the data. Through this procedure, the extracted data from any source is stored in the data lake and cleaned or structured only when needed the most.
On the other hand, the data warehouse uses the ETL (Extract, Transform, Load) technique. Through this procedure, the sources are used to extract the data, after which it is scrubbed and then structured to be made ready for business analysis.
· Data Storage Costs
Data storage costs are inevitable, and with a data lake, they are quite inexpensive. Moreover, data lakes consume much less time to manage. Because of this, the operational costs reduce significantly. This makes data lakes a better choice between data lake and data warehouse discussion.
While data warehouses are certainly on the pricier side, data warehouse requires more time to manage the data, which eventually increases the overall operational costs. Data storage costs can be a decisive factor for many in this regard.
Data Lake & Its Benefits
The answer to “what is a data lake?” is simple. A data lake is a storage space where all data of your organization is stored. This data can be both structured and unstructured. The concept of the data lake is like a large pool where water comes in from multiple sources, including treated and untreated.
A data lake can usually handle heaps of data produced by organizations without the need to structure it. All kinds of data stored in a data lake are used to build data pipelines so that they can be used for multiple data analytics tools to extract useful insights that enable informed decision-making.
Advantages of Data Lake
It has already been established that data lakes are not for non-technical individuals. Data scientists and engineers with subject expertise and knowledge of data analytics tools can better leverage the benefits of a data lake quicker and with more accuracy compared to a data warehouse.
Here are a few data lake benefits:
- Huge volumes of data (structured and unstructured), including logs, transactions, and more, can be stored effectively
- Data lakes allow quick accessibility of data for various purposes because of being kept in the raw/unstructured state
- Data can be analyzed on more touch points to gain a competitive advantage by accessing the latest insights and information
Examples of Data Lake
Here are a few examples of technologies that allow building scalable and sustainable storage for building data lakes:
- Google Cloud Storage
- AWS S3
- Azure Data Lake Storage Gen2
Some more technologies that you can use for querying and organizing data in data lakes:
- MongoDB Atlas Data Lake
- Databricks SQL Analytics
- AWS Athena
Data Warehouse & Its Benefits
A data warehouse is also a repository like a data lake, but with a few fundamental differences. The data stored in a data warehouse is unified and structured. All the data stored here is there to support specific business requirements of data analytics and intelligence.
Whenever there is a need for quick decision-making, analysis, and reporting, the data in a data warehouse can be immediately used to serve the purpose based on an organization’s needs. Unlike data lake, business professionals can easily use the data stored in a data warehouse.
Benefits of Data Warehousing
Data warehouse offers multiple benefits to organizations, especially for those looking to leverage business intelligence (BI) and analytics. In order to make the most of the data stored in a data warehouse, it needs to be properly cleaned, after which it can be used to extract invaluable information.
Here are a few data warehouse benefits:
- Data warehousing requires no preparation, making it convenient for engineers, analysts, and business professionals to utilize the data
- The data at disposal is generally more accurate and can be accessed quickly, which can be used to extract the correct information
- Data stored in a data warehouse acts as a source of information, ensuring trust in business insights better
Examples of Data Warehouse
Here are a few prominent examples of a data warehouse:
- Microsoft Azure Synapse
- Amazon Redshift
- Google BigQuery
- IBM Db2 Warehouse
- Oracle Autonomous Data Warehouse
- Teradata Vantage
Data Warehouse Vs Data Lake: Which One Does Your Company Need?
It is often tough to find out the preference in the choice between data lake vs data warehouse. Data lakes emerged to meet the need to extract useful information from unstructured, raw data, but that doesn’t mean data warehouses are of any less importance. These play a crucial part in day-to-day analytics needs for business users.
The data lake vs data warehouse debate has no unified answer. It depends on the industry and the diverse requirements they might have. To understand which one is right for your company, here are a few questions that you must answer first.
- Where am I currently storing my data?
- Do I unstructured, semi-structured, or fully structured?
- Should I have a fixed scheme from the start, and will it benefit me?
Answering these questions will allow you to understand what your company needs based on the analysis and requirements. Determining the right choice between a data warehouse and a data lake can significantly help your company move forward.
Choosing between a data lake and a data warehouse is not simple, but if you have a clear understanding of what you need and know the difference between both in terms of structure, processes, and targeted users – the decision becomes straightforward.
If you are still confused about which one is the right choice for your organization, contact us at Veraqor, and we assure you that we will leave no stone unturned to get things right for you.