Data lake  and data warehouses are both widely used for storing big data, but they are not interchangeable terms. A data lake is a vast pool of raw data, the purpose for which is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.

What is a Data Warehouse, exactly?

A data warehouse is a collection of technologies and components used to make strategic data decisions. It gathers and maintains data from a variety of sources in order to give actionable business insights. It refers to the electronic storing of a huge volume of data for inquiry and analysis rather than transaction processing. It is the transformation of data into information.

A Data Warehouse is a database that exclusively contains data that has been pre-processed. The data structure is well-defined here, it’s optimised for SQL queries, and it’s ready to be used for analytics. The Data Warehouse is also known as a Business Intelligence Solution or a Decision Support System.

Characteristics of data warehouses

Large amounts of current and historical data from diverse sources are stored in data warehouses. They hold a variety of data, ranging from raw ingested information to highly curated, cleansed, filtered, and aggregated information.

Data is moved from its original source to the data warehouse using extract, transform, and load (ETL) operations. Data in the data warehouse may not reflect the most up-to-date condition of the systems since ETL operations move data on a regular frequency (for example, hourly or daily).

A pre-defined and fixed relational structure is common in data warehouses. As a result, they function effectively with organised data. Semi-structured data is also supported by some data warehouses.

Business analysts can connect data warehouses to BI tools after the data is in the warehouse. Business analysts and data scientists can use these tools to study data, find insights, and generate reports for business stakeholders.

What are the benefits of using a data warehouse?

When you need to store significant amounts of historical data and/or undertake in-depth analysis of your data to develop business information, data warehouses are a suitable alternative. Analyzing data in data warehouses is relatively simple due to their highly structured nature, and it can be done by business analysts and data scientists.

Note that data warehouses are not intended to satisfy the transaction and concurrency needs of an application. If an organization determines they will benefit from a data warehouse, they will need a separate database or databases to power their daily operations.

Data warehouse examples

Examples of data warehouses include:

  • Amazon Redshift.
  • Google BigQuery.
  • IBM Db2 Warehouse.
  • Microsoft Azure Synapse.
  • Oracle Autonomous Data Warehouse.
  • Snowflake.
  • Teradata Vantage.

What is a Data Lake, exactly?

A Data Lake is a large-scale storage repository for structured, semi-structured, and unstructured data. It’s a location where you can save any type of data in its original format, with no restrictions on account size or file size. It provides a significant number of data for improved analytical performance and native integration.

A data lake is a huge container that looks a lot like a lake or a river. Similar to how a lake has various tributaries, a data lake has structured data, unstructured data, machine-to-machine communication, and logs flowing through in real-time.

A data lake’s primary function is usually to analyse data in order to acquire insights. Organizations, on the other hand, occasionally utilise data lakes just for the purpose of cheap storage, with the hope that the data will be used for analytics in the future.

Is a data lake the same as a database?

“Is a data lake a database?” you might think. A data lake is a storage location for data from several sources, including databases. A data lake can also serve as the database’s storage layer, thanks to modern tools and technology. Tools such as Starburst, Presto, Dremio, and Atlas Data Lake can provide a database-like view of your data lake’s data.

Characteristics of the data lake

Large amounts of structured, semi-structured, and unstructured data are stored in data lakes. Everything from relational data to JSON texts, PDFs, and audio files can be stored in them.

Because data does not need to be changed before being added to the data lake, it can be added (or “ingested”) quickly and without any prior forethought.

The principal users of a data lake can vary depending on the data’s structure. When data is better structured, business analysts will be able to get insights. Data analysis will almost certainly require the expertise of developers, data scientists, or data engineers when the data is more unstructured.

Data lakes’ adaptability allows business analysts and data scientists to search for unexpected patterns and insights. Users can solve problems that they may not have been aware of when they first set up the data lake because of the raw nature of the data and its magnitude.

Data lakes can be processed using a number of OLAP systems and visualised using business intelligence tools.

What are the benefits of using a data lake?

Data lakes are a low-cost method of storing large amounts of data. When you want to obtain insights from your current and historical data without having to alter or move it, use a data lake. Machine learning and predictive analytics are also supported by data lakes.

Concept of a Data Warehouse:

Data Warehouse organises and uses data to make strategic decisions by storing it in files or folders. A multi-dimensional view of atomic and summary data is also provided by this storage technology. The following are the critical functions that must be carried out:

  • Extraction of information
  • Cleaning of data
  • Transformation of data
  • Data Refreshing and Loading


Data Lake stores all data, regardless of source or format, whereas Data Warehouse saves data in quantitative measurements with their associated qualities.

Data Lake is a storage repository for large amounts of structured, semi-structured, and unstructured data, whereas Data Warehouse is a combination of technology and components that enables data to be used strategically.

The schema of a Data Lake is defined after the data has been stored, whereas the schema of a Data Warehouse is defined before the data has been stored.

The ELT (Extract Load Transform) process is used in the Data Lake, while the ETL (Extract Transform Load) process is used in the Data Warehouse.

When it comes to Data Lake vs. Warehouse, Data Lake is better for in-depth analysis, whereas Data Warehouse is better for operational users.

Concept of a Data Lake:

A Data Lake is a big storage repository that stores a lot of raw data in its original format until it’s needed. A unique identification is assigned to each data element in a Data lake, along with a set of enhanced metadata tags. It has a broad range of analytic capabilities.

A data lake is a centralised repository that can hold both organised and unstructured data at any scale. You can use dashboards and visualisations to make better decisions, and you can run several sorts of analytics—from big data processing, real-time analytics, and machine learning—without needing to first arrange the data.

What is the purpose of a data lake?

Companies that are successful in generating business value from their data will outperform their competitors. Organizations who deployed a Data Lake outperformed similar enterprises by 9% in organic revenue growth, according to an Aberdeen survey. These leaders were able to perform new forms of analytics, such as machine learning, using new data sources in the data lake, such as log files, data from click-streams, social media, and internet-connected gadgets. By attracting and retaining customers, increasing productivity, proactively maintaining equipment, and making educated decisions, they were able to recognise and act on chances for business growth faster.

Data lake examples

Data lakes can provide storage and compute capabilities, either independently or together.

The following are examples of technology that provide flexible and scalable storage for building data lakes:

  • AWS S3
  • Azure Data Lake Storage Gen2
  • Google Cloud Storage

Difference between Data Lake and Data Warehouse

1.Storage :

  • Data lake: All data is stored in the data lake, regardless of its source or structure. The data is stored in its unprocessed state. When it is ready to be used, it is converted.
  • Data Warehouse: Data extracted from transactional systems or data consisting of quantitative measures and their properties will be stored in a data warehouse. The information has been cleansed and changed.

2.history :

  • Data lake: The employment of big data technologies in data lakes is still relatively new.
  • Data Warehouse: Unlike big data, the data warehouse concept has been around for decades.


  • Data lake: Captures semi-structured and unstructured data and structures in their original form from source systems.
  • Data Warehouse: Captures structured data and organises it according to defined standards for data warehouse purposes.


  • Data lake: All data can be stored in a data lake. This includes not only current data but also data that may be used in the future. Also, data is saved indefinitely so that it can be analysed in the future.
  • Data Warehouse: Various data sources are analysed extensively during the data warehouse construction process.


  • Data lake: The data lake is appropriate for those that perform in-depth analysis. Data scientists, for example, require advanced analytical techniques that include predictive modelling and statistical analysis.
  • Data Warehouse: Because it is highly structured, easy to use, and understand, the data warehouse is perfect for operational users.


  • Data lake: The cost of storing data in big data technology is less than that of storing data in a data warehouse.
  • Dara warehouse: Data warehouse storage is more expensive and time-consuming.


  • Data lake: All data and data kinds can be stored in data lakes, which allows users to access data before it is transformed, cleansed, and structured.
  • Data Warehouse: Pre-defined inquiries for pre-defined data categories can be answered using data warehouses.


  • Data lake: Users can access data in data lakes before it has been transformed, cleansed, or structured. In comparison to a traditional data warehouse, it allows consumers to get to their results faster.
  • Data warehouse: Pre-defined inquiries for pre-defined data kinds are answered by data warehouses. As a result, any updates to the data warehouse took longer.


  • Data lake: The schema is usually developed after the data has been stored. This provides a great level of flexibility and convenience of data collecting, but it necessitates labour at the end of the process.
  • Data Warehouse: Schema is usually defined before data is saved. Work is required at the start of the process, but performance, security, and integration are all advantages.

10. Data processing:

  • Data lake: The ELT (Extract Load Transform) method is used in Data Lakes.
  • Data Warehouse: A standard ETL (Extract Transform Load) procedure is used in a data warehouse.


  • Data lake: The data is stored in its unprocessed state. When it is ready to be used, it is converted.
  • Data Warehouse: The impossibility, or problem, with trying to make changes in data warehouses is the most common complaint.


  • Data Lake: Because these customers are unlikely to employ data warehouses because they may need to go beyond their capabilities, they combine diverse sorts of data to come up with totally new questions.
  • Data Warehouse The majority of users at a company are working. These users are only interested in reports and key performance indicators.

A data lake is a highly scalable storage repository that stores enormous amounts of raw data in its natural format until it is needed. Data in a data lake can come from a variety of sources and formats, including structured, semi-structured, and unstructured data. A flat architecture is used to store data, which can then be queried as needed. A data lake is an effective solution for companies that need to collect and store a lot of data but don’t need to process and analyse it all right away. It can load and store large amounts of data quickly and without transformation.

Traditional data warehouses, on the other hand, process and transform data in a more structured database environment for advanced querying and analytics. Data lakes are frequently thought of as supplements to data warehouses. Cloud data warehouses and data lakes, on the other hand, are becoming the favoured solution as businesses battle with ever-increasing data volumes. Only the cloud can provide the economies of scale, data security, reliability, and cheap maintenance that are required to handle this data explosion.

Which should you use: a data lake or a data warehouse?

The data you collect will be primarily unstructured if your company deals with healthcare or social media (documents, images). The amount of structured data is really small. As a result, the data lake is an excellent fit because it can manage both types of data and provides additional analytical flexibility.

If your web firm is divided into several pillars, you’ll want to have dashboards that summarise all of them. In this scenario, data warehouses will aid in making educated decisions. It will ensure that the data is of high quality, consistent, and accurate.

The majority of the time, businesses utilise a combination of the two. They use the data lake for data exploration and analysis before moving the rich data to data warehouses for quick and advanced reporting.


Although data warehouses and data lakes appear to be simple concepts, there is much to consider before opting on one over the other (or deciding to use both).

The following are the most important factors to consider:

  • How important is data to your company? If data is one of your top priorities, storing as much of it as possible in a lake is likely to be beneficial.
  • How mature and well-understood your use case is: if you’re working on a well-understood topic and your data fits neatly into specified schemas, there’s no need to reinvent the wheel.

Both systems are important in different contexts, and neither is going away anytime soon, but data warehouses have been around for a long time, whilst data lakes are relatively new.

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