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Data Lake Vs Data Warehousing: Key Differences You Should Know

Data has become a very crucial part of every company. Data has several associated ingredients to acquire its most value, such as gathering extensive volumes of data, processing, analysing, and storing it. As an organisation requires a lot of data, storing them properly is a significant task. Data lake and data warehouse are two prominently known data storage models that have proven to be useful for organisations that use these methodologies.

Both data storage models are primarily used for storing Big Data. Sometimes an organisation uses data lake and data warehousing simultaneously as and when required. However, the two techniques are very different from each other, and they have been developed with different purposes. A data lake and data warehouse differ in structure and capabilities for storing data.

Let’s dive into understanding these two data storage models and the key differences between data lake and data warehouse

What is a Data Warehouse?

A data warehouse is a type of data management system that possesses abilities to support Business Intelligence (BI) pursuit, mainly data analytics. Data warehouses contain a lot of historical data and intend to perform queries to carry out the process of data analysis. Data warehouse as a storage model is expected to reach a growth rate of 22.56% by 2026. This is three times the market size in 2021 which is $4.7 billion.

Data warehousing technique is mostly used by medium and large size organisations. It is a convenient technique to share significant data across various departments in an organisation through databases. Data warehouses regularly attract data and are generally sourced from multiple places. 

A data warehouse is a great way to channelise and consolidate large amounts of data. It mainly stores information about customers, products, services, orders, inventory, etc.

What is Data Lake?

A data lake is a central storage repository that stores big data in a raw format. Data lake possesses the ability to store unstructured, semi-structured and structured data, and this provision offers the flexibility of the type of storage to the users. A data lake is an advantage for organisations as the global data Lake market size is expected to reach a growth rate of 20.6% by 2027, implying its rapid market implementation.

Data Lake uses metadata and identifiers while storing data, where the metadata tags allow a data lake to retrieve data quickly. A cluster primarily writes the configuration of data lakes of hardware with more scalability. So the data lake system dumps the data into a storage space in case it is required later.

However, a data lake does not analyse or process the data immediately. It is a quick storage method generally used by data scientists.

Difference Between Data Warehouse and Data Lake

Data warehouse vs data lake is a buzzword in today’s IT industry. These are the two most popular modes of storing and processing big data, but both have certain distinctions. The difference between data lake and data warehouse can be stated as follows:

Basis Data lake Data warehouse
Storage Any type of data can be stored in our data lake regardless of its structure or source. It deals with raw data and transforms it only when required. Data warehouse deals with the data that consist of quantitative metrics and has been attracted from transactional sources. The data is periodically transformed.
History The data lake storage methodology uses big data and is a relatively new concept.  Data warehouses have been prevalent for many years, unlike big data.
Data capturing It has the ability to capture structured, semi-structured and unstructured data in their original format. It only captures structure data and streamlines it for warehousing purposes.
Data timeline A data lake stores all data, whether it is required in the present or maybe required in future. A Data lake retains data permanently to be used for analysis.  Data warehouse spares a significant amount of time for processing and analysing various data sources and deciding what to store.
Users Best for users performing deep analysis. For instance, data scientists, statisticians, engineers etc. Best for operational users. For instance, entrepreneurs, business owners, stakeholders etc.
Cost A data lake is comparatively less costly when it comes to storing data using this methodology. A data warehouse is relatively costly and consumes more time to store data.
Task It allows users to access the data even before clearing, transforming and structuring it. It allows users to obtain insights into predefined questions for predefined data types.
Processing time It generates faster results and has less processing time. Data warehouses need more processing time, especially when changes are made in them.
Drawback Sometimes, the raw form of data can be very hard to understand. Hence, no immediate simplification is a complaint against data lakes. The major drawback of data warehouses is the difficulty encountered when attempting to make changes in them.
Data processing Data lakes use ELT (Extract Load Transform). Data warehouses use a traditional form of ELT (Extract Load Transform).

Data Lake Tools

Here is the list of the most prominently used data lake tools:

Azure Data Lake Storage

This popularly used data lake tool helps create single and unified data storage space. The Azure Data Lake tool is beneficial since it provides precise data authentication along with advanced and secure facilities. The data can be transferred to specific databases to send the information only to intended departments or individuals. This tool is best for a massive amount of queries.

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AWS Lake Formation

With the help of the tool, it is very simple to set up a data lake. The AWS-based machine learning services offer consistent analysis functions. It can easily identify the data access history with the assistance of a database that helps search everything easily. 

Qubole

Qubole is an open-format data lake tool that is widely accessible and has open standards. The main advantage of this tool is that it offers ad hoc analytics services and activities. It performs the function of amalgamating data pipelines which is important for gaining real-time insights.

Infor Data Lake

This tool has the ability to attract and collect data from numerous sources and process them immediately to retrieve valuable and meaningful information. This data storage system does not allow the drenching of data, and it is the most prominent advantage this tool offers.

Intelligent Data Lake

This data Lake tool is Hadoop based. It ensures that uses do not have to be very technical as it does not require a large amount of coding to retrieve results. It runs queries on large-scale data and helps consumers to obtain maximum value.

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Data Warehouse Tools

The top-rated data warehouse tools are enumerated as follows:

Amazon Redshift 

It is a great cloud-based data warehousing tool that offers speedy data analytics. It does not need any extra operational overhead and can execute multiple concurrent queries.

Microsoft Azure

It is a node-based data warehouse tool that offers the facility to process large amounts of data at the same time. It helps to obtain and analyse business insights faster and in an accurate way.

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Google BigQuery 

This famous data warehousing tool integrates well with TensorFlow and Cloud ML, which enables it to build powerful models based on AI. 

Snowflake

This tool offers the function of performing data analytics from various structured and unstructured sources. This tool has a precise architecture that offers separate processing and storage facilities. That is why the CPU resources can be adjusted according to the activities of the users.

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Micro Focus Vertica

It is an SQL-based data warehousing tool compatible with Cloud platforms like AWS, Azure etc. it is specifically designed with a built-in analytics ability for time series functions, machine learning activities and so on.

Amazon DynamoDB 

This tool is known to have a format that allows fast scaling of data. It can scale the capacity of its query process to 10 or 20 trillion requests on a daily basis over data petabytes.

Which One is Right For You?

The data warehouse model is usually about the potential of ingesting useful data from RDBMS. It is all about performance functionality and BI applications. Whereas, the data lake model is less restrictive and gives the freedom to work on a scheme basis. 

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Thus, companies find data lakes more suited for their storage system. 

In case you want to learn the detailed concept of data storage methodologies, we’ve got you covered! upGrad’s Master of Science in Data Science will enlighten you about data science and all its related concepts, including data lake and data warehouse.

With the best mentors and modules that upGrad offers, this course is well-equipped to impart the understanding of the concept of data warehouse vs data lake to its learners. It enables learners to choose the right data storage methodology for their organisation.

Q. What is a data lake?

A data lake refers to a centralised storehouse for data of all types- structured, unstructured or semi-structured. The storehouse stores data in its authentic form to further enable organisations to reap benefits from it on their own terms.

Q. Are data lake and data warehousing interchangeable terms?

No, data lake and warehousing are two different approaches to storing big data to later analyse, evaluate, clean and process in order to obtain valuable insights for businesses. They both comprise a different set of tools to store maximum data.

Q. Can data lake replace data warehouse?

Data lake and warehouse are not an alternative to each other. Therefore, replacing one with the other would not provide you with similar results. While some technologies offered under each may overlap, most of the assistance provided under the two varies.

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