Data Lake vs Data Warehouse

The concept of data lake and data warehouse has been discussed thoroughly online. However, today we talk about the key differences between the two. We assume you are familiar with the concepts in detail, but let’s have a look at the two with a few small explanations to rekindle your memory.

Data Lake explained

A data Lake in plain terms can be referred to as a data storage system that can store a large amount of structured, semi structured and unstructured data. A data Lake can store every type of data in its native format with no fixed limits on account size or file. The Benefit of a Data Lake is that it offers a large amount of data for increased analytical performance and native integration.

Think about it like this, A Data Lake is a large container which in all aspects is similar to a massive lake or a river. Hence just like a lake it has many or?multiple tributaries coming in; similarly, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.

Data Warehouse explained?

Data warehousing is basically an amalgamation of different technologies and components that is meant for the strategic use of data. Data Warehouse collects and manages data from varied sources to provide meaningful business insights

Data Warehouse is a blend of technologies and components for the strategic use of data. It collects and manages data from varied sources to provide meaningful business insights. It is the electronic storage of a large amount of information designed for query and analysis instead of transaction processing. Basically, It is a process of transforming data into information.

Data Lake Concept explained

As discussed above, a Data Lake is a large size storage that places a large amount of raw data in its original format until the time it is needed. Every data element in a Data lake is given a unique identifier and tagged with a set of extended metadata tags. What this does is it offers a huge and wide variety of analytic capabilities.

Data Warehouse Concept explained?

Looking at the Data Warehouse concept, it basically stores data in files or folders which helps to organize and use these data to make strategic decisions as directed. This storage system also gives a multi-dimensional view of atomic and summary data. The important functions which are needed to be performed are Data Extraction, Data Cleaning, Data Transformation, Data Loading and Data Refreshing. Moving on let’s start to look at the differences between the two concepts

Above the line Differences between Data Lake and Data Warehouse

1. Starting off, a very important difference is that Data Lake stores all given data irrespective of what the sources are and does not take into account its structure, However Data warehouse stores the given data quantitative metrics and measures depending and variable to the attributes of the data.?
2. We will also put in the basic definition of the two, which is that Data Lake is a storage repository that stores huge structured, semi-structured and unstructured data while Data Warehouse is a blending of technologies and components which allows the strategic use of data.
3. Another big difference is that Date Lake defines the schema after the date is placed in it, but Data warehouse defines the schema way before the data is considered as stored.
4. Data Lake uses the Extract Load Transform process also known as ELT while Data Warehouse uses the Extract Transform Load process also known as the ETL process.
5. Finally it must be also mentioned that generally, Data Lake is ideal for people who want an in-depth analysis whereas Data Warehouse Is ideal for people who are more operational.

Now let's get into the differences in much more detail. We will look at the differences based on the below parameters, and we will be using the article by David Taylor as reference to understand these parameters. Refer his article by clicking on this link - https://www.guru99.com/data-lake-vs-data-warehouse.html

1. Storage
2. History
3. Data Capturing
4. Data Timeline
5. Users
6. Storage Costs
7. Task
8. Processing Time
9. Position of Schema
10. Data Processing
11. Complain

Storage

Data Lake - In the data lake, all data is kept irrespective of the source and its structure. Data is kept in its raw form. It is only transformed when it is ready to be used.

Data Warehouse - A data warehouse will consist of data that is extracted from transactional systems or data which consists of quantitative metrics with their attributes. The data is cleaned and transformed

History

Data Lake - Data Technologies used in data Lakes are relatively new. They are known to be trending technologies in 2021

Data Warehouse - Data warehouse technology, unlike big data, has been used for decades and the technology is widely available and old.

Data Capturing

Data Lake - Captures all kinds of data and structures, semi-structured and unstructured in their original form from source systems.

Date Warehouse - Captures structured information and organizes them in schemas as defined for data warehouse purposes

Data Timeline

Date Lake - Data lakes can retain all data. This includes not only the data that is in use but also data that it might use in the future. Also, data is kept for all time, to go back in time and do an analysis.

Data Warehouse - In the data warehouse development process, significant time is spent on analyzing various data sources.

Users

Date Lake - Data lake is ideal for the users who indulge in deep analysis. Such users include data scientists who need advanced analytical tools with capabilities such as predictive modeling and statistical analysis.

Data Warehouse - The data warehouse is ideal for operational users because of being well structured, easy to use and understand.

Storage costs?

Data Lake - Data storing in big data technologies is relatively inexpensive then storing data in a data warehouse.

Data warehouse - Storing data in Data warehouse is costlier and time-consuming.

Task?

Data Lake - Data lakes can contain all data and data types; it empowers users to access data prior to the process of transformed, cleansed and structured.

Data Warehouse - Data warehouses can provide insights into predefined questions for pre-defined data types.

Processing time?

Data Lake - Data lakes empower users to access data before it has been transformed, cleansed and structured. Thus, it allows users to get to their result more quickly compared to the traditional data warehouse.

Data Warehouse - Data warehouses offer insights into predefined questions for pre-defined data types. So, any changes to the data warehouse needed more time.

Position of Schema?

Data Lake - Typically, the schema is defined after data is stored. This offers high agility and ease of data capture but requires work at the end of the process

Data Warehouse - Typically schema is defined before data is stored. Requires work at the start of the process, but offers performance, security, and integration.

Data Processing

Data Lake - Data Lakes use of the ELT (Extract Load Transform) process.

Data Warehouse - Data warehouse uses a traditional ETL (Extract Transform Load) process.

Complain

Data Lake - Data is kept in its raw form. It is only transformed when it is ready to be used.

Data Warehouse - The chief complaint against data warehouses is the inability, or the problem faced when trying to make change in them.

Other Benefits

Data Lake - They integrate different types of data to come up with entirely new questions as these users are not likely to use data warehouses because they may need to go beyond its capabilities.

Data Warehouse - Most users in an organization are operational. These types of users only care about reports and key performance metrics.

As such you can understand the differences between the two concepts. However there are no preferences given in this article. If you want to know about these concepts you can also read up more on the articles written by David Taylor, who has explained more about the two concepts separately. Moreover if you want to learn more about similar concepts and areas you can visit go back to our main blog page on www.earltech.biz. Catch you in the next one.