Both a Data Lake and a Data Warehouse are options for storing data. While traditionally data warehouses have been the preferred storage method of organizations, recent advancements and cloud computing have seen a rise in data lakes. While both storage systems, one is not a replacement of the other, and both have their place in the modern data framework.
A data warehouse is made of databases, which hold tables. These tables are structured – they have rows and columns, each with specific data types and rules to follow. However, as the data field has advanced, so has the volume of data we collect and our abilities to analyze it. Our ability to capture data has evolved resulting in huge amounts of data being kept. When looking at massive datasets, Data Warehouse can be a costly option to store so much data.
The rise of data science, helped by the availability of more compute power and cloud computing, has let us analyze things we never could before. Examples include extracting speech from audio, computer vision to understand and analyze video or pictures, or automatically classifying email messages. Yet audio, images and video or emails are unstructured data in nature. They cannot be put in a table like our traditional data, nor can be separated into rows and columns.
A data lake gives a new option to store this data, both in size and unstructured nature. While different skills are needed to be able to understand and use this data, it enables an organization to access rich data for analysis, data science and machine learning.
What is a Data Lake?
A data lake is an organic system in which data can be stored in its original raw format to be analyzed and restructured later. A data lake is like a storage repository which stores large amounts of structured, semi-structured and unstructured data. With the amount of file types and data constantly flowing through an organization's fingertips, it's important to have a handle on the type of data your system consumes and how it can benefit your organization through predictive analytics and more. Data lakes can store structured data AND unprocessed data, meaning data warehouses (more on that below) can exist inside data lakes. Data lakes incorporate data from all sources spread across an enterprise
Benefits of a Data Lake
Data lakes are scalable, meaning they can hold large amounts of data without the risk of losing storage space. The conversation naturally pivots to data lakes when we start having conversations around big data solutions. Given that data lakes are cloud-based, having a data lake in your repository is a smart investment to pair with cloud migration.
The cloud-based scalability of a data lake then allows for raw data to be quickly inputted into the system and interpreted later. This can be useful when you know you're going to need a particular set of data later, but don't have the time or capacity to interpret said data yet.
What are the costs/trade-offs of incorporating a data lake?
Cost wise, having a data lake can be an affordable option for storing large amounts of information. Data lakes are typically used by specialists that are highly skilled in the interpretation and analytics of raw and unstructured data, therefore data lakes are not suitable for all broad-span users. This shouldn't deter an organization from using data lakes, however, because along with specialists using the system, you can also incorporate machine learning to analyze and interpret data for you. This becomes a very compelling option as more and more organizations look to machine learning to drive operational improvements.
What is a data warehouse?
A data warehouse, or database, can be a subset of a data lake or a standalone system in which data is stored in a uniformed, structured, and consistent & structured data for accessibility to a broad range of users. Data warehouses can handle unstructured data but there's lack of efficiency in doing so. Data Warehouses store historical data. This is used by Data Analysts and Business Analysts to make business decisions.
Data warehouses are particularly useful for Data Analysis & strategic decisions by business users. Traditional Data Warehouses uses the ETL ( Extract, Transform and load) Process where data is extracted from different sources and complied in Data warehouse system.
Differences between a Data Warehouse and a Data Lake
Data warehouses are much more cut-and-dry than data lakes, and the accessibility of data makes it easier to be interpreted and used right away once it's inputted. However, because data is uniform and consistently created in a data warehouse, there is much more front-end work done on data before it even reaches the system to be stored. Data undergoes reconstruction and then is inputted.
The Data Lake Approach vs. The Data Warehouse Approach
There isn’t one source of truth when it comes to whether or not a data lake is better or worse than a data warehouse. It is largely dependent on how your organization runs currently, and where you want to go with your data. However, data lakes are better suited for organizations that have or plan to migrate to the cloud.
Both Data Lakes and Data warehouses have their own benefits, but when the search for a new query arises, Data lakes can be more helpful to organizations. Data warehouses require more time in analyzing the data and storing it in the structured format for analysis. With respect to speed of retrieving data, Data lakes are quicker compared to data warehouse which requires more time for analyzing the data stored in the system.