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Vietnamese The data warehouse has provided great value to businesses in unlocking the full potential of big data. Recently, a new emerging technology – Data Lake – is changing the way we approach and utilise data once again.
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3. Data lake vs. Data warehouse
In order to clear up any confusion and distinguish these two concepts, let’s first take a look at the definitions.
What is a data lake?
A data lake is a central storage repository that holds a large amount of raw data for later use. Since data can be stored as-is, your business doesn’t have to waste effort on converting, structuring and filing data until it is needed.
What is a data warehouse?
A data warehouse is also a data repository for businesses, mainly used to provide reports and data analysis. Data stored in the warehouse sometimes have to go through an extracting, transforming, and loading (ETL) process before being added to the repository.
Data Lakes vs Data warehouses - 4 main differences
To put it in simple terms, data warehouses store transformed and structured data from various enterprise sources. This data is ready to be used for other purposes, especially reporting and analysis.
Data lakes store unstructured data which is loaded in its raw state. Further transformations will be required when this data is needed for other purposes.
Infographic: 4 Steps to Automate Enterprise Data ManagementEach has its own way of handling data and providing results.
1. Data types
As mentioned above, data warehouses consist of data extracted from transactional systems and quantitative metrics to support the analytics of performance and business status. Data warehouses require a highly structured data model to define which data is loaded into the data warehouse and which data is not.
In data lakes, all kinds of data from the source system are loaded including data sources that may be denied in data warehouses, such as web server logs, sensor data, social network activity, text and images, etc.
Data lakes can even store data that is currently not in use but might become helpful in the future. This is made possible by low-cost storage solutions such as Hadoop.
2. Schema
Data warehouses apply the “Schema on Write” approach, which means its model is highly structured for the main purpose of reporting. In details, this process requires a considerable amount of time to analyse data sources, understand business processes, and filing data, result in a predefined system for data storing.
Data lakes keep data in its original state; when data is needed to answer a business-related question, only the relevant data would be provided, and that smaller set of data can then be analysed to help answer the question. This approach is known as “Schema on Read”, effectively saves time and cost for your organisation.
Read more: How Grab Uses Data Analytics to Refine New Products
3. Flexibility
Since data warehouses are a highly structured repository, they are laborious to make changes to the structure according to the business’ needs. The changing process requires lots of complex processes, thus, making it slow and expensive.
Data lakes, on the other hand, take advantage of the flexibility of data, because data is stored in its raw format and always accessible, allowing reconfiguration on the fly.
4. Users
Data warehouses, which are familiar to businesses and users, easily fulfil businesses’ operation needs, namely their performance reports, metrics or data statistics. Provided its well-structured, easy-to-use and mainly aimed to answer user’s questions, the data warehouse meets the needs of operational stages.
A data lake is more suited to users who are tasked with deep analysis – such as data scientists. Given the broad data types, they can mash up many different types of data and come up with entirely new questions to be answered.
Who should use data lakes?
Depend on their natures and abilities, data warehouse seems to be a better choice for organisations looking to capitalise on data. In the meantime, the data lake allows users to deeply exploit the possibilities that data can bring; however, this might be a difficult task for the average end-users whose skills are not advanced enough.
Both technologies will continue to evolve. And chances are, providers would come up with a hybrid solution to make data utilisation faster, more scalable and more reliable.
Nevertheless, the bigger question is who are data lake vendors? What should you look for before adapting an advanced data management platform like data lakes? Discuss your concerns with TRG's digital transformation experts today!
