Data lakes, warehouses, and lakehouses are all designed to store data.
We use data lakes to store large amounts of unstructured data. When we say unstructured data, it means images, audio, text, and other complex data structures. This data needs more processing before we consume it.
Data warehouses are designed to store processed data—mostly tabular data. Because of their structured nature, queries on this storage are speedy. Thus data teams prefer enterprise data warehouse solutions for business intelligence tools where real-time data fetching is needed.
Data lakehouses provide a centralized repository for both structured and unstructured data. It's not a data warehouse, and it's not a data lakehouse, either. These are relatively recent advancements in the big data landscape.
The subtle differences between these resources are essential for successful long-term data management. Your organization can't make accurate data-driven decisions on time without exemplary architecture.
This article will learn the differences between these three modern data architectures, their use cases, costs, and other aspects of choosing the best for your business.
From data warehouses to data lakes.
In the early 90s, organizations began to adopt data warehouses to mine data and make business decisions due to rapidly increasing volumes of data.
Data warehouses are centralized repositories for storing structured data. They are used for reporting and data analytics and usually contain historical data that has been cleansed and transformed.
A data warehouse is a single source of truth used for reporting and analytics. It usually contains historical information that has been cleansed and transformed.
The traditional data warehouse approach involved extracting data from many sources, cleansing and transforming it, and loading it into a centralized data repository. This approach is time-consuming and expensive, and it doesn't always provide the most accurate data because data can become stale by the time it is loaded into the data warehouse.
There has been a recent shift from traditional warehouses to data lakes. A data lake is a centralized repository storing structured, unstructured, and semi-structured data. Data lakes are built on top of a Hadoop cluster, a scalable storage platform that can handle large amounts of data.
The most significant advantage of a data lake is that it can provide near-real-time retrieval because the data is not transformed and loaded into a centralized repository. Data lakes also can scale more efficiently than traditional data warehouses.
Their most significant disadvantage is that they can be challenging to manage and govern. Without proper management, data lakes can become a dumping ground for all data, making it difficult to find and use the most relevant data. We need to frequently monitor the lake for poor data quality. This is less concerning in a data warehouse, thanks to its standard schema.
Also, data lakes aren't a good option for OLAP workloads requiring highly-structured data due to their unstructured nature.
Data lakes perform best when they are used alongside a data warehouse. For example, they can store raw data, while data warehouses can be used for storing cleansed and transformed data. This approach provides the best of both worlds: a data lake's flexibility and a data warehouse's reliability.
Data lake vs. data lakehouses: How it evolved?
Data lakehouses were first proposed in 2015 to combine the best of both worlds. Data lakehouses provide a centralized repository for both structured and unstructured data. The advantage of data lakehouses is that they're well-suited for OLAP and OLTP.
Data lakehouses are also designed to be more scalable and easier to manage than data lakes.
Many organizations prefer lakehouses because they could replace the need for two separate data repositories (i.e., data warehouses and data lakes). Also, data lakehouses make it easier to govern and control access to sensitive data.
Lakehouses are effectively reducing data duplications. You'd have to save some information in both places if you have a data lake and a separate data warehouse. This doesn't happen with data lakehouses because they allow all kinds of data to be indexed and stored under the same resource.
There are a few disadvantages of data lakehouses. One is that they can be more expensive to set up and maintain than lakes.
Data lakehouses are still relatively new, so there's only a little real-world experience to draw from.
The video below gives an in-depth understanding of the lakehouse approach using Amazon Redshift. It uses AWS S3 to hold data since Redshift is strictly a relational database.
Data Lake Vs. Data Warehouse Vs. Data Lakehouse
Here's the comparison between data warehouses, data lakes, and data lakehouses.
|Data Warehouse||Data Lake||Data Lakehouse|
|Primary Purpose||Business Intelligence, Reporting, Data Mining, All sorts of OLAP, Predictive analytics, training data for machine learning models||Store data from multiple sources||OLAP, OLTP; Designed for a wide variety of workloads|
|Supported Data Formats||Relational||Relational, CSV, JSON, Images, Videos, etc.||Relational, CSV, JSON, Images, Videos, etc.|
|Cost||High - Data Warehouses needs to support on-demand BI queries||Low - Data lakes uses low-cost hardwares and you can turn it on only when you need them.||High - but may be cheaper than maintaining a data warehouse alongside a data lake.|
|Flexibility||Rigid, only supports Relational tables||Store any type of data without a predefined schema||High - Supports both structured and unstructured databases|
|Performance||High - because of structured data formats||Low - because queries on raw unstructured data are slow||Good|
|Data redundancy||No (That's what relational databases are for)||High||Low|
Switching between data warehouse, data lake, and data lakehouse.
By now, you'd better understand how easy it is to move data from one architecture to another.
Since a data warehouse holds the most structured form of data, it's easy to transfer data from them to others. You may choose this option if query speed is not your concern, but the storage cost is.
But, if you have a data lake and need more query speed, you might switch to a data warehouse. Unfortunately, this isn't an easy task.
Even with only a few terabytes of data, processing unstructured data can be a monumental task.
Say you have CCTV footage of a network of grocery stores. You'd have the raw footage on your data lake. You can't convert raw data to a tabular format with a major spell.
You must decide what information to extract and store from the CCTV footage. This can be the number of people who visited a specific aisle. Or it can be the number of people who turned without buying anything.
Once you have identified the specifics, you need a team to work on it. You may take an AI-based solution to speed up things. But this is work that could span into months.
The risk here is that if you've got the right objectives defined. Once you moved them to the data warehouse, there's no going back. Unless you wish to keep your data lake parallel, you'll lose all your precious storage.
If you later find out you missed some crucial information, you can't get them.
This is why data lakehouses are an attractive option for many organizations. Inside a data lakehouse, you can convert the raw data and have the structured one inside them.
Most data scientists will prefer data lakehouses that are best for their needs if the little extra cost is okay for them.
Which data storage is suitable for transactional data?
Unfortunately, neither of these storages is for transactional data. We use data stored in a data warehouse or data lake for analytics and reporting purposes.
You can connect your Tableau or Power BI dashboards with them. If you need more control, you can create a Plotly dashboard on top of this data.
Yet, for a live application, you'd want to connect them to a regular database like Postgres.
Why this restriction?
Transactional databases are optimized for continuous changes. Transactions are also more costly operations than queries. Yet, data warehousing is great if real-time edits and updates aren't the biggest issues.
The two most prominent use cases I've seen for historical data are business intelligence reporting and machine learning model training. Data warehousing is also helpful in streaming data. Your dashboards can update in real-time with it.
In general, data lakehouses are a perfect big data storage architecture. Data warehouses are a faster-low-cost option. I may choose to have them both and access multiple systems as needed. And I try not to restrict myself to a data lake architecture.
Modern data architecture often includes data warehouses. However, some organizations also use data lake solutions like Hadoop and NoSQL databases to bridge the crucial gap of unstructured data support. The goal is to have a centralized hub that pulls together all of an organization’s essential data, making it available for analysis and decision-making.
Many organizations are turning to the cloud to build their data warehouses, taking advantage of its scalability, flexibility, and cost-effectiveness. Cloud data warehouses like Amazon Redshift and Google BigQuery have become increasingly popular.
When it comes to data architecture, there is no one-size-fits-all solution. The best data architecture for your organization will depend on your specific needs and goals.
We've discussed the different types of architecture and their merits to make an educated decision.