The Difference Between Data Warehouses, Data Lakes, and Data Lakehouses.

April 16, 2022

The Difference Between Data Warehouses, Data Lakes, and Data Lakehouses.

Data lakes, data warehouses, and data lakehouses are all designed to store data. We use data lakes to store large amounts of unstructured data. Data warehouses are designed to store structured data. Data lakehouses provide a centralized repository for both structured and unstructured data.

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 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.

Popular managed cloud data warehouse solutions include Azure Synapse Analytics, Azure SQL Database, and Amazon Redshift

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 shift from traditional data warehouses to data lakes in recent years. A data lake is a centralized repository that can store 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 biggest 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.

The biggest disadvantage of data lakes 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.

Also, data lakes aren't a good option for OLAP workloads requiring highly-structured data due to their unstructured nature.

Popular data lake solutions include AWS Data Lake, Databriks, Snowflake, and Azure Data Lake.

Data lakes perform best when they are used alongside a data warehouse. For example, data lakes can store raw data, while data warehouses can be used for storing cleansed and transformed data. This approach provides the best of both worlds: the flexibility of a data lake and the reliability of a data warehouse.

From data lakes to data lakehouses.

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.

There are a few disadvantages of data lakehouses. One is that they can be more expensive to set up and maintain than data lakes.

Data lakehouses are still a relatively new concept, so there's not a lot of real-world experience to draw from yet.

The video below gives an in-depth understanding of the lakehouse approach using Amazon Redshift. It uses AWS S3 to store data since Redshift is strictly a relational database.

Data Lakes Vs Data Warehouses Vs Data Lakehouses

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 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

Final thoughts

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 the cloud's 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.

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