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<\/figure><\/div>\n\n\nThe 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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
From data warehouses to data lakes.<\/b><\/h2>\n\n\n\n
In the early 90s, organizations began to adopt data warehouses<\/a> to mine data and make business decisions due to rapidly increasing volumes of data.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n Popular managed cloud data warehouse solutions include Azure Synapse Analytics<\/a>, Azure SQL Database<\/a>, and Amazon Redshift<\/a>.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n There has been a recent shift from traditional warehouses to data lakes.<\/a> 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.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n Related:<\/b> 11 Advantages of Cloud Databases Over On-Premise Databases.<\/i><\/b><\/a><\/p>\n\n\n\n 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.<\/p>\n\n\n\n Also, data lakes aren’t a good option for OLAP workloads requiring highly-structured data due to their unstructured nature.<\/p>\n\n\n\n Popular data lake solutions include AWS Data Lake<\/a>, Databriks<\/a>, Snowflake<\/a>, and Azure Data Lake<\/a>.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n <\/p>\n\n\n\n Related:<\/b> How to Improve Data Quality Without Firefighting Them?<\/i><\/b><\/a><\/p>\n\n\n\n Data lakehouses were first proposed in 2015 to combine the best of both worlds. Data lakehouses<\/a> 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.<\/p>\n\n\n\n Data lakehouses are also designed to be more scalable and easier to manage than data lakes.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n There are a few disadvantages of data lakehouses. One is that they can be more expensive to set up and maintain than lakes.<\/p>\n\n\n\n Data lakehouses are still relatively new, so there’s only a little real-world experience to draw from.<\/p>\n\n\n\n The video below gives an in-depth understanding of the lakehouse approach using Amazon Redshift<\/a>. It uses AWS S3 to hold data since Redshift is strictly a relational database.<\/p>\n\n\n\nData lake vs. data lakehouses: How it evolved?<\/b><\/h2>\n\n\n\n