Data Engineering: Data Warehouse and Data Lake

Rahul S
2 min readDec 3, 2022

The term ‘big data’ implies an enormous amount and variety of structured and unstructured data that needs to be processed and updated at very high speeds.

Data Warehouse

A data warehouse facilitates the storage of structured and semi-structured data from various sources, both historical and current, that allows decision-makers to extract insights. The main purpose of an enterprise data warehouse is to identify correlations between data derived from different sources.

While databases record information, data warehouses help in data analytics.

Databases are useful for small transaction data for daily operations, like new customer entries. Data warehouses perform larger tasks, such as data mining to identify unidentified insights.

Scalability is vital for a growing business as more data and queries pile up that require structuring and analysis. With Big Data Warehouses, we get meaningful and high-quality data, which is SCALABLE and secure. It leads to an increased efficiency in our organization. For example, data warehouses help healthcare workers make predictions, create treatment reports, and exchange data with insurance agencies, laboratories, and other researchers.

On top of storing information, big data warehouses convert it into consistent formats that decision-makers require. They also get access to historical backgrounds, such as performance trends at the time and other key information that provides context to the data. This increases efficiency and quality of the analysis.

Amazon Redshift and Snowflake are good examples.

Data Lake

A data lake has/is a centralized location that stores enormous amounts of data in raw format.

Unlike data warehouses, where data formats are standardized and information is structured and moved to different corresponding folders, a data lake is just a large pool of data with object storage and a flat architecture, where each piece of data is assigned a unique identifier to streamline data retrieval.

When comparing data lake and data warehouse, the cost-efficiency of the former usually comes to mind. Because of the inexpensive object storage system and undefined formats, many companies can afford only data lakes to store and retrieve information.

Data lakes allow geneticists to collect as much data as needed to better understand the human genome.

AWS Data Lake and Cloudera are famous data lake solutions providers.

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