Big Data: Data Warehouse and Data Lake

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

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

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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