Comparing Data Lakes and Traditional Warehouses for BI
By Tom Nonmacher
In our fast-paced digital world, managing data efficiently and making it accessible for Business Intelligence (BI) has become a critical aspect of business. Traditionally, companies relied on data warehouses to manage and store data. However, with the advent of Big Data, a new concept called Data Lake has emerged. In this blog post, we will explore the key differences between a Data Lake and traditional Data Warehouses, using technologies such as SQL Server 2012, SQL Server 2014, MySQL 5.6, DB2 10.5, and Azure SQL.
A Data Warehouse is a large set of structured data stored in a database designed to facilitate complex queries and analysis. It's a central repository of data that has been cleansed, transformed, and structured into a schema-on-write format. This means the data is defined first before being loaded into the warehouse. Here is an example of how data is loaded into a SQL Server 2014 data warehouse:
BULK INSERT SalesOrderDetail
FROM 'C:\Data\SalesOrderDetail.csv' WITH (FIELDTERMINATOR = ',');
On the other hand, a Data Lake is a vast pool of raw data, the purpose for which is not defined until it is needed. Data Lakes store all types of data: structured, semi-structured, or unstructured, and it uses a schema-on-read approach, which means data is not organized until it is accessed. This allows for more flexibility and agility in terms of the type of data stored and the operations performed on the data. Here is how you load data into a Data Lake using Azure SQL:
CREATE EXTERNAL DATA SOURCE MyAzureDataLakeStore
WITH ( TYPE = HADOOP, LOCATION = 'adl://mydatalake.azuredatalakestore.net');
In terms of data processing, Data Warehouses use Online Analytical Processing (OLAP), a method that aggregates data to provide an answer to a particular query quickly. This is ideal when dealing with structured data, which is mostly the case in Data Warehouses. SQL Server 2012 and 2014 provide robust OLAP solutions. However, Data Lakes use Massive Parallel Processing (MPP), which makes it possible to process large amounts of raw data quickly, which is an advantage when dealing with unstructured or semi-structured data.
The choice between a Data Lake and a Data Warehouse ultimately depends on the business requirements. If your company relies on structured data for reporting and decision-making, a traditional Data Warehouse may be the best choice. SQL Server and MySQL provide excellent database solutions for this. However, if your company needs to store large volumes of raw data of various types and process it on the fly, a Data Lake would be more suitable. Azure SQL and DB2 10.5 offer powerful tools for managing Data Lakes.
In conclusion, both Data Lakes and Data Warehouses have their strengths and weaknesses. It is important to understand the nuances of each to make an informed decision based on your business needs. Today's BI is not about choosing between a Data Lake or a Data Warehouse, but about leveraging the strengths of both to drive insights and value from your data.