Βusinesses that are working with data must determine how to store, manage, and analyze them efficiently. Two popular options for data storage are data lakes and data warehouses. In this blog post, we will explore the differences between data lakes and data warehouses. When it makes sense to use each with Snowflake and SQL.

First things first. Let’s define these two entities.

Data Lakes

Data lakes are typically designed using a “schema on read” architecture, which means that the data is stored in its raw form, without any predefined schema.

This provides businesses with the flexibility to store any kind of data, structured, semi-structured, or unstructured, without any restrictions. They do not require data to be preprocessed or structured before they are loaded into the lake. However, it also means that this procedure will occur at the time of analysis. This makes data lakes more flexible and cost-effective for storing large amounts of data.

To make sense of the data stored in a data lake, businesses must use tools and technologies to extract, transform, and load (ETL) the data into a structured format that can be queried and analyzed.

A data lake offers an effective solution that can load and store large amounts of data very rapidly without transformation. Companies that need to collect and store a lot of data but do not necessarily need to process and analyze all of it right away.

Data Warehouses

Data warehouses, on the other hand, are designed using a “schema on write” architecture, which means that the data is stored in a predefined schema.

A data warehouse is a large, centralized repository of structured data. It allows businesses to store, manage, and analyze data from various sources. Data warehouses are typically used for storing historical data that has been extracted, transformed, and loaded (ETL) from various sources.

This schema defines the structure of the data, including the tables, columns, data types, and relationships between the tables. This structure allows businesses to optimize their queries for fast querying and reporting, making data warehouses an excellent choice for businesses that need to generate reports and analytics quickly.

To load data into a data warehouse, businesses must use ETL tools and technologies to extract the data from various sources, transform it into a structured format that matches the predefined schema of the data warehouse, and then load the data into the data warehouse.

There is a variety of different definitions out there for these two entities. While I was doing my research on the topic I came across a variety of articles. Each one was approaching the subject using a different lens, like in this article or this video.

When to use a Data Lake

A data lake is an excellent choice for businesses that want to store and analyze large volumes of raw data from various sources. Data lakes are flexible and can store data in their native format, which means businesses can store unstructured and structured data without the need for preprocessing or structuring. This makes data lakes a cost-effective option for businesses that need to store and analyze large amounts of data without incurring high infrastructure costs.

For example, imagine a company that collects data from various sources such as social media, IoT devices, and transactional systems. This data is unstructured and is in various formats such as JSON, CSV, and XML. Instead of preprocessing and structuring the data before storing it, the company can store the data in a data lake in its native format. The company can then use Snowflake and SQL to analyze the data and generate insights that can help them make informed business decisions.

When to use a Data Warehouse

A data warehouse is an excellent choice for businesses that need to store structured data and generate reports and analytics quickly. Data warehouses are optimized for fast querying and reporting, which means businesses can generate insights from their data quickly. Data warehouses are also ideal for storing historical data that has been ETL processed from various sources.

For example, imagine a company that operates a large e-commerce website. The company collects transactional data such as orders, payments, and shipping information. It needs to generate reports and analytics quickly to track sales, customer behavior, and inventory levels. Then it can use Snowflake and SQL to ETL process the structured data and load it into a data warehouse. As a result, the company can then generate reports and analytics quickly and efficiently.

Last but not least, keep always in mind that only a cloud environment, like Snowflake, can offer the economies of scale, data security, reliability, and low maintenance needed to handle this data explosion.

Conclusion

In conclusion, data lakes and data warehouses are both excellent options for storing and analyzing data. Data lakes are flexible and cost-effective, making them an excellent choice for storing large volumes of raw data from various sources. Data warehouses, on the other hand, are optimized for fast querying and reporting, making them an excellent choice for businesses that need to generate reports and analytics quickly.

When choosing between a data lake and a data warehouse, businesses should consider their data storage and analytics needs carefully. With the right infrastructure, such as Snowflake, businesses can leverage the benefits of both data lakes and data warehouses to gain valuable insights from their data.