Data Lake vs Data Warehouse: Features and Benefits

Data management and analytics are two major concepts that drive the digital world. Businesses, small and large, see these as the moving parts that help them make the best informed decisions. They keep the data organized and maintained for easy access to ensure a better customer service experience. These may require either the use of a data lake or a data warehouse.

In this guide, we’ll be looking at data lake vs data warehouse. We’ll look at the overall features and benefits of both. Finally, you’ll learn the major differences between the two. We’re excited to bring you this guide and for good reasons - let’s get started.

What is a data lake?

To begin, let’s dive into what a data lake is. This is a centralized repository that allows all kinds of data to be stored. It can be structured, semi-structured, and unstructured data as well. Data lakes are not in any way like traditional storage systems where the data has to be stored in a specific manner. 

In this instance, you can use data without having to worry about whether or not it has structure or not. The data included in these data lakes include but are not limited to:

• Sensor data
• Social media feeds
• Transactional records
• Log information

Data lakes operate with various cloud-based solutions such as Google Cloud, Amazon S3, and Microsoft Azure. They also utilize technologies like the Apache Hadoop and Hadoop Distributed File Systems (HDFS).

What are the features of data lakes?

Now that you have a basic understanding of what data lakes are, we’re going to take a look at the list of features it has. Here’s what they are:

• Support for diverse data types: As mentioned, data lakes can handle structured, semi-structured, and unstructured data. Thus, it will provide versatility for numerous uses. 
• Schema-on-Read: Data lakes use this approach in terms of how the data is being applied when read. This pertains to the data analysis and exploration aspect. 
• Scalability: One of the best features of anything on the cloud is scalability. When it comes to taking on more data, this will be key for your business. Your resources will scale up or down depending on the average amount of data you take in.
• It’s cost-effective: Keeping on topic with scalability, data lakes are a cost-effective solution. If you scale upward, you pay more. If scaling down, you pay less. There is no set fee. It will all depend on the amount of data you take in on a regular basis.

What are the benefits of data lakes?

Investing in data lakes for your business can yield excellent benefits. What exactly will you get out of them? Let’s have a look:



• Speed data ingestion: Data lakes will allow data to be ingested quickly and from various sources. You’ll be able to access the data fast for analytical purposes. This will save you time but also allow you to make data-driven decisions much faster. When you need to make some serious moves in business, this benefit could help.
• Advanced analytics: Integrated with machine learning, AI, and real-time analytics, you get advanced analytics using the diverse data types that are stored. After all, you’re getting it from a centralized library.
• Flexibility and agility: Using the schema-on-read and support feature, data gives you flexibility so you can easily adapt to any data requirements that change. At the same time, it also supports any analytical needs without the need for restructuring.
• Excellent cost advantages: Why save money on an expensive solution? You can spend less while taking in less data. Only you pay more when business is booming. Best of all, with more money in your coffers, you’ll already have enough of it to cover the increased costs. Not bad, right?

What is a data warehouse?

Data warehouses operate in a similar manner to data lakes. It too is a centralized repository for data. The only difference is that the data is structured, transformed, and clean. It utilizes a schema-on-write system where the data will be structured and organized prior to being stored.

Examples of these database management systems include MySQL but can also be used in Google’s BigQuery, Amazon’s Redshift, and Microsoft Snowflake.

Features of data warehouses

Let’s take a look at the following features of data warehouses:

• Schema-on-Write: As mentioned, data warehouses use this approach for the purpose of structuring and organizing data.Once complete, it’s stored in the warehouse. This feature is designed for consistent and efficient queries.
• Structured data model: Data warehouses are stored in a structured format. This will make data analysis and querying simple.
• Data quality and consistency: The data stored in warehouses will undergo a cleansing, transformation, and validation process. This will make sure the data is satisfactory in terms of quality and consistency.

What are the benefits of data warehouses?

Finally, let’s take a look at the benefits of data warehouses:

• Data consistency and integrity: The data quality will always be in good hands thanks to a series of quality and consistency checks.
• Historical analysis: If you want to look over data from a certain time frame, you can be able to do that with data warehouses. This benefit will be perfect for trend analysis and historical comparisons. It will play an excellent role in forecasting and making better data-driven decisions.
• High performance querying: Data warehouses handle the complex analytical stuff regardless of the dataset size. So no need to do any more heavy lifting than necessary when it comes to queries.

Oamii Tech - Your authority in handling your data management

Data management and analytics go hand-in-hand for business in today’s digital world. You can manage it properly while using it for analytical purposes. Together, you have the data in front of you to make the best moves for your business. At Oamii Tech, we can make that happen.

Are you ready to make data easy to understand and manage for your business? You may need the right solutions for that. Contact Oamii Tech today and we’ll be able to help.