Do you like your data like you like your steak — raw? While this might not be how you’d want your next meal, you’ll need a lake if that’s how you like your data. On the other hand, if you prefer more processed stuff, you’ll be okay with a warehouse. And if you want it done in real time, stick with the streaming. So, bear with us until the end, as we’ve got some pretty good examples of data warehouse vs. data lake vs. data streaming.
Data warehouse, data lake and data streaming explained
So what is a data warehouse? A data warehouse is a central repository of data coming from multiple sources. Unlike a database, we’ll use a data warehouse for the online analytical process (OLAP). All data from different databases are gathered and sent via an ETL process to the data warehouse. An ETL process includes extracting, transforming, and loading data. So data warehousing is used for analysis and reporting. Unlike data lakes, data warehouses use structured and semi-structured data to create models that suit your business.
The data warehouse is nonvolatile, meaning the data can’t be changed. Unlike a database, it doesn’t contain all the data, just the data of particular interest. Thus, data warehouses are much faster in querying different data than databases.
In very simple terms, this would be like a “room where you keep everything else.” So, what does that mean? A data lake is a system or repository of data stored in its natural/raw format. When we say raw data, it means it hasn’t been processed for use yet. If you’re going to use it in a database or a data warehouse, you’re going to clean it a little bit first.
This data isn’t very usable, so if you want to apply it, you’ll need to transfer it into a database or a data warehouse. Most businesses use data lakes because they’re cheaper than a data warehouse and can store any form of data. On the other hand, they’re also widely used for machine learning and AI.
Data streaming is quite a popular term these days. Since there are more and more data, it requires faster analysis — that’s where data streaming comes in. The key aspects of data streaming are real-time analytics and processing. Therefore, data streaming is the real-time processing of continuously generated data. On the other hand, data streaming is highly scalable, which is good for servers or batches because they won’t be overloaded. That’s because data can be broken down into queues, so if one fails, others won’t.
The simplest example of data streaming is a credit card. As a person swipes their credit card through the payment terminal, the data from that card is sent to the corresponding bank. This must happen in seconds (in real time). In the end, the bank signals there is enough money in the account and approves the transaction. This is just one example of data streaming. Nowadays, it has seen quite a few applications in multiplayer gaming, stock exchange trading, social media feeds, etc.
Data warehouse vs. data lake vs. data streaming comparison
- The pro of a data lake is that it can store data in its original form. Thus it’s a good place for starting new ideas, big data analytics, machine learning, developing AI, etc.
- The con of a data lake is that it has no meaningful structures. Thus, it’s not useful as a database or a data warehouse.
- The pro of a data warehouse is that it’s highly scalable, fast for analyzing data, and it enables historical insights. A data warehouse is more “orderly” when compared to a data lake.
- The cons of a data warehouse are that it needs a lot of time-consuming preparation. Also, understanding or using the data from a data warehouse in a proper manner can be hard because the stored data is in its raw or natural state — it hasn’t been cleaned, classified, or sorted. Furthermore, it’s not as fast as data streaming. Updates and maintenance can be costly.
- The pro of data streaming is that it provides companies with real-time data analytics. Data streaming is great for improving customer satisfaction because you can interact with the customer instantly.
- The con of data streaming is that there’s a lot of data (this can be a pro), so your team has to adapt to analyzing large amounts of data in real time. Data streaming will add a certain level of complexity.
Data warehouse vs. data lake vs. data streaming – the conclusion
Data streaming sounds the most useful and probably is, but you’ll also need a data warehouse and data lake. For example, you’ll need a data lake if you don’t know where to store certain data or if you just want to collect some raw data. If you need to categorize and analyze data systematically, you’ll probably need a data warehouse, especially if you have large amounts of data. On the other hand, data streaming architecture is more similar to cloud architecture.
Big companies like Amazon and Google use it all the time. Google uses a data lake for any analysis of any type of data. This allows your teams to safely and affordably consume, store, and analyze vast amounts of varied, high-fidelity data. Amazon uses data warehouses to quickly analyze enormous amounts of data to determine customers’ purchase patterns. Google maps use data streaming to know your location all the time.
So, how can I benefit from any of this?
There is no one-size-fits-all when it comes to data analytics. We can use different tools in different ways for different purposes, so think about your business and what you can benefit from. Don’t worry too much as you’ll create even more data problems in your head. If you’re unsure which of these solutions is suitable for you, google it — in the end, they have all the data in the world.