Databases Vs Data Warehouses Vs Data Lakes - What Is The Difference And Why Should You Care?

the data warehouses i worked with were all not columnar

Hey, really appreciate this video. If I could summarize, it sounds like:

- (transactional) databases are generally closer to the data generation source and tend to be closer to operations
- data warehouses are further downstream of the transactional databases and have usually gone through some pre-processing to make it more accessible for downstream usage (ie: analytics, machine learning, etc.)
- data lakes are kind of a catch all storage method for your data that may require a little more technical knowledge and effort to access

Awesome video. I am prepping for an interview for my dream job and this helped me so much. Thank you!

Nice video, might be useful to show examples of each at the end.

If your company needs to process transactions quickly, but you also need reporting, then wouldn’t you have BOTH OLAP and OLTP databases?? Instead of picking one or the other??

Great video.

Amamzing thank u

Thank you for this great content.
How to reach out if I have other questions?
I just got certified data warehouse engineer, so, I'm new to this but I have a good knowledge of the whole concept.

data warehouses represent a centralized location for storing data assets from various other sources where the centralization allows data experts to answer business and analytics questions with a 360 view of data that the company has. Often the underlying format of the data is based on the analytical engine of the warehouse chosen. Whether your warehouse is row-based or columnar or just files is decision made by the engine responsible for handling load/insert/query operations. You can have a warehouse that doesn't leverage star schema or snowflake design and still call it a warehouse albeit probably not one that is efficient to analyze.

Can you tells how you switch from data analyst to data engineering in your 2 years of being a data analyst, what did you expose your self first into, is it going to be mastering python and SQL then etl?
Thank you

It’s like you’re speaking a different language 😅 I have no idea about whatever this is.

At my company they treat data stores as the new shiny mirror. Nobody really knows what are the limits and the use cases for the different options

So if you have a lot of document journals that you need to like archived but accessible for read access. Would you recommend a wear house instead of a lake?

Listening to the data lake explanation all i could think about was the old AS400 XD

Hey Ben! when you say row oriented data warehouse, it caught my attention and I tried to look it up on google but did not get any satisfactory results. Could you elaborate on this term? what are the use cases these address? Why do they exist in the first place?

Great and informative video, what about datalakehouses? Thanks!!

What is the advantage of snapshots in a data warehouse instead of just saving a copy of the database each period?
Also, you can use these separate copies for analytics without interfering with the transaction DB version.

I always thought 'database' was just an umbrella term for referring to any storage thing which stores data, whether its a relational, non-relational, object, etc. type database.

Great video to compare the differences among the 3 types and their general use cases; it is very helpful to help me identify which type I'm dealing with on my job. Their definitions have always been debatable because their use cases vary a lot by how companies define them for their projects.

Hey Gary, is data engineer for introvert people, do u have to communicate a lot to stakeholders ?

Very interesting guide... Was stuck on a decision earlier on what approach to take but I guess my uncertainty was a result of the evolving use cases and requirements.... Awesome explanation here💯

what's your opinion on Databricks?