Where Should You Store Data? Data Lake vs Warehouse vs Object Storage Explained

Описание: If you’re learning data engineering, one of the most confusing questions you eventually run into is: where should the data actually be stored? Should you use a data warehouse, a data lake, object storage, a database, or something like a lakehouse or feature store? On paper the options look similar, but in the real world, they behave very differently.

Most tutorials simplify this part or skip it entirely, but in practice, storage is one of the most important architectural decisions you’ll make. The choice influences performance, cost, scalability, data governance, long-term maintainability, and the experience for analysts, BI teams, ML engineers, and downstream applications. If you get storage wrong, everything downstream becomes harder.

This video explains data storage from a real-world data engineering perspective. Instead of listing tools or repeating buzzwords, we break down how different storage types behave, why they exist, and how engineers select the right approach based on workload patterns, query behavior, data growth, schema flexibility, regulatory requirements, and long-term cost considerations.

You’ll learn the differences between:

Object storage vs block storage vs file storage
Data lakes vs data warehouses
Lakehouses and modern hybrid models
Streaming storage and log-based systems
Hot, warm, and cold storage tiers

More importantly, you’ll learn how to think about storage the way senior engineers and architects do: based on how the data is used, how long it needs to be retained, how fresh it must be, and whether the system needs to support analytics, machine learning, reporting, reverse ETL, real-time pipelines, or product-facing features.

By the end of this video, you’ll understand why in some companies storage looks simple, while in others it evolves into a layered architecture consisting of raw zones, curated zones, semantic layers, archival tiers, and performance-optimized serving layers.

What This Video Covers

What “storage” means in the context of modern data engineering
The practical differences between data warehouses, lakes, and object storage
Why hot, warm, and cold data tiers matter for performance and cost
How query patterns, user roles, and compute models affect storage architecture
What to evaluate before choosing any storage approach (retention, latency, governance, schema change, scalability)

Real-world examples of storage decisions going wrong (and how senior engineers avoid these mistakes)

How to map workloads to storage systems with confidence

Who This Video Is For

This session is designed for:

Aspiring data engineers
BI and data analysts transitioning into engineering
Software or cloud engineers working on data-heavy systems
Students or early-career practitioners trying to build correct mental models
Anyone designing or maintaining data platforms and pipelines

If you're preparing for a data engineering role, working with cloud data platforms, or trying to understand how modern data systems actually operate, this video will help you treat storage as a core architectural decision, not just a technical afterthought.


If this helped you understand storage in a clearer and more practical way, subscribe and drop a comment:

What storage system are you using today—object storage, a data lake, a data warehouse, a lakehouse, or a mix of all of them?

Your answers help shape future videos.


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