Data Lake vs. Data Warehouse: Key Differences You Should Know

Author : Johan Doc | Published On : 23 May 2024

We recently came across a mind-boggling fact.

Did you know that as per the latest estimate, over 328.77 million terabytes of data are generated each day? That’s not just big, it’s HUGE!

It further states that the volume of data generated has catapulted year-over-year ever since 2010.

This begs the question: what can businesses do to manage this deluge of data?

As data becomes omnipresent and indispensable, businesses continuously seek efficient, innovative ways to leverage the vast amounts of information available.

Over the years, we’ve seen several technological solutions cropping up on the scene for seamless storage, management, and analysis of data; however, the two that stayed the longest in the spotlight are:

Data Lakes
Data warehouses

Many enterprises are increasingly hiring enterprise data lake and data warehouse service providers for making them data actionable. However, their inability to differentiate between the two are making their efforts go in the wrong direction.

Today, we aim to unravel the meaning and significance of data lakes as well as data warehouses. We also aim to outline the key differences that can help businesses choose what better aligns with their requirements.

Sounds exciting? Let’s dig right in, then!

What is a Data Lake?

A data lake acts as a reservoir of data, storing every form of information - structured, semi-structured, or unstructured. There’s no predefined schema, and whatever information comes in is ingested without compromising fidelity.

In a simple definition, a data lake is capable of storing every format and version of data in its original form, helping decision-makers perform effective sampling and correlation.

A data lake, when implemented by a Data Lake Development company, empowers businesses with:

Scalability by allowing a seamless mechanism for storing and managing petabytes of data.
Flexibility by supporting almost every data type and format under the sun.
Cost-effectiveness by enabling an efficient, multi-data storage, especially for large volumes of information.

What is a Data Warehouse?

A data warehouse, on the other hand, cuts a niche for itself by storing processed and structured data with a predefined schema.

As a centralized repository, the warehouse cradles information, both contemporary and historical, from multiple sources and supports business intelligence (BI) objectives.

Companies that provide Data Warehouse Consulting services conduct a deep evaluation of your current business data and offer tailored recommendations on implementation data warehouses. These companies, with their market-leading, strategic guidance, help businesses transform as more proactive and agile based on actionable intelligence.

Data warehouses help enterprises become:

Efficient by unlocking rapid insights to address complex queries and reports.
Competitive by leveraging competitive, accurate data and making effective decisions for tangible results.
Secure by harnessing robust encryption practices.

Data Lake vs. Data Warehouse: Key Differences

Aspect	Data Lake	Data Warehouse
Data Storage	Stores raw data without predefined schema	Stores processed and structured data with predefined schema
Users	Used by data scientists and engineers	Used by business analysts and professionals
Analysis	Ideal for complex analytical processes	Suitable for traditional business intelligence tasks
Format	Stores and manages structured, semi-structured, and unstructured data	deals with structured data
Sources	Ingests data from various sources, including IoT devices, social media, and mobile apps	Sources data from transactional systems, CRM, ERP, etc.
Scalability	Highly scalable, providing the opportunity for exponential data growth	Scalable but more expensive and complex to scale
Schema	Schema-on-read, applied during analysis	Schema-on-write, applied during data ingestion
Processing	Supports both batch and real-time processing	Primarily supports batch processing
Cost	Generally more cost-effective for storing large volumes of data	Can be expensive

Data Lake vs. Data Warehouse: What to Choose?

The choice between a data lake and a data warehouse is a tough one.

So, here’s the drill: carefully evaluate your business requirements, look into the type and scale of your data, map out your data objectives, and factor in the required processing capabilities.

Once you’ve done all that, pick your preference.

Data lakes are usually ideal for organizations that want to store large, multitudinous data and perform complex analytics. Data warehouses, on the contrary, are the best bets for organizations that want to store processed data in large quantities for reporting and BI purposes.

There’s a noticeable rise in the number of data warehouse service providers that can help any willing organization to streamline its data functions. Similarly, the market is flooded with data lake development companies that can help improve data bottom line from top to bottom.