Data Warehouse vs Data Lake vs Data Lakehouse

Companies today are collecting, saving, processing, and using more data than ever before to make more decisions. However, 81% of IT leaders say that their C-suite has not ordered any extra spending or a drop in cloud costs.

The need for strong and reliable data tools needs to be balanced with a closer look at costs by data teams. Teams must pick the right design for the storage layer of their data stack because of this.

However, the ways to store data are changing quickly. Different companies that sell data warehouses, data lakes, and now data lakehouses all have their own pros and cons that data teams need to think about.

What Is a Data Warehouse?

An company can store a lot of information from many different sources in a single place called a data warehouse. It is an organization’s main source of “data truth” and a key part of both reporting and business analytics.

These are usually kept old information by putting together relational data sets from different sources, like business, transactional, and application data.

Before putting the data into the warehousing system, data stores change and clean it up from different sources so that it can be used as a single source of truth. Companies spend money on data warehouses because they quickly bring together business ideas from all over the company.

Business researchers, data engineers, and decision makers can use BI tools, SQL clients, and other less advanced (i.e., not data science) analytics apps to access data in data warehouses.

What Is a Data Lake?

It is a centralized, extremely adaptable storage facility that holds vast quantities of original, unformatted, raw data, both structured and unstructured.

The relational data in data warehouses has already been “cleaned.”  on the other hand, uses a flat design and object storage to store data in its original form.These are adaptable, long-lasting, and inexpensive. They let businesses get deeper insights from unstructured data, while data stores have trouble with this type of data.

When data is recorded in a data lake, the schema or data is not set. Instead, data is extracted, loaded, and transformed (ELT) so that it can be analyzed. It let you use tools for different types of data from IoT devices, social media, and live data to do machine learning and predictive analytics.

What Is a Data Lakehouse?

It is a new way to store large amounts of data that takes the best parts of both data warehouses and data lakes and puts them together in one place.

It lets you store all of your data in one place, including organized, semi-structured, and unstructured data. It also gives you the best machine learning, business intelligence, and streaming tools.

Most data lakehouses begin as data lakes with all kinds of data. The data is then changed to Delta Lake format, which is an open-source storage layer that makes data lakes more reliable. Delta lakes let ACID transactional processes run on data lakes from standard data warehouses.

Core Differences Between Data Warehouse, Data Lake, and Data Lakehouse

Recent Innovations and Convergence Trends

The world of data design is changing quickly. New technologies are making it harder to tell the difference between data warehouses, data lakes, and lakehouses. Databricks and Snowflake are at the forefront of this change. Both have added new features that are breaking new ground to meet the needs of current data teams.

Databricks: Creating the Lakehouse Paradigm First
Databricks was one of the first companies to use lakehouse design, which combines the best parts of data lakes and data warehouses. Some recent changes they’ve made are:

Unity Catalog: it  is a unified governance system that gives all data assets fine-grained access controls.

Delta Lake 3.0: it has improvements that make it easier to handle data by supporting more table formats, such as Delta, Hudi, and Iceberg.

LakehouseIQ: it  is an AI-powered knowledge engine that lets users ask questions about data using natural language. This makes data easier for everyone in the company to access.

With these new features, Databricks becomes a leader in offering data solutions that are scalable, flexible, and easy to use.

Snowflake: Making the Data Cloud Bigger
Snowflake keeps changing what a modern data warehouse is by adding features that are usually found in data lakes:

Unified Iceberg Tables: These make it easier for systems to work together by letting them easily access and use external data saved in open formats.

Document AI: uses its own big language models to extract and understand unstructured data, which makes it easier to do analysis.

Dynamic Tables and Snowpipe Streaming: it makes it easier to add and handle streaming data, which makes real-time analytics possible.

Snowflake presents itself as a flexible “data cloud” that can meet a wide range of data processing needs by adding these features.

The Convergence of Architectures
New products from Databricks and Snowflake show that there are fewer and fewer differences between data warehouses, lakes, and lakehouses. They are now looking for sites that offer:

Unified Data Management: Using a single platform to handle organized, semi-structured, and unstructured data.

In real time, processing can handle both batch and live data loads.

Combining AI and machine learning makes it easier to do advanced analytics and make predictions.

Choosing the Right Architecture

Choosing the right data architecture, like a data warehouse, data lake, or lakehouse, relies on a number of things, such as the type of data, the processing needs, and the organization’s goals.

Data Warehouse: Structured and Performance-Oriented

Ideal for organizations that:

• Primarily handle structured data.
• Require high-performance SQL querying for business intelligence.
• Need consistent, reliable reporting mechanisms.

Structured datasets can be stored and retrieved more efficiently in data warehouses, which makes them good for traditional analytics and reporting jobs.

Data Lake: Ability to Handle Different Kinds of Data

It works best for businesses that:

• Take in a lot of raw, unstructured, or partially structured info.
• Do something related to data science, machine learning, or experimental analytics.
• Need storage options that can be expanded and have schema-on-read features.

Data lakes let you store and process a lot of different types of data, which is useful for when your analytical needs change.

Data Lakehouse: Unified and Scalable

An optimal choice for organizations that:

• Desire the combined benefits of data lakes and warehouses.

• Need to support both real-time and batch processing.

• Aim to democratize data access across technical and non-technical users.

Lakehouses offer a unified platform that simplifies data architecture, reduces redundancy, and enhances collaboration across teams.

Considerations for Making Decisions

When picking the right design, think about:

Data Variety: Take a look at the different kinds of data your business uses.

Processing Needs: Figure out whether real-time processing or batch processing is needed.

User Base: Know who will be accessing the info and how well they know how to use technology.

Scalability and Flexibility: Think about how the system will grow in the future and how well it can change to new data needs.

By matching these factors with the good points of each design, businesses can make smart choices that help their data strategy and meet their business goals.

Conclusion

Depending on your data type, processing requirements, and user objectives, you can choose between a lake, lakehouse, or data warehouse as data architectures change. Databricks and Snowflake’s innovations demonstrate the trend toward scalable, unified platforms. A thorough understanding of these technologies is necessary to stay ahead.

Explore Edureka’s Microsoft Fabric Training course to gain hands-on experience with modern data solutions. Whether you’re a data engineer or analyst, this course equips you with the skills to manage and analyze data efficiently in today’s dynamic landscape.

FAQs

Is Snowflake a data lake or Lakehouse?

With a few data lake features, Snowflake is primarily a cloud data warehouse. It provides a combination of both for flexible data use, but it is not a full lakehouse.

Can Data Lakehouse replace data warehouse?

Given its high performance in handling both structured and unstructured data, a data lakehouse can frequently take the place of a data warehouse. However, for certain high-speed analytics requirements, some companies might still favor data warehouses.

Is Databricks a Data lakehouse?

Yes, Databricks is a platform for data lakes. For unified, scalable analytics, it combines the capabilities of data lakes and data warehouses.

What is ETL in a data warehouse?

In a data warehouse, ETL stands for Extract, Transform, Load. It entails gathering information from various sources, formatting and cleaning it, and then putting it in the warehouse for examination.