The data warehouse and the data lake both have their strengths and weaknesses. Like yin and yang, they often coexist within the same data stack and this has given rise to a hybrid category – the Data Lakehouse!

What is a Data Lakehouse?

A data lakehouse is a modern platform that combines the functions of a data lake and a data warehouse. It has the flexibility of a data lake for storing unstructured data while providing the data management tools and features of a data warehouse, coupling them strategically in a larger data storage/management system. Integrating these two data solutions allows users to leverage the best of both worlds.

How does it work?

Data lakehouses typically provide support for data ingestion through a variety of methods. These include application programming interfaces, streaming, and more. This data lands in its original, raw form without requiring schema definition. A data lakehouse provides a cost-effective storage layer for both structured and unstructured data. Therefore, it contains all of an organization’s data.

Generally, the storage technology categorizes data into the landing, raw, and curated zones depending on its consumption readiness. The result is a framework that offers a single source of truth and enables companies to make the most of advanced analytics capabilities simultaneously.

In a data lakehouse model, organizations first migrate data from sources into a data lake. Then, a subset of this data seamlessly filters through to become more curated and trusted data sets on which organizations set the required governance, use, and access rules.

data lakehouse

Some key features 

With an understanding of a data lakehouse’s general concept, let’s look a little deeper at the specific elements involved. It offers many pieces that are familiar from historical data lake and data warehouse concepts, but in a way that merges them into something new and more effective for today’s digital world.

Data management features

A data warehouse typically offers data management features such as data cleansing, ETL, and schema enforcement. These are brought into a data lakehouse as a means of rapidly preparing data, allowing data from curated sources to naturally work together and be prepared for further analytics and business intelligence (BI) tools.

Open storage formats

Using open and standardized storage formats means that data from curated data sources have a significant head start in being able to work together and be ready for analytics or reporting.

Flexible storage

The ability to separate compute from storage resources makes it easy to scale storage as necessary.

Support for streaming

Many data sources use real-time streaming directly from devices. A data lakehouse is built to better support this type of real-time ingestion compared to a standard data warehouse. As the world becomes more integrated with Internet of Things devices, real-time support is becoming increasingly important.

Diverse workloads

Because a data lakehouse integrates the features of both a data warehouse and a data lake, it is an ideal solution for several different workloads. From business reporting to data science teams to analytics tools, the inherent qualities of a data lakehouse can support different workloads within an organization.

Why might a business use a data lakehouse?

Combining data lakes and data warehouses into data lakehouses allows data teams to operate swiftly because they no longer need to access multiple systems to use the data.

This simplified data infrastructure solves several challenges that are inherent to the two-tier architecture mentioned above:

  • Improved reliability: Businesses don’t have to worry about engineering ETL transfers between fragile systems that may be disrupted due to quality issues.
  • Reduced data redundancy: The data lakehouse serves as a single repository for all data, eliminating redundancies and supporting more efficient data movement.
  • Fresher data: The issue of data staleness is addressed with a data lakehouse because data is available for analysis in a few hours rather than a few days.
  • Decreased cost: By streamlining ETL processes and moving to a single-tier architecture, businesses often save money after adopting the data lakehouse approach.

Featuring increased agility and up-to-date data, it’s clear that data lakehouses are a great fit for organizations looking to fuel a wide variety of workloads that require advanced analytics capabilities. In fact, lakehouses enable businesses to use BI tools, such as Tableau and Power BI, directly on the source data, resulting in the ability to have both batch and real-time analytics on the same platform.

It also gives businesses the ability to adopt AI and machine learning (ML) or take their existing technology to the next level, while still meeting compliance requirements. Though the unstructured data needed for AI and ML can be stored in a data lake, it creates data security and governance issues. A lakehouse solves this problem by automating compliance processes and even anonymizing personal data if needed.

data lakehouse

Challenges of Data Lakehouse

However, implementing a data lakehouse can be challenging. Organizations should avoid treating the lakehouse as a complete, uniform solution for everything. It contains many complex components that require separate management.

Another challenge of adopting a data lakehouse approach is migrating all existing data warehouses to the company’s data lake before experiencing the benefits. Migration is often time-consuming and expensive and carries risks associated with outages and latency.

It also requires designing the lakehouse infrastructure for scalability and cost-effectiveness. A poorly planned data lakehouse won’t support round-the-clock analytics for business-critical use cases. Some data lakehouse vendors require customers to use specific tools, which might not be compatible with all existing tools connected to the organization’s data lake.

Data warehouse vs data lake vs data lakehouse: What’s the difference?

While data lakehouses combine the flexibility and cost-efficiency of data lakes with the querying capabilities of data warehouses, it’s important to understand how these storage environments differ.

Data warehouses

Data warehouses were the original big data storage option. Designed to provide a single source of truth for structured data, they offer a way for organizations to simplify data management by centralizing inputs. While they are extremely adept at this function, they lack the flexibility of their data lake descendants.

Data lakes

Data lakes evolved as a way to better manage structured and unstructured data simultaneously. These lakes are highly flexible and allow companies to store data in native form. In turn, this empowers more in-depth data science and reduces the total cost of data management.

Data lakehouses

It combines the key aspects of the lake and warehouse. This enables enterprises to use the single-repository model of data warehouses for unified storage without sacrificing the analytical flexibility of data lakes, allowing data lakehouses to excel at both analytical and machine learning workloads.

data lakehouse

Conclusion

Is your organization assessing whether it should embrace a data lakehouse or a data warehouse? Or does it want to simplify its stack by eliminating the lake or the warehouse?

As I’ve shown, both modern warehouses and lakehouses share a lot of the same capabilities. If you don’t care about what’s under the hood or which paradigm should be abided by, the degree of unstructuredness that you require should be your primary consideration.