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Data lake vs lakehouse: Why a lakehouse is the better option


Want the flexibility and expansive storage of data lakes and the analytic sophistication of data warehouses? With a data lakehouse, you get the best of both.

An emerging solution — the data lakehouse — unleashes a new era of data management.

Want the flexibility and expansive storage capabilities of data lakes? What about the analytic sophistication of data warehouses? With a data lakehouse, you get the best of both worlds.

Pivoting to data lakehouses can tame data chaos and unlock valuable insights.

What are data lakes?

A data lake is a vast pool of raw data stored in its native format. It offers unparalleled flexibility in storing diverse data types. Organizations dealing with volumes of varied data find it an ideal choice.

But data lakes lack structure when retrieving and analyzing data. If mismanaged, you might find yourself wallowing in so-called “data swamps.”

Imagine a hospital compiling exhaustive datasets from clinical trials and patient records. The sheer variety and volume of this data make a data lake an attractive option for storage.

Yet, researchers face difficulties drawing conclusions from all this unorganized data. They’d need extensive data preparation and transformation efforts to make anything happen.

The data lake allows the institution to store all its data in one place. Yet disorganization can hamper timely analysis and data application. The result — a slower advance in medical research.

What is a data lakehouse?

A data lakehouse combines data lake storage with data warehouse analysis. This fusion supports both unstructured and structured data. It offers an all-encompassing solution for data storage and analytics without compromising performance.

The data lakehouse addresses CIOs’ demands for a unified approach to data management. It does so by combining scalability with operational efficiency.

Modern businesses of all kinds demand such efficiency. For example, banks gather terabytes of data from transactions, customers, and so on.

A data lake would fragment this data for unprocessed storage. Meanwhile, a data warehouse would do so for structured, ready-for-analysis data.

But with a data lakehouse, the bank can store all its data in a singular repository. It can support algorithm-driven fraud detection and predictive modeling for market trends. It also ensures compliance with global financial regulations and data governance standards.

Such an approach enhances the bank’s ability to innovate while providing personalized customer service.

Read about how a data lakehouse can reshape banking.

Why a lakehouse is the better option

Data lakehouses offer many distinct advantages over data lakes:

Unmatched operational efficiency

Lakehouses streamline ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes.

  • ETL extracts data from various sources. It transforms it into a structured format ready for data warehouse analysis.
  • ELT keeps the data raw during the load phase in the data storage system. Afterward, it can transform the data as needed for analysis.

Both ETL and ELT are central to data integration and management. By combining their processes, a data lakehouse boosts efficiency across the board.

Data professionals have found that these efficiency gains can increase their productivity. A Forrester report found 75% of respondents save over four hours a day with everything on one data system.

Fostering superior growth

Data lakehouses can also enhance a company’s growth trajectory. The architecture of a data lakehouse supports the seamless execution of complex data workflows. Businesses aiming to streamline operations and enhance decision-making processes would find this crucial.

Furthermore, a data lakehouse makes everything so much easier.

  • Discover meaningful patterns and trends in data.
  • Use predictive modeling.
  • Transform raw, messy data into clean, usable information.

Lakehouses do all that.

Enhancing data governance

For 63% of companies, data that is inaccessible and scattered across different sources creates major headaches. Without a data lakehouse, they struggle to effectively adhere to data governance protocols.

Data lakehouses provide organizations with robust tools to control data access. Thus, they uphold data integrity across their entire data ecosystem.

The lakehouse system streamlines governance processes. It also ensures uniform policy enforcement on both structured and unstructured data.

Other advantages of a data lakehouse

AI-driven data management

Data lakehouses boost the AI data stack’s agility and speed. Businesses can then adapt to changing market demands and opportunities.

Support for data mesh

They offer a compelling path toward decentralized data architecture, providing a single source of truth for all data assets and efficient data management across all departments.

Stronger security profile

With comprehensive protection against data breaches and unauthorized access, data lakehouses integrate advanced security right into their architecture.

Such an integrated security approach ensures the safeguarding of all data. Organizations get both peace of mind and increased trust in their data ecosystems.

Data lakehouses are the way to go

The advantages of adopting a data lakehouse over a traditional data lake are clear. If you want large financial gains and seamless AI integration, get a lakehouse.

The digital landscape is evolving daily. Moving to a data lakehouse is no longer a mere operational decision. Rather, it’s a strategic move towards future-proofing your data management capabilities.

Read next: The true cost of your data, beyond server costs

About Lumenalta

Lumenalta stands at the forefront of this transformative shift. We help mid-market companies navigate the complexities of data architecture. Doing so unlocks unparalleled growth and efficiency.

We can help you adopt a data lakehouse to ensure your data strategy and objectives align — reach out to us to ask questions or get started.

Learn more about the benefits of a data lakehouse.