As cloud data warehouse and data lake architectures merge in the future, enterprises may soon find vendors who incorporate all of the features of all the data lakehouse tools. When it comes to developing and managing data pipelines, this might open up a world of possibilities.

Cloud data lakes and warehousing architectures have helped businesses in scaling their data management operations while cutting expenses, over the years. Traditionally, enterprise data is extracted from operational data repositories and stored in a raw data lake as part of the data management architecture. The next stage is to run another series of ETL processes to shift essential portions of this data into a data warehouse, where business insights can be generated for decision-making.

However, there are various challenges involved in the current setup, such as:

• Lack of consistency – Organizations may find it challenging to maintain consistency in their data lake and data warehouse architecture. It’s not only an expensive endeavour; teams must also use continuous data engineering techniques to ETL/ELT data between the two systems. Each stage has the potential to introduce errors and flaws, lowering the overall data quality.
• Datasets that are constantly changing – Data in a data warehouse may not be as current as data in a data lake, which is dependent on the data pipeline schedule and frequency.

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• Vendor lock-in – Moving significant amounts of data into a centralized EDW is difficult for businesses, not only because of the resources and time necessary, but also because this architecture creates a closed-loop, resulting in vendor lock-in. Furthermore, data kept in warehouses is more difficult to share with all data end-users in a company.
• Data governance – While data in a data lake is largely in various file-based formats, data in a data warehouse is mostly in database format, which adds to the complexity of data governance and lineage.

A data lakehouse overcomes the limitations of both a data lake and data warehouse architecture by integrating the best features of each to provide substantial value to enterprises.

The benefits of data lakehouse

There are various reasons to consider modern data lakehouse architecture when it comes to implementing long-term data management methods.

A data lakehouse has a dual-layered architecture, with a warehouse layer placed over a data lake enforcing schema, which ensures data integrity and control while also allowing for faster BI and reporting. Data lakehouse architecture also eliminates the need for multiple data copies and drastically decreases data drift issues

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More informed decision-making is facilitated by faster interactive queries combined with true data democratization. Data scientists, analysts and engineers can quickly access the data they need thanks to the architecture. As a result, the time-to-insight cycle is shortened.

Organizations can help their data teams save time and effort by using a data lakehouse architecture, which takes less resources and time for processing and storing data and delivering business insights. In fact, a data lakehouse can reduce major administrative burdens by providing a single platform for data management.

When it comes to data integrity, it allows data teams to maintain appropriate access controls and encryption across pipelines. Furthermore, data teams are not required to handle security for all data copies in a data lakehouse model, making security administration much easier and cost-effective.

Data lakehouse architecture reduces data drift by minimizing the requirement for multiple data copies in the implementation of data lakes and data warehouses. It also has a high level of data and metadata scalability. This enables businesses to complete crucial analytics initiatives in a short amount of time.

A data lakehouse is a step forward from cloud data lakes and warehouse architectures, allowing data teams to benefit from the best of both worlds while addressing all previous data management flaws. A data lakehouse initiative, when done correctly, can free up data and let an organization use it the way it wants and at the speed it wants.

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