For companies, data lakehouses fuse the data warehouse and data lake concepts.
These resources utilise the data structures found in data warehouses and the management features found in data lakes to create cost-effective data storage arrangements.
Furthermore, less time is spent administrating and decreased data movement and redundancy, while shortened schema and data governance procedures become a reality.
One data lakehouse has numerous benefits over a multiple-solution storage system. Data scientists continue to use these resources to enhance their knowledge of machine learning and business intelligence processes.
But what exactly is a data lakehouse?
This guide will discuss data lakehouses in detail, including challenges organisations faced with data warehouses and data lakes that gave rise to data lakehouses. We will also cover the benefits of data lakehouses. Let’s dive in!
The Definition of a Data Lakehouse
A data lakehouse is a contemporary data resource that combines the features of a data lake and a data warehouse. As stated earlier, the data lakehouse banks on a data warehouse’s data structures and a data lake’s management features.
The result is a cost-effective data storage arrangement that saves time and shortens processes.
Before we delve into the crux of data lakehouse, it is vital to first understand the meaning of data warehouse and data lake.
What Is a Data Warehouse?
Simply put, a data warehouse is a centralised storage resource that contains information that can be used for decision-making.
Data regularly enters data warehouses from relational databases, transactional systems, and other sources. Business analysts, data scientists, and engineers can access the data through SQL clients, business intelligence (BI) tools, and alternative analytics applications.
The purpose of a data warehouse is to perform queries and analyses.
These structures have substantial amounts of historical data that are then used to derive helpful business insights through analytics. Sometimes, industry experts refer to the data warehouse as a company’s “single source of truth” due to its influence on decision-making.
What Is a Data Lake?
On its part, a data lake is a centralised storage facility that contains raw data. A data lake differs from a data warehouse in that the latter houses hierarchical information in files and folders, whereas the former stores raw data based on flat architecture and object storage.
A flat storage architecture refers to a storage method where link chains are nonexistent. Users can access each page by clicking one to three links. Object storage utilises metadata tags and unique identifiers, which eases the data retrieval process and enhances performance. Therefore, data lakes are essential because they utilise low-cost object storage procedures in open formats.
Now back to the data lakehouse concept.
Before the idea of data lakehouse was born, experts discovered challenges with data warehouses and lakes that reduced their impact. Data lakehouses were introduced to change how companies and data centers managed the large amounts and varied categories of data. Some of the challenges discovered with data warehouses and lakes are explained below.
Challenges Large Scale Organisations Face When Using Data Warehouses and Data Lakes
The following are some challenges organisations face with data lakes and warehouses. Some of these challenges contributed to the development of data lakehouses.
1. The Unreliability of Data Lakes
Despite the nature of data lakes to utilise low-cost object storage procedures in open formats, they are not without drawbacks.
For instance, data lakes store unstructured data, making them unreliable data swamps. Therefore, data lakes cannot be relied upon to support business intelligence applications.
2. The Rigidness of Data Warehouses
The rigid proprietary formats of data warehouses make them weak in supporting the varied data types prevalent in the modern context.
Examples are audio, video, and deep learning models such as artificial intelligence (AI) and machine learning (ML). This nature of data warehouses triggered enterprise organisations to create data lakes for excess data storage and extraction.
3. The Duplication Encountered When Using Data Warehouses and Data Lakes
Another fundamental issue to note is that duplication became a problem after data lakes were introduced. Organisations attempted to leverage lake and warehouse capabilities by installing different systems and departments.
However, the move brought about duplicated data, enlarged budgets, and replicated processes that led to the development of the data lakehouse.
Benefits of a Data Lakehouse
Based on the information covered above, a data lakehouse is better than a data lake or warehouse. Here are some specific benefits:
- Cost-effectiveness: A data lakehouse is a low-cost object storage option for enterprises. When companies install data lakehouses, they reduce the resources they use because these facilities remove the costs and time required for maintaining several data storage systems.
- A data lakehouse can support a variety of assignments: When a company installs a data lakehouse, its business analysts, data scientists, and engineers have unrestricted access to the best BI tools like Tableau and PowerBI. These tools are used for advanced analytics. Data lakehouses also utilise open data formats that ease the analytics process.
- There is reduced data duplication: As earlier stated, data duplication was one of the challenges that affected the use of both data warehouses and lakes. Data lakehouses limit data redundancy because they offer users an all-purpose data storage system. This way, all business data demands can be catered to. Although most companies go for the hybrid data warehouse and data lake option, the duplication involved can prove costly down the line.
- Better data governance, versioning, and security: Finally, the data lakehouse architecture brings about condensed schema and data governance procedures that make it easier for enterprises to incorporate robust data governance and security instruments.
Wrapping Up
Data lakehouses are better alternatives to data lakes and warehouses.
Although data lakes offer centralised storage for raw data, they can sometimes become unreliable data swamps. On their part, data warehouses are inefficient in supporting varied data types despite the ability to store information that can be used for decision-making. When used together, data warehouses and lakes cause data duplication.
Therefore, the best solution is to invest in data lakehouses. These resources utilise the data structures and management features of data warehouses and data lakes, respectively. Data lakehouses are cost-effective, can support a variety of assignments, reduce data duplication, and offer advanced data governance, versioning, and security solutions.
