Companies today are faced with the new challenge of storing, managing and analysing large amounts of data effectively. Due to poor data management strategies, 70 % of companies are struggling with data silos. Conventional data architectures often fall short here and prevent companies from realising the potential of their data. 

This is where the data lakehouse comes in - a hybrid data management architecture that combines the best features of data lakes and data warehouses. This article discusses data lakehouses and how they can be the perfect solution for data management.

What is a data lakehouse? 

A data lakehouse is a modern data management architecture that aims to achieve this, to provide scalable storage and flexibility for data lakes and the data management and transactional capabilities of data warehouses. to ensure. 

Data lakehouses help companies to process large volumes of raw data and use them for Business Intelligence to use. They offer a standardised platform on which structured and unstructured data can be Data exist side by side and can be analysed.

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Differences between Data Lakehouse, Data Lake and Data Warehouse

Although all three offer data storage solutions, they are nevertheless different. The following tables summarise the differences between a data lakehouse, a Data Lake and a Data Warehouse explained.

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Differences between Data Lakehouse and Data Mesh

Data Lakehouses and Data Mesh are both innovative approaches to data management. However, they differ in their main focus. Below you will find a table with the most important differences:

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How a data lakehouse works

A data lakehouse functions like a well-organised factory for your data, in which several levels work together. The functionalities and the five layers that are responsible for how it works are explained below:

• Recording layer: This is the entry point. Data from various sources, such as Databasesdata, sensors, social media and log files, are collected and loaded into the lakehouse. Tools in this layer can also perform initial filtering or pre-processing to ensure that only relevant information enters the data lakehouse.
• Storage level: The storage level offers a cost-effective Cloud-based object storage infrastructure for data storage. In this way, you can store all data, regardless of its size or format, without the costs spiralling out of control.
• Processing layerRaw data can be difficult to analyse. In this phase, the Data cleansedorganised and converted into a usable format. Tools such as Spark or Flink are often used for data processing.
• Metadata layerConsider this as a detailed catalogue for your data. It stores information about the data itself, such as its origin, format and meaning. This layer provides structured schemas, supports ACID transactions and improves data management and optimisation functions. 
• Utilisation levelOnce the data has been prepared and organised, it is ready for use. Data analysts, Data Scientists and business users can access the data via the Lakehouse. They can analyse trends, create reports and even create models for machine learning to gain valuable insights.

This five-layer architecture ensures an efficient data flow to make the data usable. It also makes it easier for different teams to work with the specific data they need.

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Advantages of a data lakehouse

The Data Lakehouse offers many advantages for companies that want to improve their data management and analysis capabilities:

• Standardised platformA data lakehouse combines the strengths of data lakes and data warehouses. It can store structured, semi-structured and unstructured data in a single location. This makes it easier for companies to manage all types of data on a single platform. 
• Cost efficiencyThe Data Lakehouse can reduce the costs associated with traditional data warehouse implementations by utilising cloud storage and compute resources. The hybrid architecture also reduces costs by eliminating the need to manage multiple storage systems. 
• Improved data governance: The Data Lakehouse provides centralised metadata management and data access controls to ensure the Data governance and Compliance to improve data security. This approach ensures that data is properly managed and backed up.
• Faster time-to-insightData Lakehouse enables companies to gain insights faster. Data pipelines are streamlined and the need for complex data is minimised. ETL processes reduced. This enables companies to make well-founded decisions more quickly.
• ScalabilityThe Data Lakehouse is designed to be easily scalable. This makes it ideal for processing growing data volumes and different data types. This feature ensures that companies can keep pace with their growing data needs.
• FlexibleThe Data Lakehouse supports various data-related use cases, such as traditional business intelligence, advanced analytics and machine learning. This gives organisations the flexibility to gain insights from all their data.

Integration challenges

Even though data lakehouses are a powerful solution, there are also some challenges for companies to consider:

• Data quality managementWith the flexibility to store any type of data comes the responsibility to ensure its quality. Organisations need appropriate data management processes to avoid problems such as messy or inaccurate data.
• Access controlCentralised data storage requires careful access control measures. Companies must determine who is authorised to access which data in order to avoid security risks or misuse of information.

In our article, we show you why good data quality is the key to reliable processes and how you can ensure this for your company:

The 5 most important measures for optimal data quality

Why should companies use a data lakehouse?

Data lakehouses offer several advantages for companies struggling with their data. Find out below why companies should consider using a data lakehouse:

• Better analyses and AI/ML capabilitiesA data lakehouse offers companies better business insights with advanced analytics and ML capabilities. It helps organisations gain insights from their structured and unstructured data to make better decisions.
• Improved data managementData lakehouses offer companies a single Platform to record and manage all your data. This strategy reduces data silos and ensures consistent data quality.
• Streamlined data pipelinesA data lakehouse combines the properties of a data lake and a data warehouse. This leads to simplified data pipelines and reduces the need for complex ETL processes.
• Support for a wide range of analysesData lakehouses are not just for reports. They provide a platform for advanced analyses such as machine learning. This allows companies to uncover hidden patterns and trends in their data, giving them a competitive advantage.

Data Lakehouse Service Provider

The market for data lakehouses is booming. Several major players offer Cloud-based services on. Below you will find some of the providers and their strengths:

Databricks

Databricks is a well-known data lakehouse provider. It offers features such as Delta Lake, Databricks SQL and MLflow that streamline operations. The Databricks platform is designed for the processing of Big Data optimised and enables advanced analyses and simple data processing. Databricks is ideal for companies that want to have their data in one place and want to be able to analyse Data analyses need. It is also suitable for companies that want to run AI/ML processes on a single scalable platform.

Google BigQuery

Google BigQuery is a serverless platform designed for fast SQL queries. This platform is cost-efficient as it utilises Google's ML functions. Bigquery can also analyse large amounts of data and process complex queries. These features make it an ideal solution for companies looking for a cloud-based data lakehouse with ML potential. 

Snowflake

Snowflake is known for its unlimited scalability. This cloud-based data warehousing service also offers data warehouse capabilities. The difference is that it allows companies to store and analyse their data along with secure data sharing options. For organisations with changing data requirements, Snowflake is a reliable solution that offers flexibility and meets their business intelligence needs.

Dremio

Dremio is another powerful data lakehouse platform. It is known for its simple data integration, the acceleration of queries and advanced analysis functions. It enables organisations to perform SQL queries on data in a data lake without ETL processes. Dermio is the first choice for organisations that need interactive insights from their big data without compromising on speed. Its data processing and analytics capabilities make it a valuable tool for decision making.

Data Lakehouse as an efficient alternative for internal data management

Overall, data lakehouses are ideal for companies that want to break free from the limitations of conventional data architectures. It can help them future-proof their data management processes. While data quality management and access control require careful planning, the benefits of data lakehouses make them a powerful solution for companies looking to maximise the value of their data assets.