Data management is a crucial component of any data-driven organization. Faced with large volumes of data and increasing complexity, organizations are in desperate need of solutions that can effectively handle and make sense of this data. Among the plethora of solutions available, one that stands out is Microsoft Fabric, a new all-in-one analytics platform that covers everything from data engineering to data science, real-time analytics, and business intelligence.
In this article, we will examine the differences between the traditional data warehouse and Microsoft Fabric Lakehouse, and provide an argument why the latter might be a better option for companies
Understanding Data Warehouse and Microsoft Fabric Lakehouse
First, let’s understand the two terms. A data warehouse is a type of data management system specifically designed to store structured data from different sources. This design supports business intelligence (BI) activities. It primarily serves SQL developers and relies heavily on SQL for data operations. Data warehouses excel at performing queries and analysis and contain large amounts of historical data.
On the other hand, Microsoft Fabric Lakehouse is a new kind of data architecture platform that combines the best of data lakes and data warehouses. It can handle structured, semi-structured, and unstructured data, making it a more versatile tool. Primarily, it primarily serves data engineers and data scientists, and relies on Apache Spark for much of its functionality.
Comparing the Key Features of Data Warehouse and Lakehouse
| Data warehouse | Lakehouse | |
| Data volume | Unlimited | Unlimited |
| Type of data | Structured | Unstructured,semi-structured,structured |
| Primary developer persona | Data warehouse developer,SQL engineer | Data engineer,data scientist |
| Primary developer skill set | SQL | Spark(Scala, PySpark, Spark SQL, R) |
| Data organized by | Databases, schemas, and tables | Folders and files, databases, and tables |
| Read operations | Spark,T-SQL | Spark,T-SQL |
| Write operations | T-SQL | Spark(Scala, PySpark, Spark SQL, R) |
| Multi-table transactions | Yes | No |
| Primary development interface | SQL scripts | Spark notebooks,Spark job definitions |
| Security | Object level (table, view, function, stored procedure, etc.),column level,row level,DDL/DML | Row level,table level (when using T-SQL),none for Spark |
| Access data via shortcuts | Yes (indirectly through the lakehouse) | Yes |
| Can be a source for shortcuts | Yes (tables) | Yes (files and tables) |
| Query across items | Yes, query across lakehouse and warehouse tables | Yes, query across lakehouse and warehouse tables;query across lakehouses (including shortcuts using Spark) |
These two technologies differ significantly in their capabilities. Although both can handle very large volumes of data, they cater to different developer personas and require distinct skill sets. The data warehouse primarily relies on SQL, while Lakehouse leans towards Spark-based languages, such as Scala, PySpark, Spark SQL, and R.
Data organization also shows stark differences. In the case of the data warehouse, it uses databases, schemas, and tables, while the Lakehouse employs folders and files, in addition to databases and tables. Notably, the read and write operations diverge, with data warehouses using Spark and T-SQL for reads but exclusively relying on T-SQL for writes. In contrast, Lakehouse employs Spark for both operations.
When it comes to security considerations, there are distinct differences between the two options. Specifically, the data warehouse provides comprehensive security at the object level (table, view, function, stored procedure, etc.), column level, row level, and DDL/DML. In contrast, Lakehouse provides security at the row level and table level (when using T-SQL), but surprisingly does not offer any security measures for Spark.
As far as the development interface is concerned, developers primarily resort to SQL scripts when developing the data warehouse. However, Lakehouse shows a clear preference for Spark notebooks and Spark job definitions.
So, Which One to Pick?
Now we arrive at the most important question – which is the better option, the data warehouse or Microsoft Fabric Lakehouse? To answer this, consider your team’s skill set, the type of data you are working with, and the specific requirements of your data operations.
Team Skillset for Data Warehouse
The choice between the data warehouse and Lakehouse should consider a team’s skillset. If your team primarily consists of SQL developers, then a traditional data warehouse might make more sense.
For instance, a skilled SQL developer has extensive experience creating data warehouses using relational database engines and has proficiency in SQL syntax and operations. Furthermore, when considering the larger team within a company, it is clear that the main users of this data are well-versed in SQL and its associated analytical tools.
In this case, choosing a data warehouse is sensible. This choice allows the team to work mainly with T-SQL, and at the same time, it provides any Spark users within the company with data access. This case demonstrates how the team’s skill set is an important consideration in the decision-making process.
Team skillset for Lakehouse
However, if your team is proficient with Spark, PySpark, and T-SQL, then a Lakehouse might be the way to go. To make the most of this team’s diverse skillset, you should choose a lakehouse solution. This strategy empowers the data engineering team to employ their various skills when working with data.
This example shows that Lakehouse might be the best option when handling large volumes of data with a team that has a wider skillset. It also demonstrates the adaptability of a Fabric Lakehouse, which can accommodate a multitude of data types and formats, thereby providing a greater level of flexibility for data management and analysis.
Type of Data
The type of data you’re working with should also influence your choice. If you mostly handle structured data, the data warehouse should suffice. However, if you are dealing with a variety of data types, like JSON, or anticipate that your data format or volumes might change significantly in the future, then Lakehouse would be the better choice.
Write Operations and Development Interface
Your choice should also consider the language requirements and primary development interfaces of your operations. The data warehouse is SQL-based, making it ideal for processes that exist within the SQL environment. If your business logic and processes already exist in stored procedures within an existing SQL database, migrating data to a warehouse would make more sense.
If you are migrating from an existing Synapse environment, the data warehouse option might be a better choice. Moving to a Lakehouse artifact would require more time to rebuild everything with notebooks.
Alternatively, a Spark-based Lakehouse would be ideal if you anticipate a need to rebuild processes with notebooks.
Conclusion
The traditional warehouse and Microsoft Fabric Lakehouse each have their own advantages and are suited to different scenarios. Ultimately, the choice comes down to what you are working with and what you need to achieve.
Remember that the persona approach is crucial as well. You need the right skills in your team to engineer and support the solutions that you create. If you have a mix of SQL developers and Spark engineers, then you could utilize both options according to your team’s preferences and the requirements of different projects.
Microsoft Fabric Lakehouse is a more flexible, scalable, and robust data management solution for organizations dealing with diverse and changing data types. Its Spark-based operations, security options, and development interfaces make it a versatile and future-ready choice for data-driven organizations.