Azure Synapse Analytics as a Cloud Lakehouse: A New Data Management Paradigm

Summary

Azure Synapse Analytics represents a new cloud lakehouse paradigm, integrating data warehouse and data lake capabilities to streamline data management and analytics.

Abstract

Azure Synapse Analytics is positioned as a cloud lakehouse solution, merging the functionalities of enterprise data warehouses (EDW) and data lakes to address the evolving needs of big data architecture. This approach supports both structured and unstructured data, providing AI and BI capabilities, end-to-end streaming, schema enforcement, governance, and cost efficiency. It is designed to enhance productivity and efficiency while reducing the risks and costs associated with data warehouse and data lake consolidation and modernization. Unlike traditional data warehouses like AWS Redshift or GCP BigQuery, Azure Synapse Analytics offers a workspace that caters to a wide range of users, from business analysts to data scientists, and supports both low code/no code interfaces and Jupyter-like notebooks for advanced analytics.

Opinions

The author believes that data lakes and enterprise data warehouses (EDW) will coexist, evolving into a cloud lakehouse model.
Azure Synapse Analytics is highlighted as an exemplar of the cloud lakehouse, providing a unified platform for diverse data management and analytical needs.
The integration of data warehouse and data lake functionalities within Azure Synapse Analytics is seen as a simplification of big data architecture.
The article suggests that Azure Synapse Analytics' workspace concept bridges the gap between business analysts and data scientists, offering a versatile environment for various data tasks.
The author notes that while Azure Synapse Analytics inherits robust data security practices from relational databases, there is a maturity gap in data security on the data lake side that needs to be addressed.
The Databricks Platform is mentioned as having architectural features aligned with the lakehouse concept, indicating a broader industry trend towards integrated data platforms.
The author anticipates that other managed services will likely follow Microsoft's lead in offering integrated singular data platforms in the future.

Will enterprise data lake & enterprise data warehouse (EDW) coexist?

In the early days of data repository, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a commonly known system used for reporting and data analysis, and is considered a core component of business intelligence. Extract, transform, load (ETL) and extract, load, transform (E-LT) are the two main approaches used to build a data warehouse system. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place. It may sound remarkably similar to the definition of a data lake. In particular, data lake can store both structured and unstructured data in whatever form the data source provides. See Data Lakes compared to Data Warehouses — Two Different Approaches (AWS) for the detailed characteristics of the two.

Many cloud solution providers often bundle the two offerings in their proposed big data architecture, positioning data lake as a storage layer and data warehouse as a model & serve layer.

The question now becomes will Data Lake & Enterprise Data Warehouse (EDW) coexist? The answer is yes! We call this new emerging pattern as a cloud lakehouse, bringing the best of data warehouse and data lake altogether and simplifying the big data architecture. Some highlighted benefits include:

AI + BI support.

Support for both structured and unstructured.

End-to-end streaming.

Schema enforcement and governance.

Drive greater productivity and efficiency.

Reduce risks and costs of data warehouse and data lake consolidation and modernization.

Unlike AWS Redshift or GCP BigQuery, Azure Synapse Analytics is considered an example of a cloud lakehouse. “Azure Synapse uses the concept of workspace to organize data and code or query artifacts. And the workspace can surface as a low code/no code tool for business analysts or a Jupyter-like notebook for data engineers and data scientists to work in Spark or apply machine learning models. In the demos, Microsoft showed how the same data transformation task could be developed using both paths. There will be some differences in the experience — for instance, while Synapse inherits the Azure SQL Data Warehouse capability to support high concurrency, Spark environments have typically involved lone wolf data scientists or data engineers. There’s also differences in levels of data security — practice is far more mature on the relational database side with table, column, and native row-level security, but not as mature on the data lake side. That’s an area where Cloudera differentiates with SDX, which is available as part of its platform offerings.” (A closer look at Microsoft Azure Synapse Analytics, Tony Baer (dbInsight) for Big on Data, April 14, 2020). “The Databricks Platform has the architectural features of a lakehouse”. As Microsoft is setting forth in the trend of integrated singular data platform, other managed services are likely to go in the similar pattern in the future.

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References

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