Nowadays, data warehousing solutions largely rely on cloud resources. At Islet, a large portion of new solutions are implemented as Lakehouse solutions, either on top of Synapse Analytics or Databricks. The implementation is determined based on requirements, and the data warehousing solution is chosen based on which technology best fulfills these requirements.
In rare cases, the use of cloud services is initiated by bringing along part of the old solution. These few cases are, for example, those where the original solution is so broad or important to maintain continuity of operations. We will go through the ways in which the transition to the cloud can be made by bringing along part of the old solution.
Benefits of cloud
In this post, we will go through ways to start using Microsoft’s cloud services easily and effortlessly in scope of data and analytics. Cloud services offer several benefits, such as flexibility, performance, and cost-effectiveness.
Flexibility is one of the most significant benefits of cloud services. Microsoft offers various services and solutions that can be scaled according to your needs. You can easily increase or decrease capacity and resources in line with your business requirements. This gives you flexibility and the ability to adapt quickly to rapidly changing business conditions.
Performance is another important advantage that cloud services provide. Microsoft’s cloud services are designed to provide high performance and efficient use of resources. You can leverage the scalability and performance offered by cloud services when dealing with large amounts of data or performing heavy computation tasks. This ensures the efficiency and speed of your business operations.
Cost-effectiveness is the third critical factor that attracts businesses to use cloud services. Microsoft’s cloud services offer flexible pricing models where you pay only for the resources used. You don’t need to invest a large initial capital or maintain expensive infrastructures. Along with scalability, cloud services also offer the opportunity to optimize costs and pay only for the services you need.
Traditional Data Warehouse Solution
A traditional data warehouse solution implemented with Microsoft products is usually based on the SQL Server, SSIS packages for data transfer, and scheduling with SQL Server Agent.
SQL Server is a well-known and reliable database solution that provides an effective foundation for a data warehousing environment.
SSIS (SQL Server Integration Services) is an ETL (Extract, Transform, Load) tool provided by Microsoft, which simplifies data transfer between different systems.
Scheduling with the SQL Server Agent provides automation and scheduling in the data warehousing environment.
This combination of SQL Server, SSIS packages, and SQL Server Agent has provided a solid foundation for data management and analysis.
How do we proceed from this traditional solution towards the cloud?
The first step is to decide which parts of the current data warehouse you want to bring to the cloud. You can choose a certain or all parts depending on your needs and business objectives. It’s important to ensure and double check that the solutions to be transferred to the cloud are compatible with the new environment so that the transition is seamless.
A traditional data warehouse solution implemented with Microsoft products is well-suited for a lift-and-shift type of cloud migration.
By moving an SQL Server data warehouse located on your own servers, as well as the SSIS runs, to the Microsoft’s Azure SQL Managed Instance, the old solution can be migrated with as little effort as possible, while also enabling the development of new cloud-native solutions.
Azure SQL Managed Instance provides nearly 100% compatibility with local SQL Server, which facilitates the transition from local data warehouses to the cloud. In addition, executing SSIS packages in Azure Data Factory allows existing ETL processes to be used with minimal changes.
Cloud-based data warehouses offer a more cost-effective way to scale resources as needed. Customers can reduce costs by moving from acquiring hardware used by the data warehouse to only paying for the resources used in the cloud. This avoids committing significant costs… for example every four years, and costs can be monitored and adjusted according to the resources used. Most resources in the Azure cloud offer scalability, as well as the option to shut down resources when they are not in use.
Azure SQL Managed Instance provides scalable performance compared to a local data warehouse. Scalable resources ensure that system capacity adapts to customer needs and enables efficient work with large amounts of data. Rarely there are situations where the resources offered by Azure would no longer provide scalability. The architecture must always consider the nature of different resources, the usage requirements of the solution, and thereby make the right resource choices.
With cloud services, customers can take advantage of advanced resource security that are continuously updated along with the service. In this case, the upkeep of keeping updates up to date is transferred to the cloud service provider. Too often, local solutions have lagged in updates or have even fallen outside of updates due to outdated operating systems.
Continuous Development of Cloud-Native Solutions
The solution doesn’t usually stop when moved to the cloud. This is only the first step towards cloud-native development. To enable further development, you can create new environments and use the services offered by the cloud even more efficiently.
When cloud migration reaches the point where rapid transition to the cloud has been achieved based on the original solution, then we need to consider how the development of the new solution will be implemented. In this case, it makes sense to continue with a cloud-native solution, which means gradually moving away from an SQL Server and SSIS-based solution.
Below is an example of the benefits of resources used for cloud-native development, for example if Azure Synapse is chosen for cloud-native development.
- Massive scalability: Data Lake-based solutions are highly scalable, enabling the storage and processing of vast amounts of data. Azure Synapse, together with Azure Data Lake Storage Gen2, provides high performance and scalability for both structured and unstructured data.
- Flexibility: Azure Synapse’s Data Lake solution accepts various data types and formats, such as text files, images, videos, and JSON files. This flexibility enables the handling and integration of complex data structures, allowing companies to analyze a wider range of data sources.
- Cost-efficiency: Data Lake-based solutions like Azure Synapse offer an affordable storage option for large amounts of data. Storage costs remain low because you only pay for the storage space and computing resources used.
- Data management: Azure Synapse and Data Lake Storage Gen2 together offer an effective data management solution. A hierarchical storage structure and metadata management enable easier classification, retrieval, and organization of data.
- Data security and compliance: Azure Synapse includes advanced security features such as data encryption at rest and in transit. In addition, it adheres to strict data protection standards, such as GDPR, helping companies ensure data protection and compliance.
- Real-time analytics: Azure Synapse and Data Lake solution enable real-time analytics in handling large amounts of data. This allows for quick insights and supports data-driven decision making.
- Compatibility and integration: Azure Synapse is compatible with many cloud services and tools, including Azure Machine Learning, Power BI, and Azure Data Factory. This facilitates data flow management, analytics, and visualization.
To enable cloud-native development, it is crucial to create new environments and utilize cloud services such as Azure Synapse and Data Lake. With these, you can continue to develop your solution, scale resources as needed, and leverage the full benefits offered by the cloud.
Microsoft also offers other cloud-native development models to support data and analytics solutions. You might see something about those in our Islet blogs… stay tuned!
Mikko and Ilkka
PS. If interested, read more about our Data & Analytics services here
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