How do cloud data warehouses integrate with existing on-premises databases?

Cloud DWH can integrate with existing on-premise DB through a hybrid approach, which basically means they work together seamlessly. Data from the on-premise database can be securely transferred to the cloud warehouse, allowing for unified data storage and analysis across both environments. Pros of the Hybrid approach are Flexibilty for migration, security control over sensitive data , Scalability in terms of workloads and on-demand cloud instances etc. But there are few downsides like complexity in managing both cloud and on Prem systems , cost involved in integration tools plus infra and perhaps potential data latency.

You can start replicating existing data from on -premises DE to cloud while building new solutions data warehouse on cloud. For data replication tools like Data build tool are very useful

I see a lot of customers when they first start out with data warehousing in the cloud take a hybrid approach. Gradual Transition: One of the primary benefits of a hybrid model is the ability to manage a gradual transition. Businesses can continue to operate their on-premises systems without disruption while slowly migrating data to a cloud environment. Leveraging Cloud Benefits: By integrating with a cloud-based data warehouse, companies can tap into advanced computational resources and scalability that the cloud offers. This is particularly valuable for big data analytics and complex computing tasks that are resource-intensive.

Adopt a hybrid approach that combines on-premises and cloud-based resources to leverage the strengths of both environments. Determine which data and workloads are best suited for migration to the cloud and which should remain on-premises based on factors such as security, compliance, and performance requirements.

Organizations must implement mechanisms to ensure that data replicated from on-premises databases to the cloud warehouse remains accurate, consistent, and reliable throughout the replication process.

Some points on this --> 1. ETL is essentially a batch process and has the same problem that all batch process has - maintenance, longer window with more data changes etc. 2. You can fine tune the "lag" between on-prem and cloud 3. There is also "real time" or "near real time" replication - that reduces the lag greatly. 4. In general you want to avoid having databases in both places so you do not have to maintain this at all.

Data replication is a good approach, however the data preprocessing and Compatibility should be taken into consideration prior to this process. All the parameters like indices , keys etc needs to taken seriously for the smooth replication of data from On Premise to Cloud.

Cloud data warehouses integrate with on-premises databases through methods like data replication, ETL/ELT processes, real-time streaming, API integration, federated queries, hybrid cloud solutions, and data virtualization. These approaches enable seamless data movement, synchronization, and unified analytics across on-premises and cloud environments, facilitating scalable storage and processing for data-driven insights.

Configure the replication process meticulously, defining schedules, transformation rules, and error handling mechanisms. Continuous monitoring and validation are essential to ensure data integrity throughout the replication process. Optimize replication performance by closely monitoring and fine-tuning schedules, rules, and network configurations. Implement robust security measures, including data encryption, access controls, and regular audits, to protect data during replication.

Establish secure connectivity between on-premises infrastructure and the cloud data warehouse to ensure data transmission integrity and confidentiality. Use virtual private networks (VPNs), dedicated connections, or secure gateways to establish encrypted tunnels and facilitate seamless communication between environments.

Auditing and monitoring tools help detect security incidents, unauthorized access attempts, and compliance violations, enabling timely intervention and response.

Always Ensure Secure Connectivity let me put a example how important it is Scenario: A retail company integrates its on-premises MySQL database with Amazon Redshift for analytics. Steps: Establish VPN or Direct Connect between on-premises network and AWS VPC. Implement SSL encryption for data transfer. Configure security groups to control traffic. Use IAM roles for access control. Outcome: Enables seamless data transfer for analytics while ensuring security and compliance.

Tools and software platforms that support schema mapping automation can automatically map data structures, identify data type conversions, and resolve naming conflicts, saving time and effort in integration projects.

Schema mapping is part of the ETL process above - covered under the topic of "maintenance". Realize that the on-prem can be a relational database like SQL server or Postgres with defined schema and the cloud may be different types of databases - perhaps a datalake - with "schema on read" i.e. it is upto the reader to interprete the schema. Also very important to future proof this design with LLM in mind, very soon your AI will run on this so this interpretation of data is crucial for AI model to work properly and be explain a decision based on the data.

Map the schemas and data models of on-premises databases to those supported by the cloud data warehouse to ensure compatibility and consistency. Transform and harmonize data structures as needed to align with the target schema, taking into account differences in data types, conventions, and constraints.

There are several options to implement it on your data infrastructure: AWS Data migration service (cloud), Debezium (open source) or fivertran (provider). Evaluate how these options fix your integration need.

Maintain a record of synchronization activities, including timestamps, source of changes, and any transformations applied. Implement data validation checks to ensure the integrity and accuracy of synchronized data.

Continuous synchronization is an extension of ETL, step 3 above. Linkedin will do readers a lot of good if these articles are shortened - to help both readers and writers:-) I hope the model sees this comment.

Implement continuous synchronization processes to keep data up-to-date between on-premises and cloud environments. Schedule and automate data synchronization tasks to ensure timely and accurate updates, minimizing latency and ensuring data consistency across systems.

Provide training and awareness programs to educate employees and stakeholders about data governance policies, procedures, and best practices.

Establish robust data governance policies and procedures to govern data access, usage, and security across hybrid environments. Implement role-based access controls (RBAC), encryption, and auditing mechanisms to enforce data governance standards and regulatory compliance. Monitor and manage data lineage, quality, and metadata to ensure visibility, accountability, and trustworthiness of data assets.

you should consider whether the warehouse to warehouse migration is what you want. I would seriously consider warehouse to lake house migration fist. Then if there are capabilities that a Lake house investment cannot provide, I might consider building a warehouse to supplement it or maybe investing in a more sophisticated semantic model technology. Also, I would think about the information landscape to include more than schema based data, like vector databases, etc.

Document what you did. Run your AI LLM app on the doc to make sure they are current with your architecture. Your successor will thank you !