What is the best way to design and implement a data loading architecture for multiple stakeholders?

Designing a robust data loading architecture for multiple stakeholders involves understanding their specific requirements, defining integration points, and implementing efficient ETL processes. Prioritize data quality, security, and scalability, selecting suitable storage solutions and real-time or batch processing as needed. Establish APIs, monitor system health, and provide comprehensive documentation and training. Iterate based on stakeholder feedback, ensuring compliance with regulations, and fostering collaboration among teams. Implement backup and disaster recovery procedures, optimize costs, and regularly update the architecture to adapt to evolving data needs and technological advancements.

For a multi-stakeholder data-loading architecture: Assess Needs: Identify stakeholders' specific data needs and frequency requirements. Scalability: Design for scalability using cloud-based solutions like AWS or Azure to handle varying data volumes. ETL Processes: Implement robust ETL (Extract, Transform, Load) pipelines using tools like Apache NiFi or Talend. Data Quality: Ensure data integrity with validation checks at each stage. Security & Compliance: Apply strict security protocols and comply with data governance policies. APIs for Integration: Use APIs for seamless integration with different systems and platforms. Monitoring & Logging: Implement monitoring tools for performance tracking and error logging.

?? Understand the business context: goals, activities and tools; because these points affect this election! ?? All these points can affect how data will extract it, required time to do, how many times extraction process will do, how data is generated from data source, related point and features of data source, how data must be updated and so on. Based in this content, you choose batch, streaming or real time data loading!

I agree, there are few more data loading methods which can be considered as per requirements and source:- CDC (Change data capture) - Identifying and capturing changes from source data to update destination system accordingly. Tools like debezium , AWS DMS has these features. Database migration - Migrating data between two systems

- Identify Stakeholders and Their Requirements: Begin by identifying all stakeholders involved in the data loading process, including business users, data analysts, data engineers, and compliance teams. Understand their specific data requirements, data access needs, and performance expectations. - Define Data Sources and Destinations: Map out the various data sources from which data will be ingested, such as transactional databases, external APIs, and legacy systems. Also, identify the destination data stores where the loaded data will reside, such as data warehouses, data lakes, and analytics platforms.

? Remember that any data loading architecture solution is subject to business needs and context; therefore it can evolve from components through hardware to design. Some points can help are: ?? Use tools can help evolve easier. Apache beam for data pipeline if apply. ?? Be modular with components as possible: get data pipeline and data warehouse like independent components, for example. ?? Document all data architecture aspect: opinions, evaluations, list of data sources, business context, decision, diagrams, setups and so on. ?? Work with worst cases in mind: how detect problems of data quality or scale resource based in capacity.

One thing I’ve noticed recently is moving data into object storage because of the obvious benefit of saving costs. Storage has become cheaper, query engines have gotten smarter, cataloguing options have become more reliable. A common pattern if you’re moving data from operational or transactional databases to analytical systems, CDC can be super helpful. Have your databases write to a kafka topic through debezium, and sink it to S3. Then pick it up from S3 using EMR and write back to S3 after some data cleaning or optimizations. Now you have the data that can be used for analytics.

Modular Design: Create a modular architecture allowing flexibility and customization for different stakeholder needs. Central Data Repository: Use a central data warehouse or lake to consolidate data, ensuring consistency and accessibility. Data Governance: Establish clear data governance policies for quality, security, and compliance. Tailored Access: Implement role-based access control to ensure stakeholder access. Automation and Scheduling: Automate data loading processes with scheduling tools to maintain up-to-date data. Scalable Infrastructure: Opt for cloud-based solutions to easily scale resources. Monitoring and Feedback Loop: Integrate monitoring tools for performance tracking and establish a feedback mechanism.

You can consider a healthcare scenario where patient records come from diverse sources. The cycle starts at data ingestion involves extracting patient data from Electronic Health Record (EHR) systems, processing it through validation checks, and loading it into a centralized storage system. This architecture will ensure a unified view of patient information, facilitating comprehensive and accurate medical analyses.

Moreover, some other general practices include: - Fostering collaboration between IT and business teams to establish a streamlined feedback loop for continuous refinement based on evolving business needs - Incorporating a robust version control system for data pipelines ensuring traceability and facilitating rollback in case of unexpected issues - Consideration of data privacy and security measures, such as encryption and access controls, adding an extra layer of resilience to the architecture - Continuous monitoring and proactive alerting mechanisms to promptly address any anomalies, ensuring the ongoing reliability of the data loading process and maintaining stakeholder confidence in the system’s integrity

Well, let us take financial services as an example, where stakeholders have distinct needs, careful prioritization is crucial. Also, for regulatory reporting, batch loading ensures accuracy, while real-time fraud detection relies on streaming methods. Implementing data quality checks becomes paramount, for example, in fraud detection, mistaken data can lead to false positives, impacting business decisions

Some best practices in data loading are: ??Create a module to handle all connection to data sources in pluggable mode. ?? Implement a processing servers with scaling feature from start. ?? All meetings with business use project management plan to explain your advance and progress.

Challenges can be really overwhelming, imagine a scenario in a multinational corporation where data comes in multiple languages and formats. Handling this diversity poses integration challenges, emphasizing the need for standardized data models and effective metadata management. Additionally, strict data privacy regulations demand robust security measures, necessary encryption and access controls to safeguard sensitive information across borders.

?? Capacity management: how will capacity of CPU, RAM, disk and I/O network i need? ?? Cost-efficient resources management: What is minimum amount of resource will i need in wide range cases while save cost? ?? Review of privacy: How law and regulations affect my process of extracting and loading data? ?? Failures management: how will it work in specific cases of failures in automatic way?, how many time to retry the loading or extraction? or how will work when it face expected change in data structure or mapping? ?? workflow management : how handle data to process and load it?

1) When Data follows 3 Vs, it's not straight forward to carry data loading. Ensuring data quality to the consumers would be the most difficult task among others. It's possible that the schema and the attributes of the data source may have evolved over time so same rules may not apply when performing data loading. Understanding trends, changes, and advancements in data and processes is crucial to promise a degree of quality in data loading. 2) What if the requirement is to join or merge a stream data source with batch data? 3) What if you need data which is present in two or more different systems/databases like Google Cloud, Azure, On premise?

Its also worth looking into: 1. Am I making use of my pipelines to the full extent? 2. Is my pipeline configured with autoscaling to handle burst workloads? 3. Is my pipeline capable of handling merges wherever required? 4. Is my pipeline writing data in an optimal fashion i.e. buffer records, target file format, CDC transformations etc.

In my opinion, you can following any of approach in data loading solutions: ?? cloud provider give some tools to implement your own data loading platform as well specific purpuse build-in tools to use. ??? Specific purpuse third tools can use to implement it; it approach is called modern data stack. ?? Tools created by communities like apache foundation can help your this process. Choose it based in business buget, knowledge and expertise of data team, tool usability and maintainance as well other required view points.

Types of Data Integration Architecture: Hub and Spoke: In this architecture, a central hub collects data from various sources and distributes it to multiple endpoints or systems. This allows for centralized control and management of the data flow, making integrating new data sources easier. Bus: In this architecture, a central bus is used to integrate data from multiple sources. The bus acts as a conduit for the data, allowing it to flow between different systems. This architecture can be useful for real-time data integration, as it allows for rapid data transfer between systems. And two are more pipeline, Federation.

In my experience, this is one of the rare use cases where several mature Data Integration tools are available to take care of different data loading requirements. Across different data sources, targets, processing, security and scalability needs and whether in streaming or batch mode. So this big ask can be easily solved by selecting the right DI tool sitting at the heart of the architecture and design. If for instance the target is always going to be databricks data lakehouse, then the databricks platform itself provides all the features needed, in one place. If multiple data targets need to be supported, one can look at the Data Integration Magic Quadrant from Gartner and select one of the tools from that, depending on exact needs.

Its important to remain as open as possible and be cloud native. There are variety of open source tools or managed products that strive to keep your data open but also optimize the workloads. Imagine loading ODS data to a EDW: 1. Use Debezium connector to write to a Kafka cluster. 2. Use Kafka S3 connect to write data in JSON/Parquet format to S3. 3. Use Spark/EMR to read the Parquet data in S3 and write as Hudi tables back in S3 (Silver area). 4. Catalog in Glue (uses HMS in the backend). 5. Use Athena (v3 uses Trino in the backend) to read the Silver tables and write back to S3 (Gold area). 6. Use a BI tool of choice to draw insights. Without cloud, it would taken months to set this whole thing up in addition to the engineering cost.