What's your strategy for complex data transformations in your pipelines?

Based on my experience in data transformation to create real estate price maps with Databricks: Data cleansing: Utilize tools such as Delta Lake and Apache Spark for thorough data cleansing, removing duplicates and imputing missing values. Use Panda's user-defined functions (UDFs) in Spark for advanced cleansing tasks. Enrichment: Implement data enrichment methods such as geocoding and (real estate) text analysis to improve the quality and depth of the dataset, especially in areas such as real estate where accurate data is essential for creating price maps. Validation: Development of validation checks required for the dozen of attributes to ensure accuracy, consistency and validity of data throughout the conversion process.

As a data engineering team, don't try to own the data quality remediation, always delegate that to the source data provider or the data stewards. always load all data as is and provide a way to review and understand the data quality in the target system. Trying to solve data quality problems in the extraction process or data platform or in the data engineering process is not a feasible process. It also tends to deflect responsibility from where the issues can actually be fixed. So, extract the data as is, don't judge it, and make sure the process allows for the data quality team, data stewards or source team to implement rules or change the data through a convenient process or UI.

In my experience, data quality is important for any successful data project. It's essential to implement data quality checks throughout the data pipeline. However, ensuring data quality is not solely responsibility of data engineers. It requires collaboration across different teams and disciplines including data scientist, domain experts and business stake holders. Additionally, leveraging industry-standard data quality tools like IDQ (Informatica Data Quality) helps to enhance data quality efforts. These tools provide advanced capabilities for data profiling, cleansing, and validation empowering data engineers to streamline the data quality process.

Implement a 'quality-first' approach. This means rigorously profiling data at the source, establishing data quality benchmarks, and continuously monitoring data through automated checks. Utilize data cleansing tools but also encourage a culture where data producers are responsible for the quality of their output.

Align the integration with the business process and the business model. Utilize a modeling approach like ELM to make sure the data is usable from a business perspective and not a source technical perspective. Never use ETL, always use ELT, extract the data as is, save it historized in the PSA, load the next layer from there. As rules and processes and transformations change, the original, unchanged, source version is still available and can be used for lineage, audit, replay or to understand why a complex transformation yielded the result it did. There are always questions as to why something is delivered in the end, making sure the end-to-end process can be tracked is vital

Leverage ETL (Extract, Transform, Load) tools that can handle diverse data sources efficiently. Prioritize metadata management to understand data lineage and transformations. Adopt a modular approach in pipeline design, where integrating a new data source involves plugging in a new module, reducing the overall integration complexity.

If it is not possible to work around any scalability issues from the start by just using a solution without scalability limits, consider approaching the challenges from a business perspective. A technical solution will always be an uphill battle. A business approach will allow the high priority work to happen first, and it might show several different ways to split the data or the processing into more reasonable pieces. Always split any complex process into smaller, more manageable steps. Find ways to reuse these steps, or pre-process or split approaches. Measure each step so that any effort can be spent where it is most needed and brings the most value

Design data pipelines to be scalable from the start. This means using cloud-based data processing frameworks like Apache Spark, which can handle large-scale data processing efficiently. Implement data partitioning and indexing strategies to improve query performance as data volume grows.

Incorporate automated testing in every stage of the data pipeline. Use data validation frameworks to ensure that data transformations adhere to expected formats and business rules. Regularly conduct back-testing with historical data to ensure the pipeline’s logic remains valid over time.

Make sure there is a valid comparison model in case a new process is comparable with an old one. If the old process is opaque or non-deterministic or unknown then comparisons will most likely fail, and it will be impossible to know why, or which one is right (if any). Provide insight into all the middle steps in the broken-down complex processing, so that testing and validation can happen throughout the processing. This will make it easier to find any issues or misunderstandings with the transformation. If comparing against another process, make sure the start/source data for the process match. A cleansing rule or dq process might have been applied somewhere in one and not the other approach.

1. Repeatable Design: Establish modular, well-documented transformation processes using reusable code blocks and functions. 2. Trust in Data Accuracy: Implement rigorous data quality checks and validation rules at every stage of the pipeline. 3. Proactive Scheduling: Utilize scheduling tools to automate your data pipeline execution. 4. Strict Security Protocols: Protect sensitive data by incorporating encryption, access controls, and audit trails throughout the pipeline. 5. Incremental Data Flow & Enhancements: Design your pipelines to handle both batch and streaming data where applicable. 6. Agility: Choose tools that allow you to easily adapt your data pipeline to changing business needs & new data sources. Views are personal

Always start with a solid understanding of the business model and business process. Make sure the process model is based on that process, and not the source technical structures or a wishful thinking of how the target should look. Implement the process in a modular approach, each step should be validated separately, ideally with automated unit testing approaches. Make sure the source data used at the start is safely stored in the data engineering-controlled data platform. Make sure each step can be validated and tested as is and in-flight. Keep track of changes to rules and data, so that any change to end results can be explained by those changes. Make sure the process can be replayed with the historical data from the PSA and the old rules

The following could be considered Monitoring and Logging: Implement comprehensive monitoring and logging to quickly identify and address issues in the pipeline. Flexibility and Adaptability: Build pipelines that can easily adapt to changing data formats and sources without significant rework. Documentation: Maintain detailed documentation of data pipelines and transformations for easier troubleshooting.

Firstly , always make sure to have good understanding of the overall product architecture and pipeline . Next you should have a good understanding of services and coding knowledge . Next comes strategies like security monitoring logging etc