Data architecture principles to accelerate your data strategy
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What are the data architecture principles you should be applying to your project design to ensure a successful outcome? In this session (see link to full webinar at the bottom) we're walking through some of the basic elements of data architecture and some of the common patterns we’ve seen in projects. And we’ll show you how you can make your projects easier to maintain and improve as your data needs evolve. Some of the key principles include: Data validation at the point of data entry – how to ensure your projects aren’t derailed by bad data Consistency – how and why you should be documenting your architecture and development practices Avoiding duplication – how you should be thinking about reusing code to improve project maintainability Watch the full webinar at https://www.cloverdx.com/webinars/data-architecture-principles-to-accelerate-data-strategy
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Data architecture principles to accelerate your data strategy
- 1. Data architecture principles to accelerate your data strategy Jan Slechta Senior Data Engineer @ CloverDX
- 2. Breaking down complex processes Avoiding duplicate functionalities Consistency Data quality Documentation Key principles
- 3. Maintenance over time o Development team productivity o Cost-effectiveness Trust in process and in data o Transparency o Completeness of the process Why do these matter?
- 4. Breakdown complex process into simple elements
- 5. Data pipelines maintainable in long-term Completeness of the process Development team productivity Better test coverage Robust solution Trust in process Why is this important?
- 6. Maintainability o Our stored procedures are too complex, and the author left the company. Real world issues
- 7. Maintainability o Our stored procedures are too complex, and the author left the company. Efficiency o Team of four developers is slow and cannot work in parallel. Real world issues
- 8. Maintainability o Our stored procedures are too complex, and the author left the company. Efficiency o Team of four developers is slow and cannot work in parallel. Completeness o We forgot to implement auditing and we don’t know how to add it to the existing process. Real world issues
- 9. Maintainability o Our stored procedures are too complex, and the author left the company. Efficiency o Team of four developers is slow and cannot work in parallel. Completeness o We forgot to implement auditing and we don’t know how to add it to the existing process. Trust o Often after deployment of new feature, our pipelines unexpectedly break. Real world issues
- 10. Large jobs are common sign of bad architecture How to break the job into smaller pieces? Transfer files to cloud Load into Snowflake Build Models
- 11. Identify individual components of data pipelines Each job should deal with a single task How to break the job into smaller pieces? Log Ingest Log Log Log Validate Transform Deliver Transfer files to cloud Load into Snowflake Build Models
- 12. Ask questions o What is the purpose of the process, and what is its business impact? o What interfaces are you going to use? o How would you like to automate the process? o What are the weak points? o How to handle errors? How to break the job into smaller pieces?
- 13. Ask questions o What is the purpose of the process, and what is its business impact? o What interfaces are you going to use? o How would you like to automate the process? o What are the weak points? o How to handle errors? Identify patterns o Repeatable and configurable code sections o Logging, monitoring, automation, … How to break the job into smaller pieces?
- 14. Reuse functionality Avoid copy-paste by building and reusing generic jobs for common tasks Logger
- 15. Beauty of keeping it small <10 steps in each design helps understand and maintain the process Parse Data File
- 17. Standardize process Increased developer productivity Faster turnaround Increased trust Reduced cost of business processes Why avoid duplicating functionality?
- 18. Productivity o Implementing a single change to our core process required updates to nearly 80 jobs. Real world issues
- 19. Productivity o Implementing a single change to our core process required updates to nearly 80 jobs. Consistency o During internal audit, we realized that auditing components do not log at the same level of detail. Real world issues
- 20. Avoid duplication by modular design Source Business logic Target
- 21. Avoid duplication by modular design Source Business logic Target New Source No additional cost of adapting new source
- 22. Avoid duplication by modular design Source Other Business logic Other Target Build new pipeline with the same source
- 23. Functional reusability in CloverDX Pipeline 1 – PII detection Pipeline 2 – Publishing to web Shared Source
- 25. Help you understand the jobs among them team Prevent data issues Will help you identify errors easier Help meet SLAs Why strive for consistency?
- 26. Data quality o Some data fields are not populated although the data is in the source. Real world issues
- 27. Data quality o Some data fields are not populated although the data is in the source. Team productivity o We don’t have good approach for team collaboration. Before each release we spend days fixing the conflicts when all teams deliver their work. Real world issues
- 28. Data quality o Some data fields are not populated although the data is in the source. Team productivity o We don’t have good approach for change management. Before each release we spend days fixing the conflicts when all teams deliver their work. Consistency o Each developer approaches the task differently and the jobs are difficult to monitor in production. Real world issues
- 29. Naming conventions Documentation conventions Development conventions o Break down where customization is expected o Versioning and teamwork related conventions Set expectations and provide training o Trainings will increase productivity (data integration platform, version control, etc.) Define conventions
- 31. Bad data = Cost o Correction o Penalties o Lost business Accurate data to support business Efficient data process Adaptability and recoverability from data issues Why data quality matters?
- 32. Distort data reports o Because we did not check data set quality, we not only had to build another complicated clean up process, but we were also running our business based on wrong sales results. Real world issues
- 33. Distort data reports o Because we did not check data set quality, we not only had to build another complicated clean up process, but we were also running our business based on wrong sales results. Unable to deliver o We have identified an issue in the pipeline, but we can’t fix the data as we do not store delta sources from our transactional systems. We can’t implement our new use case. Real world issues
- 34. Distort data reports o Because we did not check data set quality, we not only had to build another complicated clean up process, but we were also running our business based on wrong sales results. Unable to deliver o We have identified an issue in the pipeline, but we can’t fix the data as we do not store delta sources from our transactional systems. We can’t implement our new use case. Data quality check is too slow o Profiling source helps us deliver better data, but the process is too slow; and we cannot meet our SLA. Do we remove data quality checks? Real world issues
- 35. Always expect poor data quality Validate early to keep SLA and reduce downstream burden Avoid unnecessary validation Reuse validation rules for consistency Data quality basic principles
- 36. Fixing the data may require original source and human review Keep the source data in staging environment Delta records might be sufficient Prioritize business critical data in storage Keep source data
- 38. Data processes evolve over time People forget or leave Quickly understand the process Maintain more effectively over many years Why is documentation important?
- 39. Job design is documentation too – smaller jobs are easier to understand Document wisely and to the point Pay special attention to interfaces and reused jobs Set documentation conventions Documentation
- 40. Quick recap Key principles Breakdown complex processes Avoid duplicating functionality Aim for consistency Maintain data quality Documentation
- 41. Upcoming Webinars 5 Characteristics of modern data architecture that drive innovation March 23rd Q&A www.cloverdx.com/webinars
Editor's Notes
- Maintainability: Your process will become extensible Completeness You will not forget about other critical elements of the process Efficient development process Enables teamwork Shorter development phase Smaller code base
- Split responsibilities between components
- Split responsibilities between components
- Ideal pipeline has up to 15 components One job should not do multiple things
- Ideal pipeline has up to 15 components One job should not do multiple things
- Multi-layer architecture Abstraction with possibilities to drill-down to more details
- Removes redundancy Smaller code base Standardize process Increased transparency and trust Shorter time to deliver updates Saves time and costs Easier scalability
- Process reusability – framework Configuration in DB or ERP, CRM etc. Pipeline reusability Subprocess (e.g. Data staging) Functional reusability Single unit / function reused in pipelines of different purpose etc.
- Three levels of reusability Process reusability (i.e., set of pipelines configured via external configuration) Pipeline reusability (e.g., sub-process reusability) Functionality reusability (e.g., logger, notifier, transformer, formatter, encryptor,…) Process reusability – framework Set of pipelines configured via external configuration Configuration in DB or ERP, CRM etc. Pipeline reusability Subprocess reusability (e.g. Data staging) Functional reusability Logger, notifier, transformer, formatter, encryptor,… Single unit / function reused in pipelines of different purpose etc.
- Modular design – you can easily change parts of the process without affecting the rest Three levels of reusability Process reusability (i.e., set of pipelines configured via external configuration) Pipeline reusability (e.g., sub-process reusability) Functionality reusability (e.g., logger, notifier, transformer, formatter, encryptor,…) Process reusability – framework Set of pipelines configured via external configuration Configuration in DB or ERP, CRM etc. Pipeline reusability Subprocess reusability (e.g. Data staging) Functional reusability Logger, notifier, transformer, formatter, encryptor,… Single unit / function reused in pipelines of different purpose etc.
- For example you can replace the source with a new source (e.g. you replace your CRM with a different product, you switch cloud providers, etc.) With good modular design you only implement the source change and WON’T HAVE TO touch the rest of the pipeline time & cost savings Three levels of reusability Process reusability (i.e., set of pipelines configured via external configuration) Pipeline reusability (e.g., sub-process reusability) Functionality reusability (e.g., logger, notifier, transformer, formatter, encryptor,…) Process reusability – framework Set of pipelines configured via external configuration Configuration in DB or ERP, CRM etc. Pipeline reusability Subprocess reusability (e.g. Data staging) Functional reusability Logger, notifier, transformer, formatter, encryptor,… Single unit / function reused in pipelines of different purpose etc.
- Or, you can use individual parts of your pipelines elsewhere – for example in here, I’m using the Source from the previous pipeline in a new one – but it’s the same source Three levels of reusability Process reusability (i.e., set of pipelines configured via external configuration) Pipeline reusability (e.g., sub-process reusability) Functionality reusability (e.g., logger, notifier, transformer, formatter, encryptor,…) Process reusability – framework Set of pipelines configured via external configuration Configuration in DB or ERP, CRM etc. Pipeline reusability Subprocess reusability (e.g. Data staging) Functional reusability Logger, notifier, transformer, formatter, encryptor,… Single unit / function reused in pipelines of different purpose etc.
- What it looks like in a product like CloverDX? In here you can see the same source, called DonationsReader, being used in two different pipelines.
- Prevent issues: in dynamic transformations
- Data quality SILENT error, automatic mapping issue Code review Automated built-in checks etc.
- Data quality SILENT error, automatic mapping issue Code review Automated built-in checks etc.
- Data quality SILENT error, automatic mapping issue Code review Automated built-in checks etc.
- Naming conventions for files, processes, …
- Ask yourself a question what the data means to your business and why you collect them? it is worth checking data quality Poor data quality Inaccurate reporting wrong business decisions RWI: Incomplete records Process fails Missing alternative path? Do you backup source delta records to rebuild history in case of an error?
- Efficient data process * Not spending too much time on something that is not worth it
- Sooner File type check Profile data Are you expecting an XML file? Check it is an XML file at first. Profile data (if necessary) before you start individual record validation. Unnecessary validation Big data profiling may lead to unnecessary read operations (few lines might be enough? Or leave it for later) Create libraries or custom components and reuse them as often as possible Handle exceptions -
- Backup data that you will not be able to retrieve again Especially those that are business critical Typically, this would be data from: Transactional systems Third party systems
- Efficient team work too…
- Document wisely: Notes in a pipeline should only deal with the code in the pipeline
- Document wisely: Notes in a pipeline should only deal with the code in the pipeline