Proactive Approaches to Data Challenges

The only way to prevent data quality issues is by helping data consumers and producers communicate effectively BEFORE breaking changes are deployed. To do that, we must first acknowledge the reality of modern software engineering: 1. Data producers don’t know who is using their data and for what 2. Data producers don’t want to cause damage to others through their changes 3. Data producers do not want to be slowed down unnecessarily Next, we must acknowledge the reality of modern data engineering: 1. Data engineers can’t be a part of every conversation for every feature (there are too many) 2. Not every change is a breaking change 3. A significant number of data quality issues CAN be prevented if data engineers are involved in the conversation What these six points imply is the following: If data producers, data consumers, and data engineers are all made aware that something will break before a change has deployed, it can resolve data quality through better communication without slowing anyone down while also building more awareness across the engineering organization. We are not talking about more meaningless alerts. The most essential piece of this puzzle is CONTEXT, communicated at the right time and place. Data producers: Should understand when they are making a breaking change, who they are impacting, and the cost to the business Data engineers: Should understand when a contract is about to be violated, the offending pull request, and the data producer making the change Data consumers: Should understand that their asset is about to be broken, how to plan for the change, or escalate if necessary The data contract is the technical mechanism to provide this context to each stakeholder in the data supply chain, facilitated through checks in the CI/CD workflow of source systems. These checks can be created by data engineers and data platform teams, just as security teams create similar checks to ensure Eng teams follow best practices! Data consumers can subscribe to contracts, just as software engineers can subscribe to GitHub repositories in order to be informed if something changes. But instead of being alerted on an arbitrary code change in a language they don’t know, they are alerted on breaking changes to the metadata which can be easily understood by all data practitioners. Data quality CAN be solved, but it won’t happen through better data pipelines or computationally efficient storage. It will happen by aligning the incentives of data producers and consumers through more effective communication. Good luck! #dataengineering

This visual captures how a 𝗠𝗼𝗱𝗲𝗹-𝗙𝗶𝗿𝘀𝘁, 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝘆𝗰𝗹𝗲 breaks the limitations of reactive data quality maintenance and overheads. 📌 Let's break it down: 𝗧𝗵𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝘀𝗽𝗼𝘁𝘀 𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀𝘀𝘂𝗲 But instead of digging through pipelines or guessing upstream sources, they immediately access metadata-rich diagnostics. Think data contracts, semantic lineage, validation history. 𝗧𝗵𝗲 𝗶𝘀𝘀𝘂𝗲 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗳𝗹𝗮𝗴𝗴𝗲𝗱 Caught at the ingestion or transformation layer by embedded validations. 𝗔𝗹𝗲𝗿𝘁𝘀 𝗮𝗿𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝗿𝗶𝗰𝗵 No generic failure messages. Engineers see exactly what broke, whether it was an invalid assumption, a schema change, or a failed test. 𝗙𝗶𝘅𝗲𝘀 𝗵𝗮𝗽𝗽𝗲𝗻 𝗶𝗻 𝗶𝘀𝗼𝗹𝗮𝘁𝗲𝗱 𝗯𝗿𝗮𝗻𝗰𝗵𝗲𝘀 𝘄𝗶𝘁𝗵 𝗺𝗼𝗰𝗸𝘀 𝗮𝗻𝗱 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻𝘀 Just like modern application development. Then they’re redeployed via CI/CD. This is non-disruptive to existing workflows. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀 𝗸𝗶𝗰𝗸 𝗶𝗻 Metadata patterns improve future anomaly detection. The system evolves. 𝗨𝗽𝘀𝘁𝗿𝗲𝗮𝗺 𝘀𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁𝗶𝗳𝗶𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 In most cases, they’re already resolving the root issue through the data product platform. --- This is what happens when data quality is owned at the model layer, not bolted on with monitoring scripts. ✔️ Root cause in minutes, not days ✔️ Failures are caught before downstream users are affected ✔️ Engineers and analysts work with confidence and context ✔️ If deployed, AI Agents work without hallucination and context ✔️ Data products become resilient by design This is the operational standard we’re moving toward: 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲, 𝗺𝗼𝗱𝗲𝗹-𝗱𝗿𝗶𝘃𝗲𝗻, 𝗰𝗼𝗻𝘁𝗿𝗮𝗰𝘁-𝗮𝘄𝗮𝗿𝗲 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆. Reactive systems can’t support strategic decisions. 🔖 If you're curious about the essence of "model-first", here's something for a deeper dive: https://lnkd.in/dWVzv3EJ #DataQuality #DataManagement #DataStrategy

I’ve lost count of projects that shipped gorgeous features but relied on messy data assets. The cost always surfaces later when inevitable firefights, expensive backfills, and credibility hits to the data team occur. This is a major reason why I argue we need to incentivize SWEs to treat data as a first-class citizen before they merge code. Here are five ways you can help SWEs make this happen: 1. Treat data as code, not exhaust Data is produced by code (regardless of whether you are the 1st party producer or ingesting from a 3rd party). Many software engineers have minimal visibility into how their logs are used (even the business-critical ones), so you need to make it easy for them to understand their impact. 2. Automate validation at commit time Data contracts enable checks during the CI/CD process when a data asset changes. A failing test should block the merge just like any unit test. Developers receive instant feedback instead of hearing their data team complain about the hundredth data issue with minimal context. 3. Challenge the "move fast and break things" mantra Traditional approaches often postpone quality and governance until after deployment, as shipping fast feels safer than debating data schemas at the outset. Instead, early negotiation shrinks rework, speeds onboarding, and keeps your pipeline clean when the feature's scope changes six months in. Having a data perspective when creating product requirement documents can be a huge unlock! 4. Embed quality checks into your pipeline Track DQ metrics such as null ratios, referential breaks, and out-of-range values on trend dashboards. Observability tools are great for this, but even a set of SQL queries that are triggered can provide value. 5. Don't boil the ocean; Focus on protecting tier 1 data assets first Your most critical but volatile data asset is your top candidate to try these approaches. Ideally, there should be meaningful change as your product or service evolves, but that change can lead to chaos. Making a case for mitigating risk for critical components is an effective way to make SWEs want to pay attention. If you want to fix a broken system, you start at the source of the problem and work your way forward. Not doing this is why so many data teams I talk to feel stuck. What’s one step your team can take to move data quality closer to SWEs? #data #swe #ai

🚨 Imagine this scenario: your long-running data pipeline suddenly breaks due to a data quality (DQ) check failure. Debugging becomes a nightmare. Recreating the failed dataset is incredibly difficult, and the complexity of the pipeline makes pinpointing the issue almost impossible. Valuable time is wasted, and frustrations run high. 🔍 Wouldn't it be great if you could investigate why the failure occurred and quickly determine the root cause? Having immediate access to the exact dataset that caused the failure would make debugging so much more efficient. You could resolve issues faster and get your pipeline back up and running without significant delays. 💡 Here's how you can achieve this: 1. Persist Datasets Per Pipeline Run: Save a version of your dataset at each pipeline run. This way, if a failure occurs, you have the exact state of the data that led to the issue. 2. Clean Only After DQ Checks Pass: Retain these datasets until after the data quality checks have passed. This ensures that you don't lose the data needed for debugging if something goes wrong. 3. Implement Pre-Validation Dataset Versions: Before running DQ checks, create a version of your dataset named something like `dataset_name_pre_validation`. This dataset captures the state of your data right before validation, making it easier to investigate any failures. By persisting datasets and strategically managing them around your DQ checks, you can significantly simplify the debugging process. This approach not only saves time but also enhances the reliability and maintainability of your data pipelines. --- Transform your data pipeline management by making debugging efficient and stress-free. Implementing these steps will help you quickly identify root causes and keep your data workflows running smoothly. #dataengineering #dataquality #debugging #datapipelines #bestpractices

To prevent data initiatives from failing, it’s crucial to manage data comprehensively, from start to finish. Starting out with tools that read the existing state, like data observability tools and monitoring systems is great, but it makes us reactive - we’d catch problems only after they've occurred. These tools can spot issues such as schema changes or quality lapses, but pinpointing the cause or preventing issues outright is often a challenge. This happens because such tools monitor changes primarily in data warehouses, making it hard to identify where exactly problems begin, if it’s further upstream. As a result, engineers might find themselves constantly fixing problems rather than preventing them. For #datamanagement strategy, we must look beyond downstream solutions and integrate proactive measures right from the data's origin. While understanding issues through downstream tools is valuable, true problem-solving requires a holistic approach that covers every step of the data’s path. Foundational simplifies #codereview and automates validation for data developers by detecting and resolving issues pre-deployment. It scans repositories in the data stack, seamlessly integrating with teams' workflows to enhance efficiency and #dataquality.