Tom Baeyens’ Post

The Core Value of a Data Engineer: The Cornerstone of a Data-Driven World In today's digital age, data is essential for every business decision, every innovation, and even every transaction. As data engineers, our mission is to build a solid data foundation for organizations, ensuring that data is not only collected but also utilized efficiently and accurately. Many people confuse the roles of data engineers with those of data scientists. In reality, without a stable data pipeline and reliable data governance, data scientists and analysts face a "garbage in, garbage out" dilemma. The value of a data engineer lies precisely in ensuring data quality, integrity, and availability. The most common challenges in my work include: Diverse data sources: Data comes from disparate systems, formats, and platforms, making integration difficult. High performance requirements: Whether it's real-time financial transactions or manufacturing monitoring, data latency directly impacts business operations. Compliance and security: Especially in multinational companies, regulations like GDPR and CCPA impose strict requirements on data processing. Therefore, data engineers are more than just SQL writers; they serve as a bridge between business and technology. We need to communicate business needs with product managers, discuss model requirements with data scientists, and ensure system stability with IT operations. In the future, with the advancement of AI and real-time computing, the role of data engineers will become even more important. We must not only "move data" but also understand how to design intelligent and automated data platforms to provide companies with sustainable competitive advantages. In short, data engineers are the "invisible cornerstone" of the digital economy. If you see a successful data-driven company, remember that there is a strong data engineering team behind it.

🔥 Your pipeline works perfectly... until stakeholders ask THE question: 'Can we trust this data?' Sound familiar? 😅 As data engineers, we're masters at building scalable ETL pipelines, but then comes the dreaded data quality interrogation: ❌ "Why are these numbers different from last month?" ❌ "Can we trace where this data came from?" ❌ "Are we missing records again?" Here's the reality check: Building pipelines is just 50% of the job. The other 50%? Ensuring data quality. 6 Data Quality Dimensions Every DE Should Master: 🎯 Accuracy - One wrong join = chaos downstream 📊 Completeness - Missing data = missing insights 🔍 Uniqueness - Duplicates are analytics nightmares ✅ Validity - Schema violations break everything ⏰ Timeliness - Stale data = wrong decisions 🛡️ Integrity - Data consistency builds stakeholder trust Quick Wins: • Add data validation at ingestion points • Implement anomaly detection alerts • Create data lineage documentation • Set up automated quality checks in your CI/CD Pro tip: Start small. Pick ONE dimension, master it, then expand. Don't try to solve everything at once. George Firican, would like to know your thoughts on data quality and how to deal with it in the rising era of AI. Your stakeholders will thank you (and actually trust your pipelines 🎉) What's your biggest data quality nightmare? Drop it below! 👇 hashtag #Data hashtag #Engineering hashtag #DataQuality

2025-09-05 :: The latest Agile Data Engineering pattern is out, this one is all about Data Matching / Data Diif / Data Reconciliation. #AgileDataEngineering #Patterns https://lnkd.in/gEdebpm6 When Nigel Vining and I used to do data project work as consultants we would always end up building a "Data Match" shortcut. Its one of those things we wouldn't use all the time, but when we needed it to trouble shoot a data problem it saved us a lot of time and effort. So Data Match is the next data engineering pattern we decided to document and share. You probably don't need to make the Data Match pattern as a reusable "product" feature, but you probably want to have the scaffolding code to do something similar in your data engineering toolkit. "you won't need it every night but you will need it"

Data Engineering will look absolutely hard to do and get into, until you get your basics strong. Start with these 30 core Fundamentals here: ⥽ SQL (Advanced) Query optimization, window functions ⥽ Data Modeling Star vs Snowflake schema, normalization/denormalization ⥽ Distributed Data Processing Spark, Flink, Beam ⥽ Data Warehousing Snowflake, BigQuery, Redshift ⥽ Event Streaming Kafka, Kinesis, Pub/Sub ⥽ Workflow Orchestration Airflow, Dagster, Prefect ⥽ Data Formats Parquet, Avro, ORC ⥽ File Systems HDFS, S3, ADLS ⥽ Schema Evolution Versioning, backward/forward compatibility ⥽ Data Contracts Validation, enforcement ⥽ ETL vs ELT Batch vs streaming, transformation patterns ⥽ Data Partitioning Time-based, hash-based ⥽ Indexing Primary, secondary, bitmap indexes ⥽ SCDs & CDC Slowly Changing Dimensions, Change Data Capture ⥽ Data Versioning Audit trails, history tables ⥽ Monitoring & Observability Data quality checks, lineage tracking ⥽ Error Handling & Retries Idempotency, dead-letter queues ⥽ Data Privacy & Security Encryption, masking, access control ⥽ Data Compliance GDPR, CCPA, retention policies ⥽ Cloud Data Platforms AWS, GCP, Azure basics ⥽ Data Lake Architecture Lakehouse, Delta Lake, Iceberg ⥽ Data Governance Cataloging, policies, ownership ⥽ CI/CD for Data Pipelines Testing, deployment, rollback ⥽ Git & Version Control Branching, code reviews ⥽ API Integration REST, GraphQL, Webhooks ⥽ Batch vs Streaming Latency, throughput, windowing ⥽ Data Enrichment Joining external sources, upserts ⥽ Cost Optimization Resource allocation, billing analysis ⥽ Data Documentation Metadata, data dictionaries ⥽ Communication & Collaboration Cross-team workflows, stakeholder alignment

Data Engineering will look absolutely hard to do and get into, until you get your basics strong. Start with these 30 core Fundamentals here: ⥽ SQL (Advanced) Query optimization, window functions ⥽ Data Modeling Star vs Snowflake schema, normalization/denormalization ⥽ Distributed Data Processing Spark, Flink, Beam ⥽ Data Warehousing Snowflake, BigQuery, Redshift ⥽ Event Streaming Kafka, Kinesis, Pub/Sub ⥽ Workflow Orchestration Airflow, Dagster, Prefect ⥽ Data Formats Parquet, Avro, ORC ⥽ File Systems HDFS, S3, ADLS ⥽ Schema Evolution Versioning, backward/forward compatibility ⥽ Data Contracts Validation, enforcement ⥽ ETL vs ELT Batch vs streaming, transformation patterns ⥽ Data Partitioning Time-based, hash-based ⥽ Indexing Primary, secondary, bitmap indexes ⥽ SCDs & CDC Slowly Changing Dimensions, Change Data Capture ⥽ Data Versioning Audit trails, history tables ⥽ Monitoring & Observability Data quality checks, lineage tracking ⥽ Error Handling & Retries Idempotency, dead-letter queues ⥽ Data Privacy & Security Encryption, masking, access control ⥽ Data Compliance GDPR, CCPA, retention policies ⥽ Cloud Data Platforms AWS, GCP, Azure basics ⥽ Data Lake Architecture Lakehouse, Delta Lake, Iceberg ⥽ Data Governance Cataloging, policies, ownership ⥽ CI/CD for Data Pipelines Testing, deployment, rollback ⥽ Git & Version Control Branching, code reviews ⥽ API Integration REST, GraphQL, Webhooks ⥽ Batch vs Streaming Latency, throughput, windowing ⥽ Data Enrichment Joining external sources, upserts ⥽ Cost Optimization Resource allocation, billing analysis ⥽ Data Documentation Metadata, data dictionaries ⥽ Communication & Collaboration Cross-team workflows, stakeholder alignment

Data problems ARE software engineering problems, even if no one thinks about them this way. Over the last 20 years or so, data engineers, data analysts, architects, and data scientists have been grappling with answering some of the fundamental questions of data management: 1. What does this data mean? 2. Why did this data change? 3. Is this data trustworthy? 4. Who owns this data? 5. Where can I find the data I need? However, all these problems have only ever been framed within the context of an analytical database and the associated data warehouse layer using tools that apply to data after it has already been materialized. "Data Catalogs" tried to extract meaning by looking at rows and columns and query history. "Data Lineage" tried to understand how tables and queries fed into each other and who was using them. "Data Observability" looked at data as it landed in the Warehouse or Lake "Data Contracts" focused on expectations of data at the moment it entered your analytical environment. Yet for all these tools, the data management story has always felt incomplete. And that, frankly, is because it IS. Data does NOT start in the Data Warehouse. It begins at a source - either internal or external. The source is not always a database; it can be an API, a file share, or application code where logs or events have been written to produce data. In the source systems, software engineers who manage the technology to ingest and transform data struggle with the SAME questions data engineers do. When an outage occurs, SWEs need to understand how systems are interconnected (via data) and attempt to identify these connections through a triage process and manual effort. When large-scale migrations need to be performed, SWEs must trace how different services communicate (via data) and what each service is doing with that data by examining code. The concept of an API contract even exists in the SWE world, but it is incomplete and fails at getting adoption, because it relies on human knowledge/effort to understand the connective tissue between producers and consumers, and then manually produce the unit tests THEN maintain them forevermore into infinity. The problems that data engineers and software engineers have are not unique. If you want your engineering organization to care more about data, data governance, and data management - stop trying to frame the problem purely as one that only involves analytical databases, and more as one involving the TRANSFER of data across any technological boundary. Good luck!

🔥 Your pipeline works perfectly... until stakeholders ask THE question: 'Can we trust this data?' Sound familiar? 😅 As data engineers, we're masters at building scalable ETL pipelines, but then comes the dreaded data quality interrogation: ❌ "Why are these numbers different from last month?" ❌ "Can we trace where this data came from?" ❌ "Are we missing records again?" Here's the reality check: Building pipelines is just 50% of the job. The other 50%? Ensuring data quality. 6 Data Quality Dimensions Every DE Should Master: 🎯 Accuracy - One wrong join = chaos downstream 📊 Completeness - Missing data = missing insights 🔍 Uniqueness - Duplicates are analytics nightmares ✅ Validity - Schema violations break everything ⏰ Timeliness - Stale data = wrong decisions 🛡️ Integrity - Data consistency builds stakeholder trust Quick Wins: • Add data validation at ingestion points • Implement anomaly detection alerts • Create data lineage documentation • Set up automated quality checks in your CI/CD Pro tip: Start small. Pick ONE dimension, master it, then expand. Don't try to solve everything at once. George Firican, would like to know your thoughts on data quality and how to deal with it in the rising era of AI. Your stakeholders will thank you (and actually trust your pipelines 🎉) What's your biggest data quality nightmare? Drop it below! 👇 #Data #Engineering #DataQuality

Data Lake Architecture – A Data Engineer’s Perspective As data engineers, our job is to move, process, and optimize data pipelines so that raw data can be turned into meaningful insights. A well-designed Data Lake Architecture is at the heart of this. Here’s how we look at it: 🔹 Ingestion Tier – Building connectors and pipelines to handle real-time streams, micro-batches, and large batch jobs. Scalability and fault tolerance here are critical. 🔹 HDFS Storage Layer – The backbone for storing structured, semi-structured, and unstructured data. Think of it as the raw data zone. 🔹 Distillation & Processing Tiers – Where engineers design ETL/ELT workflows. Using in-memory engines, MPP databases, or MapReduce, we refine raw data into curated, queryable formats. 🔹 Insights Tier – The product of good engineering: enabling SQL/NoSQL interfaces so analysts, data scientists, and business teams can run queries at scale. 🔹 Unified Operations Tier – The often underrated piece: system monitoring, policy enforcement, data governance (MDM/RDM), and workflow orchestration ensuring the lake doesn’t turn into a swamp. From a data engineering lens, success means: ✔️ Seamless ingestion pipelines ✔️ Scalable storage & processing ✔️ Strong governance and monitoring ✔️ Enabling real-time, batch, and interactive insights downstream In short, we build the foundations that let organizations unlock the true power of data. As a data engineer: What tools are you using in your data lake pipelines today?

As a data engineer, you have a lot of freedom to specialize. If you've mastered building pipelines then maybe it's time to branch out. I am slightly biased but I believe that data engineers are a hub role. We sit in the middle of software engineering and data analytics but also rub shoulders with data architecture and ML/AI. I'm a strong advocate for folks having their primary role (data engineer) and a secondary, complimentary role. This can make you invaluable. Here's what I would do to add another specialism to my toolkit: → 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Pipeline monitoring is a great foundation for MLOps. You need to learn a new suite of algorithms and data types. • Focus on vector databases and RAG applications • Dive deep into statistical modeling and ML algorithms • Build projects on computer vision OR natural language processing → 𝗗𝗮𝘁𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Data warehousing is the foundation for data lakes and lakehouses. You need to develop strong communication skills and strategic thinking. • Focus on data governance and management • Dive deep into cloud infrastructure patterns and SDKs • Build projects that deploy and consume real cloud resources → 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Data modeling is the foundation of having analysis ready data. You need to develop strong business intuition and stakeholder management. • Focus on data visualization and a BI tool • Dive deep into analytics patterns and statistical analysis • Build projects that solve a real business problem in your target domain → 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 The software development lifecycle feels familiar to the data lifecycle. You need to learn how different services and applications are built. • Focus on version control and code testing • Dive deep into the full software development lifecycle • Build projects like robust APIs with logging and authentication I've gone the data architect route. Which is your preferred path?

Some final notes from my exploration of Gemini to vibe code some simple data engineering file ingestions: - Breaking down tasks into smaller pieces seems to make Gemini more effective. In this case creating functions to handle focused transformations seems to work best, and calling the resulting functions serially is much better than one big chunk of code. - It did a good job of handling basic exceptions and logging them to an output file for later follow up - It added some counters to track what was being processed, successfully transformed, and the number of exceptions without being asked.  This was a nice touch but something I should have asked it to do in the first place. - The approach it used was OK, but did create too many false positive exceptions. Instead of trying to clean up any data inconsistencies as it was asked for, it instead used a regex to match a simple separator pattern.  It turns out that this works well for 99% of the sample data in my files.  However, of the data marked as having an exception, about 60% of that data was the result of the regex not accepting special characters (mostly emojis).  While this is a simple fix it was unexpected. Overall, Gemini did better than I expected.  However, this did require quite a bit of adjusting the prompts until it worked as well as my manually created code.  It also took longer, but added some nice touches like the counters and wrote a neater looking set of exception handling. I became faster with tuning the prompts over time, and having a set of code to compare against my own was useful. I wouldn't call the Gemini coding experience time saving let alone transformative, but it was helpful overall. I may try this on other platforms over time to see if they can do any better.