Piotr Czarnas’ Post

A staging data forwarding system works by first Extracting data from various source systems into a temporary staging area. In this intermediate zone, data is cleaned, standardized, and transformed to match the target data model. Finally, this processed data is Loaded from the staging area and forwarded to the final destination, such as a data warehouse, making it a core part of the Extract, Transform, Load (ETL) process. How the Process Works 1. Extract: Data is pulled from one or more source systems (like databases, applications, or files).  2. Stage: The extracted data is temporarily stored in a dedicated staging area, which is a separate storage space designed to hold raw data.  3. Transform: In the staging area, the data undergoes various transformations: Cleansing: Errors, duplicates, and inconsistencies are identified and resolved.  Standardization: Data is converted into consistent formats and structures.  Integration: Data from different sources is integrated and aligned with the target data model.  4. Load (Forward): Once the data is clean and transformed, it is then loaded from the staging area into its final destination, such as a data warehouse, data lake, or analytical repository.  Why Use a Staging Data Forwarding System? Quality Assurance: It provides a controlled environment to clean and validate data, ensuring only high-quality data moves to the destination.  Flexibility: It allows for complex transformations without impacting the performance of the source systems.  Efficiency: By processing data in a separate staging environment, the performance of the production systems is not affected.  Data Consistency: It helps to create a consistent, unified view of data by standardizing it from various sources.  Scalability: The staging area is built to handle large volumes of data and can be scaled as data grows. ###If you like this post you can drop your comments###

✅ Make your data integration seamless. Data integration relies heavily on IT departments' technical expertise to connect and manage diverse data sources for organizational use. It is a very slow and complicated process that requires many steps. Accessing, prepare and process #data to make quick and effective #businessdecisions is always a barrier to operations and SME teams, as it requires skills of data expertise. What if that was not the case so that SME and operations could work with data without relying on IT and the data team? Read more to learn how to make it easier for non-technical users to acquire and use data without relying on IT and data teams for #decisionmaking scenarios. ➡️ Read More: https://bit.ly/3w9OQxD

✅ Make your data integration seamless. Data integration relies heavily on IT departments' technical expertise to connect and manage diverse data sources for organizational use. It is a very slow and complicated process that requires many steps. Accessing, prepare and process #data to make quick and effective #businessdecisions is always a barrier to operations and SME teams, as it requires skills of data expertise. What if that was not the case so that SME and operations could work with data without relying on IT and the data team? Read more to learn how to make it easier for non-technical users to acquire and use data without relying on IT and data teams for #decisionmaking scenarios. ➡️ Read More: https://bit.ly/3w9OQxD

Data Observability What is Data Observability? Data observability is the discipline of continuously monitoring and understanding the health of data pipelines and assets. It goes beyond simple data quality checks by providing complete visibility into the entire data landscape. Observability includes tracking key data health metrics like: Data freshness — ensuring data is up to date. Data accuracy — identifying errors and anomalies. Data completeness — verifying no important data is missing. Data lineage — tracing how data flows across systems. Data consistency — confirming alignment across multiple sources. When data issues arise, observability tools highlight: What is broken. Where the problem occurred. Why it happened. This enables rapid root cause analysis and timely resolution of data problems. Why Organizations Rely on Data Observability ? Even in well-controlled environments, data errors can slip through the cracks. Broken integrations, unsynchronized data sources, evolving schemas, or manual input mistakes can threaten reporting accuracy, regulatory compliance, and business trust. Data observability changes the approach from reactive problem fixing to proactive risk management by offering early warning signals when issues arise and providing tools to trace problems back to their root causes before downstream processes are impacted. Modern Use Case: Real-Time E-commerce Order Monitoring Consider a global e-commerce platform processing millions of orders every day. Recently, data observability tools detected an unusual drop in orders being recorded from a newly launched sales channel. Real-time anomaly alerts allowed the data engineering team to quickly identify a failed batch ingestion caused by a timezone mismatch. With lineage tracing pinpointing the root cause in minutes, the team rapidly fixed the issue—restoring order visibility, preventing shipment delays, and maintaining customer satisfaction. How to Embed Observability in Your Data Operations ? Map and document data pipelines across systems and integrations. Identify critical data elements essential for reporting, compliance, and decision-making. Implement automated monitoring and alerting solutions for key datasets. Establish clear data ownership and accountability. Integrate data observability practices into core workflows such as reporting, forecasting, and audits. #DataObservability #DataOps #DataQuality #DataEngineering #DataManagement #DataGovernance #DataAnalytics #BigData #DataMonitoring #Observability #DataDriven #DataPipeline #AIforData #DataIntegrity #CloudData

Monday Musings: From Project to Practice with Data Stewards In the last two Monday Musings, I covered starting a data governance program with a tactical data quality analysis and then mapping data lineage to understand where issues come from and how they move through your systems. These steps create a clear picture of your data’s condition and its journey. The next challenge is keeping that picture accurate over time. That is where data stewards come in. Data stewards are the people who take ownership of a data domain and ensure it stays clean, accurate, and fit for purpose. They bridge the gap between business and technical teams by understanding both the meaning of the data and how it is used. Without stewards, governance often fades once the initial project ends, and old problems start to creep back in. Step 1: Nominate stewards early Do not wait until the end of your governance initiative to assign stewardship. Identify the right people while you are still profiling data and mapping lineage. These are often subject matter experts who already have informal responsibility for the data but need the formal role and support to manage it effectively. Step 2: Provide clear responsibilities Stewards need to know exactly what they are responsible for. This might include approving changes to key data elements, monitoring data quality dashboards, investigating issues, and coordinating with technical teams to resolve them. Avoid vague job descriptions. Clarity makes accountability possible. Step 3: Build repeatable checks Turn your initial profiling and lineage maps into ongoing monitoring. Schedule regular quality checks and set thresholds that trigger action when data falls below agreed standards. Give stewards the tools to perform this task themselves, rather than relying on IT for every analysis. Step 4: Equip with tools, not just policies Policies matter, but they are not enough. Stewards need accessible reports, dashboards, and workflows that make it easy to spot and fix issues. Good tools help them work efficiently and make governance feel like part of daily operations rather than an extra task. When you empower data stewards, governance stops being a one-time cleanup and becomes a living, self-sustaining discipline. It shifts from reactive problem-solving to proactive data care. This is where the real return on your governance investment comes from. Over the last three weeks, we have gone from identifying problems with data quality to tracing their origins through lineage mapping to embedding governance into daily operations with stewardship. If you take these steps in sequence, you create a program that delivers immediate value and remains effective long after the initial project ends.

1. Define the term 'Data Wrangling. Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format. 2. What are the best methods for data cleaning? Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry. 3. Explain 4 steps to use CTE in sql. All CTE starts with "with" clause. After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp". Once you have defined CTE we need to specify the SQL which will give the result for the CTE. Finally you can use the CTE in your SQL query.

How can data "tell you" about the change that is happening in it? Change Data Capture (CDC) In the world of data, change happens every moment. And it is smart that we wait a long time to collect the complete information from the data source So that we can analyze quickly and make a decision at the right time! ⚡ From reality: I have sales data that is collected every day at 6 a.m., and I work on it all day. But today, for example, I received a batch with 100 orders, and two hours later there was a change in 10 orders (for example, the customer returned them). What if I have no way to detect these changes on the same day? My analysis will be wrong, the decision may be inaccurate, and this mistake is repeated every day. This explains why Change Data Capture (CDC) is so important. ✅ What does the CDC mean? It is a method that allows you to track any modification (Insert – Update – Delete) that gets your data without having to do a full refresh or load all the data again. 🧠 Why do we use CDC? • We work on "real-time" data • We maintain the performance of ETL and analytical systems • Save time and cost • We connect systems without duplication or contradiction 🧩 CDC Types ✅ 1. Log-based CDC 🔹 Reads changes from transaction log files 🔹 Best for efficiency, speed, and reliability ✅ 2. Trigger-based CDC 🔹 It relies on triggers within tables to record adjustments 🔹 Suitable for systems that don't support Log Access 🔹 The system can slow down a bit – but it's effective in small and medium-sized environments ⚠️ 3. Timestamp-based CDC (using Audit Columns) 🔹 Depends on the LastModifiedDate or UpdatedAt column 🔹 Simple and easy to implement 🔹 Changes may be missed if they happen quickly or without updating the date 🔹 Suitable for Scheduled Loads 🚨 4. Snapshot-based CDC 🔹 He takes a complete copy of the table and compares it to an old copy 🔹 Very slow and expensive 🔹 Used as a last resort in the absence of other solutions 🔐 Important Notes: Log-based CDC is the most professional but requires: • Robust infrastructure • Intelligent pipeline design • High security of logs and protection of modifications 💬 If you work in the field of data engineering or analytics, The CDC should be an essential tool in your toolbox... Because it's the key to: Instant Analysis Streaming System Integration 🧰 Tools and technologies that support CDC: • SQL Server CDC • Oracle GoldenGate • AWS DMS • Azure Data Factory • Microsoft Fabric • Delta Lake / Apache Hudi / Apache Iceberg hashtag #ChangeDataCapture hashtag #CDC hashtag #DataEngineering hashtag #StreamingData hashtag #ETL hashtag #ELT hashtag #DataPipeline hashtag #RealTimeAnalytics hashtag #Debezium hashtag #ApacheKafka hashtag #SQLServer hashtag #Oracle hashtag #MicrosoftFabric hashtag #DeltaLake hashtag #BigData hashtag #ModernDataStack hashtag #DataGovernance

Data governance = deciding how decisions are made about data and data related things. So in some respects data governance is the ultimate enabler, because if you can’t make decisions, you can’t do data things. And if you can’t do things you can’t deliver value. You get stuck in discussion mode or ‘PoC’ mode. But don’t confuse the need for ‘data governance’ am- a clear framework for making decisions about data, for ‘Data Governance’ - some central, enterprisewide decision making allocation exercise. Data Governance sometimes is needed. But often ‘Data Governance’ comes near the end of an organisation’s data journey, not at the start. The benefits of having an enterprise wide framework for making decisions about data are often secondary to having any decision making framework at all. And if you want to get ‘enterprise’ buy in to something as scary as an enterprise Data Governance framework, Best to do once you have proven a lot of value first!

TRUST 🤝💙🫂 It is such a simple yet powerful word. It takes time & effort to build and easily broken. Once broken, game over. It cannot be repaired to the original state. This is the same for DATA. Once you break the trust of your customers, your consumers, your team- very hard to recover from it. Data Trust is 🟩 IT IS to know "your data", "the users", and "the use cases". 🟥 IT IS NOT a one time activity. It is continuous, automated, and repeatable. ✅ REQUIRED ❥ Data Discoverability Knowing your data, organizing it, and enriching your data is all part of data discoverability. This mostly comes into play at the level of data sources, data models, metadata, data definitions (glossary), data interconnects and linkages. ❥ Data Control Knowing your users is an important part of building trust in data. If you don't know who the users are, and therefore what their requirements are, how do you ensure that the data they use is trustworthy. This brings in aspects of users, access control, data journey (lineage), tagging, and segmentation of data. ❥ Data Quality/Observability It is a given that if you're going to rely on data to make critical decisions, it must be of highest quality possible. Data, by nature, is always moving, changing. So, strong checks-n-balances are needed to ensure it remains of high quality and confidence until the point of consumption. ❥ Data Use Cases Data forms the foundation of a modern enterprise. So, without knowing how its collected, its journey, and usage, it is impossible to ensure all collected data (and we collect a lot!) is of hgh confidence and quality. By understanding the use cases, we can apply business smarts & prioritize which data is to be fresh and accurate, which use cases have better tolerances for deviation in data, etc. ☑️ USEFUL ⬢ Data Products It is the mechanism by which right data sets, rules, policies, and usage can be packaged together as a product. AirBnB is a data product company. Uber is a data product company. When they want to get into new businesses, they simply create a new data product to offer. ▶ Data Contract A construct by which data products are continuously tested & validated for high quality & confidence. As we have learned over a few decades, putting together a silo'd data ecosystem of singular capabilities is not the most optimum. It takes up a lot of scarce resources (people & money), delays outcomes, requires constant maintenance and training, and still misses the mark on rapid innovation. The solution is to unify the required functionality into a single scaleable platform. Not the monolithic apps of the past, this must be a modern, cloud-native, SaaS solution w AI-augmented smarts & automation. This solution should be cost-efficient and not require an army of people. At Decube, we're building the first unified data trust platform that combines required & useful capabilities for customers to start getting rapid value (high ROI) with low TCO.

Everybody wants clean data, but it requires a little effort to get it. We have two options: accept only clean data or clean (enrich) it. The first option is simple. It is called a Data Contract. We define the data quality rules that the data producer or the data source owner must ensure. However, when the data source owner cannot fix the data, we have to clean it before providing clean datasets to our data consumers. We have two options: 👉 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗱𝗮𝘁𝗮 𝗰𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Define automated rules within the data pipeline to enrich data. Apply data conversions, transformations, and lookups to fix invalid records. All other records should be put aside and returned to the data producer. 👉 𝗠𝗮𝗻𝘂𝗮𝗹 𝗱𝗮𝘁𝗮 𝗰𝗹𝗲𝗮𝗻𝘀𝗶𝗻𝗴: Run a set of data quality rules defined by data stewards. If any rule fails, a data steward must review the incorrect records manually. It is a costly process, but inevitable in regulated industries or for high-value datasets. How often do you have anybody reviewing data quality issues in your organization? #dataquality #datagovernance #dataengineering