Lucas Morse’s Post

Handling Data Inconsistencies During Changes ⚠️ Handling Data Inconsistencies During Changes Data inconsistencies can arise during changes in data engineering processes, potentially compromising data quality. Strategies to handle inconsistencies include: Data Validation Rules: Implement strict validation rules to ensure data integrity during transformations and migrations. Error Handling Mechanisms: Design robust error handling to manage and rectify inconsistencies as they occur. Automated Data Cleansing: Use automated tools to identify and correct data anomalies during ETL processes. Audit Trails: Maintain detailed audit logs to trace and address inconsistencies effectively. Regular Data Audits: Conduct periodic data audits to identify and resolve inconsistencies proactively. Ensuring data consistency is critical for reliable analytics and informed decision-making. Implement these strategies to maintain high data quality during engineering changes.

Data integration is crucial for seamless business operations, but if not done correctly, it can lead to costly errors. ETL (Extract, Transform, Load) testing ensures your data is accurate, consistent, and reliable throughout the integration process. Here’s a checklist to help you avoid data integration disasters: ✔️ Validate Data Sources: Ensure the data extracted from multiple sources is correct and complete. Missing or incorrect data can disrupt the entire process. ✔️ Data Transformation Accuracy: Verify that business rules are applied correctly during the transformation process. Check that data formatting, filtering, and calculations are accurate. ✔️ Verify Data Mapping: Ensure proper mapping between source and target systems. Mismapped fields or tables can lead to serious errors in the final data output. ✔️ Data Quality Checks: Implement data quality validation to catch duplicates, inaccuracies, or missing data. This helps maintain the integrity of your data after loading. ✔️ Performance Testing: Test how well your ETL process handles large datasets. A slow or inefficient process can impact performance and lead to delays. A solid ETL testing strategy is key to avoiding data integration disasters. By following this checklist, businesses can ensure smooth, accurate, and efficient data integration processes. #skalable #ETLTesting #DataIntegration #DataQuality #BusinessIntelligence #ETL

🚀 To understand the difference between a Data Lake and a Data Warehouse, mastering ACID properties is extremely important. ⏩️ Data Lake + ACID guarantees + Schema management + Query Optimisation tools + Structured Data Handling => Data Warehouse ⏪️ 🚀 Let's break down ACID properties using a simple money transfer example: When you transfer money to a friend, the transaction involves two key steps: 1. Deducting the amount from your account. 2. Adding the amount to your friend's account. ⭐ Atomicity: Both steps must occur together. If one step fails, the other doesn’t proceed. This all-or-nothing approach prevents errors like money disappearing during the process. ⭐ Consistency: The system must follow all rules. Inconsistencies arise if only one step completes or if the transaction ignores rules like minimum balance requirements. ⭐ Isolation: If one step occurs at 10:00 AM and the other at 10:01 AM, someone checking between these times might see incorrect data. Isolation ensures changes aren’t visible until both steps are complete, maintaining data integrity. ⭐ Durability: Once the transaction is complete, the changes should be permanent. This ensures that completed transactions aren’t lost, even in the event of a system failure. ⭐ Atomicity vs Isolation: -> Atomicity ensures both steps occur together or not at all. -> Isolation ensures partial transactions aren’t visible, even if there’s a delay between steps. Sumit Mittal

Why did the data catalog breakup with it's partner? because it couldn't handle so many relationships... on a serious note though, how many entities can your catalog responsively support? how many do you need to support, how does your catalog scale? Alex Solutions supports tens of millions, quite frankly, if the infrastructure is big enough to support the knowledge graph in all its glory, the limits are probably relatively boundless. but it remains an important question. Consider that a table is just one entity, if it has ten attributes it just jumped to 11 entities and those 10 attributes all have a relationship with the table. Your database now contains at least 21 records. Start adding views, stored procedures, ETL and reporting applications, it very quickly exploded to hundreds and then thousands and then millions of entries. Start adding data people, controls, technology describers and business processes, quality measures, KPIs and metrics and it extends further. Can your catalog adequately serve up the answers you need in this context?

One of the biggest concerns in core system migration is crystal clear: “Will all our data be accurate in the new system?” Our client had every reason to worry about data quality: 1. New Processes: They were built directly on the contemporary application. 2. Complex Queries: With a vast volume of data points, they needed accurate and consistently structured queries. 3. Data Transformation: Many data points in the new database came from combining and transforming multiple sources. But here’s the good news! 💪 With a relentless focus on data quality and a commitment to continuous improvement, we successfully tackled these challenges head-on. Curious about how we did it? Check out the use case for insights and strategies that can help you in your own data migration journey! 👉 Let’s connect if you have questions! #DataMigration #SystemMigration #DataQuality

Data migration might seem daunting. But it should never be a nightmare. It can be a smooth process with the right approach. 7 signals you're handling data migration like a pro: 1. Thorough Planning ↳ You've mapped out every step of the process. ↳ You've identified potential risks and solutions. 2. Clear Communication ↳ Everyone involved knows their role. ↳ Regular updates keep stakeholders informed. 3. Data Cleansing ↳ You've cleaned and validated data before migration. ↳ Duplicate and obsolete data have been removed. 4. Robust Testing ↳ You've conducted thorough pre-migration tests. ↳ Post-migration validation is part of your plan. 5. Backup Strategy ↳ You've created comprehensive backups. ↳ A rollback plan is in place, just in case. 6. Phased Approach ↳ You're migrating data in manageable chunks. ↳ Each phase is reviewed before moving to the next. 7. Post-Migration Support ↳ You've planned for immediate post-migration issues. ↳ A long-term support strategy should be in place. P.S. Are you following these data migration best practices?

How to Build a Data Domain? Building a data domain involves defining and organizing your data assets around a specific subject area. The exact steps might differ depending on the tools you're using, but here's a general guideline: 1.           Define the Scope: o   Identify the business area the data domain will support. o   What kind of data will it encompass (e.g., customer data, product data, financial data)? o   Determine the level of granularity - will it be a broad domain or more specific 2.           Data Inventory: o   Catalog all existing data sources relevant to the domain. o   This includes internal databases, external feeds, and any other data repositories. 3.           Data Model: o   Design a data model that defines how the data elements will be structured and relate to each other. o   Ensure consistency in data formats, units, and naming conventions across all sources within the domain. 4.           Data Governance: o   Establish policies and procedures for managing the data domain. o   This includes data ownership, access control, quality checks, and security measures. 5.           Data Storage and Access: o   Choose appropriate storage solutions based on data volume, access needs, and security requirements. o   Define methods for users to access and utilize the data within the domain.

The term Snapshot keeps on coming in databases lot many times,Snapshot basically refers to state of database at any point of time, capturing the data and metadata as they existed at that instant. Couple of usecase of Snapshot are listed below:- 1. Consistent Reads(reading across all devices should be same): Snapshots ensure that a transaction or query sees a consistent view of the data, even if changes are being made concurrently. This is crucial for ensuring data integrity and consistency in multi-user or distributed systems. 2. Backup and Recovery: Snapshots are commonly used for backup and recovery purposes. By creating regular snapshots of a database or system, restore data to a previous state in case of data loss, corruption. 3 .Analytics and Reporting: Snapshots provide a stable and consistent dataset for analytics, reporting, and data analysis purposes. Analysts can query and analyze the data without worrying about changes occurring to the underlying dataset during the analysis process. 4. Auditing and Compliance: Snapshots can be used for auditing and compliance purposes, providing a historical record of data changes and system states. Organizations can use snapshots to track changes, monitor access, and ensure compliance with regulatory requirements. 5. Testing and Development: Snapshots are valuable for testing and development environments, allowing developers and testers to work with realistic data sets without affecting the production environment. Developers can create snapshots of production data for testing new features, debugging, and performance tuning. #backendEngineering #systemDesign #distributedSystems

If you want to make solid decisions efficiently with your complex real-world data, a data exchange solution can give your company the functionality and reliability it needs. 📈 But how do you choose the ideal data file exchange solution for your use case and needs? Here are the key factors you should keep in mind: https://buff.ly/3TKJ3fd #datamanagement #dataimport #dev #developers #developertools

Data analysts employ two data integration approaches to convey data. The Extract, Transform, and Load (ETL) process collects and processes data from a variety of sources before loading it into a database. The Extract, Load, Transform (ELT) method is similar, but it is generally used for huge amounts of unstructured data, allowing raw data to skip the transformation step and move directly to storage in an unstructured format.