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🌟 Join Our Free Webinar: How to Setup and Manage Tair🌟 📅 Date & Time: January 24, 2025, 5:00 PM - 6:00 PM (UTC+8) --- 📍Are you ready to dive into the world of efficient data management? Join us for an exclusive webinar where we will guide you through setting up and managing Tair, a powerful tool for data processing and analysis. 📌What You'll Learn: 1. Introduction to DataWorks - Get an overview of DataWorks and its key features. - Understand how DataWorks can streamline your data management processes. 2. Migration of Data Objects in DataWorks - Discover the importance of data migration in data management. - Explore common scenarios for data migration. - Follow a step-by-step guide to migrating data objects seamlessly. 3. Data Upload and Download - Learn about the significance of data upload and download in data processing. - Explore different methods for uploading and downloading data. - Follow a step-by-step guide to efficiently manage data uploads and downloads. 4. Scheduling Data Processing Tasks - Learn how to use the scheduling function in DataWorks to automate data processing tasks. - Examples include calculating daily sales, analyzing user purchasing preferences, and more. - See how these automated tasks can help merchants make more accurate and informed decisions. --- 📣 Register Now! https://lnkd.in/gb74BGkZ We look forward to seeing you there!

Hey Data Wizards! 🌟 Let me share an interesting story today😊! Alice, a fellow tech enthusiast, designed a database for an inventory management system. Everything was going smoothly—new products were added daily, prices updated frequently. However, the system just overwrote old data with the new values for every update request. One day, a client reported that a batch of products had been inserted with the wrong prices, which were later updated. The client now wanted to revert to the previous prices, realizing the mistake was with a different batch. 🛒💸 Alice faced a big problem: the system didn't keep track of the old prices. Panicking, she reached out to Bob, her Data Enthusiast friend 🤠. Bob explained that the system needed a different approach—an SCD Type-2, instead of his SCD Type-1, which simply overwrites old data. 💡 So, what are these SCD Type-1, SCD Type-2 actually ??! The term SCD (Slowly Changing Dimension) which refers to how your database's tables are impacted as values change. In data management and data warehousing, to handle these scenarios, there are some approaches: 👉 SCD Type 1: If the value of an attribute changes, without keeping any record, it directly overwrites the value of that attribute. 👉 SCD Type 2: Store every single change with a timestamp! Perfect for those times you need to know the price of a product at any specific time-range. But the concerning point is, your database might turn into a historical novel!. 👉 SCD Type 3: Tracks the previous value and current version of the attribute (for the initial record, the previous value is set to NULL). These different types of SCDs offer various ways to manage your data effectively along with other SCD types. What are your favorite ways to handle changing data? Share your thoughts in the comments! 🚀 #DataEngineering #TechJourney #LearningEveryday #SCD #DatabaseManagement #DataWarehousing

In Data Vault, Pit Stops are temporary storage areas that hold data temporarily while it is being processed, transformed, or loaded into the Data Vault. Here's a detailed overview: Purpose of Pit Stops 1. Temporary Storage: Pit Stops provide temporary storage for data that needs to be processed or transformed before loading into the Data Vault. 2. Data Transformation: Pit Stops can be used to transform data into a format suitable for loading into the Data Vault. 3. Data Quality Checks: Pit Stops can be used to perform data quality checks and data cleansing before loading data into the Data Vault. Characteristics of Pit Stops 1. Temporary: Pit Stops are temporary storage areas, and data should not be stored here permanently. 2. Raw Data: Pit Stops typically store raw, unprocessed data. 3. No Business Logic: Pit Stops do not contain business logic or rules. 4. No Data Relationships: Pit Stops do not establish relationships between data entities. Types of Pit Stops 1. Raw Data Pit Stop: Stores raw, unprocessed data from source systems. 2. Transformed Data Pit Stop: Stores transformed data that has been processed and formatted for loading into the Data Vault. 3. Error Handling Pit Stop: Stores data that has failed data quality checks or has errors

👉🏽 Unexpected changes cause so much pain in data, yet if the business isn't changing, it's decaying. 🔄 One way to make sense of these changes is what I describe as the "Business Logic Lifecycle" with the following steps: 📌 1. Domain experts within the business define context-specific business logic that reflects the actions taken for their work. 📌 2. This business logic is translated into technical requirements and represented as code to capture the business's actions within a transactional database. (note: this data is structured for quick read/write access) 📌 3. The transactional database is replicated into an analytical database, and then the data is transformed into a structure that allows large scans and identifying relationships. ‼️ This is important! At this stage, the data is now two degrees removed from the reality being captured by the data. Numerous assumptions must be made about the business logic while accounting for the assumptions made in the transactional database. 📌 4. Insights are extracted from the analytical database to inform the business about how the current business logic can be optimized and/or changed to account for new market information. 🔄 Repeat! 👀 With this in mind, another way to view poor data quality is the deviation of these steps from the ground truth the data is supposed to represent. Knowing where you sit within this lifecycle can give you clues on how to approach solving your data quality problems. 🫡 I hope this helps! ----- 💬 This slide is from my Data Day Texas keynote, which provided an introduction to data contracts. If you are interested in me giving this talk at your company, please feel free to send me a DM!

https://lnkd.in/dSCb9NQn What do you mean by master data management? Master data management (MDM) involves creating a single master record for each person, place, or thing in a business, from across internal and external data sources and applications. This information has been de-duplicated, reconciled, and enriched, becoming a consistent, reliable source.

Master Data Management plays a vital role in enhancing business and maintaining data precision. Explore how efficient MDM can revolutionize your processes and improve decision-making Learn more at https://okt.to/IrKLOQ

New blog post: Learn about Slowly Changing Dimensions (SCDs) in data warehousing. The article features a key visual tables that makes the concepts easy to grasp. https://lnkd.in/g3ZfGDzN #DataWarehousing #SCD #Idempotency #DataEngineering

𝐒𝐥𝐨𝐰𝐥𝐲 𝐂𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧 (𝐒𝐂𝐃) 📖 Let's understand Slowly Changing Dimensions (SCD) and the most important types used in data warehousing. 𝐒𝐂𝐃: Slowly Changing Dimension is a concept in data warehousing that refers to how changes in data are managed over time. It is particularly important for maintaining historical accuracy and ensuring that data analysis can account for changes in dimension attributes. There are multiple types of SCD, but the most important and widely adopted in data warehousing are SCD1, SCD2, and SCD3. Let's break down each type and understand them one by one. 𝐓𝐲𝐩𝐞 𝟏 𝐒𝐂𝐃 (𝐎𝐯𝐞𝐫𝐰𝐫𝐢𝐭𝐞): If a record in the dimension table changes, the existing record is updated or overwritten. Otherwise, a new record is inserted into the dimension table. This approach always reflects the latest record in the dimension table. In simple terms, existing records are overwritten or new records are inserted without maintaining historical records. 𝐓𝐲𝐩𝐞 𝟐 𝐒𝐂𝐃 (𝐀𝐝𝐝 𝐍𝐞𝐰 𝐑𝐨𝐰): One of the most popular and widely used SCD types in data warehousing. Type 2 maintains historical data by inserting a new row in the dimension table without overwriting existing records. A flag column is used to check whether the record is active or not. 𝐓𝐲𝐩𝐞 𝟑 𝐒𝐂𝐃 (𝐀𝐝𝐝 𝐍𝐞𝐰 𝐂𝐨𝐥𝐮𝐦𝐧): A new column is added to track changes. This approach preserves some history but is limited to the number of changes that can be tracked, typically only the current and previous state, while still allowing for some tracking of changes. Share your experience with SCD and its best practices. If you found this helpful, like and repost to your connections. Let's connect for more insightful content. 😊 #SCD #database #DataWarehouse #DataAnalysis #SlowlyChangingDimension #HappyLearning

Everyone is talking about data contracts but standardization still needs to be reached on that topic. From different definitions to a myriad of tools to implement those, it can be quite daunting to start your data contract journey. In this article, we try to demystify the origin and concepts of data contracts and the main tools to implement them. We then take an opinionated standpoint on the “data contract CLI” (thanks Dr. Simon Harrer for this great discovery) and how we think this tool-agnostic framework has a bright future. 🚀 Everyone's Talking About Data Contracts! But, have we reached standardization yet? 🤔 🔗 Dive into our latest article as we explore the exciting world of data contracts:  ➡️Data Contract CLI: The Future of Data Contract Definition https://lnkd.in/ekYWRNYA Here’s what you’ll discover: 📚 Definitions & Origins: Understand what data contracts are and where they come from. 🛠️ Tools & Implementations: An overview of the myriad tools available to implement data contracts. 🌟 Spotlight on Data Contract CLI: (special thanks to Dr. Simon Harrer for the introduction on this tool)! We discuss why this tool-agnostic framework is poised for a bright future. ✨ Starting your data contract journey can seem daunting, but it doesn’t have to be. Enjoy the read #DataContracts #DataQuality

I developed an integrated methodology to structure and formalize business requirements for large, data-intensive projects, such as data warehouse implementations. This approach turns business requirements into clear and precise data definitions, which help in harmonizing data and assigning data governance responsibilities. #dailythought