Quais são algumas técnicas para evitar a duplicação de dados durante a manipulação de dados?

1. Data duplication is often a result of inadequate planning rather than intentional actions, making it necessary to incorporate design and careful consideration to eliminate or engage in "deduplication." The process of removing duplicates after they have already been created can be both expensive and time-consuming. To prevent or minimize the negative effects of data duplication on your business, there are several methods you can employ. 2. One approach is to establish better data governance practices, which involve implementing policies and procedures to ensure data accuracy, consistency, and integrity. By enforcing strict guidelines and standards, you can reduce the likelihood of data duplication occurring in the first place.

3. Another method is to utilize data virtualization, which allows you to access and manage data from various sources without physically duplicating it. This approach enables you to create a unified view of your data while minimizing the risk of duplication. 4. Additionally, leveraging metadata can be beneficial in preventing data duplication. By using metadata, which provides information about the characteristics and attributes of your data, you can identify and eliminate duplicate records more efficiently. 5. Implementing these methods can reduce data duplication and its negative business impact by prioritizing planning and design to avoid costly and time-consuming removal of duplicates.

With streaming or time series data, it often makes sense to use time windows to detect and eliminate duplicates within a certain time frame. Of course, it is important to choose the right window size. A window that is too small could miss duplicates, while a window that is too large can increase processing time.

A remoção das duplicatas também pode ser feita Ad-hoc(sobre demanda) ao simplesmente usar a combinação de group by com having, outra forma também seria com o uso de window functions.

Preventing data duplication during data manipulation is crucial for maintaining data quality and accuracy. Duplicate data can lead to inconsistencies, errors, and confusion in analytical results. Apply unique constraints to relevant columns in databases. This ensures that no two records have identical values in specified columns, preventing the insertion of duplicate data. Use primary keys to uniquely identify records in a database table. Primary keys enforce uniqueness and help prevent the insertion of duplicate records. Create indexes on columns that need to be unique. Indexing can improve query performance and helps identify duplicate values efficiently. Implement data validation rules to check for duplicate values during data entry.

Although normalization reduces duplication, it can make queries more difficult due to the need for multiple links. Over-normalization can also lead to performance problems.

**First Normal Form (1NF):** Concept: Breaks down tables with non-atomic or repeating values, ensuring each column contains only atomic (indivisible) values. For example, splitting a column with multiple authors into separate rows for each author. **Second Normal Form (2NF):** Concept: Addresses partial dependencies by organizing data to remove redundancy. It involves ensuring that each column in a table is fully dependent on the primary key, preventing issues where non-key attributes depend on only a part of the primary key.

Some ways to prevent and detect possible data duplication in data integrations are: ✅ Identify duplicated data from data source by identity fields: maybe the data source have several records with same values. ✅ Identify how several records are combining to give a unified and standard record; and check that record is unique: sometimes the problems is in integration logic. ✅ Add extracted data, some metadata of extraction process like timestamp of loading or extract for example; you can differences records each others using this metadata.

This is the process of establishing and enforcing rules for how your data is managed. This can help to prevent data duplication by making it clear who is responsible for data updates and by ensuring that new data is always integrated with existing data in a way that minimizes duplication.

💥 Sometimes; by performance or scalability optimizations; changes functionality of tables by new segmentation, add new tables to extend it or lifecycle changes: this is part of evolution of data modeling and databases. 😎 Data governance participate in how all data areas work to handle this changes and avoid negativaly effect in services: way of communication for all related areas and how areas can be affected with this changes.

This involves regularly checking your data for quality issues, including duplication. This can help to identify and fix duplication problems before they become a serious issue.

Understanding the concept and applications of Idempotency is an absolute must for every savvy data engineer. In a nutshell, it's the magic that ensures repeating an operation delivers the same result, it aids in forming the rock-solid foundation of a dependable, duplicate-free data pipeline. It reinforces data replication, sculpting pipelines that gracefully dance around duplicates. Picture it like a safety net, keeping your data integrity intact by maintaining a consistent rhythm, and thwarting unintended errors caused by those repetitive data moves.

There are a couple of things which you need to ensure before you perform any kind of data manipulation to avoid duplication: 1. Validate the data so that it doesn't have any inconsistencies (that may lead to duplication) 2. If by manipulation, you mean to add some data, then cross-check if that data to be added exists already 3. Use unique identifiers, composite keys and constraints/validations to ensure further ingenuity. 4. Last and most important, use transactions, i.e. (ACID) to ensure that the data manipulation job is either completely run or rolled-back, no data is left in between, that is what leads to duplication and inconsistency in most cases.

Data Mapping and Profiling: Establish a clear data mapping and profiling process to understand the structure and relationships between different data sets. This helps prevent duplicates from creeping in from different sources.