How can you effectively handle duplicated data during transformation?

Here are some key guidelines I use for handling duplicated data which include identifying and removing duplicates based on key criteria(set by the business requirements) and regularly updating records. Tools such as Microsoft Excel (using functions like “Remove Duplicates”), Python libraries like Pandas (using functions like df.drop_duplicates()), and database management systems like SQL (using functions like SELECT DISTINCT) can be effective for detecting and managing duplicated data. Additionally, the process of profiling the data helps in understanding the content and quality of the data, ensuring it meets the required standards for accuracy and reliability.

Lidar com dados duplicados durante a transforma??o é crucial para manter a qualidade e precis?o dos dados. Em um primeiro momento é interessante realizar uma análise exploratória dos dados para identificar e entender a extens?o dos dados duplicados. Em seguida, faz-se uma compara??o para identificar registros duplicados com base no critérios escolhido, como identificadores únicos ou combina??es de atributos. Após a identifica??o, é necessário estabelecer novos critérios específicos para remo??o dos registros duplicados, como por exemplo manter o registro mais recente, para só ent?o deletar os dados duplicados.

The most common reason for duplication is the problem with extraction itself. While the source system many times can’t be enforced to give unique values, it’s better we have a coping mechanism build into the data phases while dumping it into data lake. This will ensure that when you start using it for any purpose {transform/ subset queries etc} you have unique values. The other most common problem is not knowing your business keys for the data you’re dealing with. You’ll probably end up putting wrong joins in your queries which will result in duplicate data.

The source of duplication must be identify before finalising the strategy to remove it. Tools and technologies like Power Query, SQL etc could be my choice to identify and handle the redundant data.

Handling duplicated data during transformation requires a multi-pronged approach. First, identify duplicates using unique identifiers or algorithms. Next, decide on the strategy: removing, merging, or flagging duplicates. Utilize tools like pandas or SQL queries for data manipulation. Standardize data entry methods to prevent future duplicates. Lastly, validate transformed data to ensure accuracy. Remember, a systematic process coupled with the right tools ensures efficient handling of duplicated data.

Handling duplicated data is like gardening in a vibrant greenhouse. Step 1: Pruning the Overgrowth—identify and remove duplicates like pruning excessive foliage for a healthy garden. Step 2: Cultivating the Soil—establish data quality processes, ensuring a fertile ground for accurate information. Step 3: Regular Maintenance—continuously weed out duplicates, much like tending to the garden to prevent overgrowth. This gardening analogy illustrates how, similar to nurturing a flourishing garden, handling duplicated data involves pruning, cultivating, and regular maintenance for a thriving and accurate dataset.

When there are multiple tables involved in creating the final dataset, then that requires one to have a good understanding of joins. If there are multiple matching values in either of the tables being joined then that would lead to duplicate records in resulting dataset. So one needs to ensure: 1. Either correct filters are applied post join 2. Or the tables are joined on primary key attributes.

Data duplication is part of the data management process, you always can use the process that data management establishes for this purpose but if you are working with customers who want strong documentation for better outcomes… you should recommend data duplicate reports by data set and monitor the process until the results comes out with less or no duplicate results.