Contents
What are the steps to normalization?
The following steps will help in attaining database normalization in MySQL.
- Step 1: Create first normal form (1NF)
- Step 2: Define relationships.
- Step 3: Make second normal form (2NF)
- Step 4: Third Normal Form (3NF)
What problems you may encounter when you try to process the data through the ETL layer?
Underestimating data transformation requirements
- Establishing key relationships across data sources, many of which might not exist in the raw data.
- Updating new values on existing records without sacrificing performance.
- Time zone consistency.
- Lookup value normalization (US = United States = USA)
What are the challenges of ETL?
The main challenges of ETL or Data warehouse testing includes :
- Data loss during ETL testing.
- Duplicate data and Incompatibility.
- Lack of inclusive test bed.
- Testers have no benefits to execute ETL jobs by their own.
- Data volume and complexity is huge.
- Inefficient in procedures and business process.
What do you need to know about the ETL process?
This ETL requirement and gathering process will include necessary preparation for carrying out data integration.
Why do we need incremental loading in ETL?
Support for change data capture (CDC) (a.k.a. binlog replication): Incremental loading allows you to update your analytics warehouse with new data without doing a full reload of the entire data set. We say more about this in the ETL Load section.
What are some examples of problems with ETL?
Data from different systems typically don’t play well together, and it requires work to get them to cooperate. Here are some common examples: Establishing key relationships across data sources, many of which might not exist in the raw data Updating new values on existing records without sacrificing performance
What is the importance of ETL in OLAP?
Short for Extract – Transform – Load, ETL is an important component in the data ecosystem of any modern business and is mostly used for OLAP processing in a data warehouse. The ETL process is what helps break down data silos and makes data easier to access for decision-makers.