## What does an aggregator do in Informatica?

Use the Aggregator transformation to perform aggregate calculations, such as averages and sums, on groups of data. task performs aggregate calculations, the task stores data in groups in an aggregate cache. To use the Aggregator transformation, you need the appropriate license.

## Why aggregator transformation is used in Informatica?

Aggregator transformation is an active transformation is used to performs aggregate calculations like sum, average, etc. For example, if you want to calculate the sum of salaries of all employees department wise, we can use the Aggregator Transformation. For this, we require a new column to store this sum.

**How do you handle aggregator transformation for better performance?**

Use the following guidelines to optimize the performance of an Aggregator transformation:

- Group by simple columns.
- Use sorted input.
- Use incremental aggregation.
- Filter data before you aggregate it.
- Limit port connections.

### What is difference between aggregator and expression transformation?

Aggregator transformation is used to perform aggregate calculations such as sum,average,max min. If you compare with Expression transformation then the difference is that in the Expression transformation calculations are done by row by row whereas in Aggregator calculations are done for group.

### Which object Cannot be used in mapplet?

You cannot include the following objects in a mapplet: Normalizer transformations. Cobol sources. XML Source Qualifier transformations.

**How do you remove duplicates using aggregator transformation in Informatica?**

Aggregator Transformation – To remove the duplicate records just Group By the port JOB_ID which will group all unique records together and pass it to target. Enable Sorted Input to improve the performance of Aggregator Transformation.

## Is aggregator an active transformation?

Aggregator transformation is an active transformation. And it is used to perform calculations on the data such as sums, averages, counts, etc. The integration service stores the group of data and row data in the aggregate cache.

## What is the use of sorted input in aggregator transformation?

The Sorted Input option reduces the amount of data cached during the session and improves performance. Use this option with the Source Qualifier Number of Sorted Ports option or a Sorter transformation to pass sorted data to the Aggregator transformation.

**How does Informatica improve target performance?**

How to Optimize the Target?

- Dropping Indexes and Key Constraints.
- Use constraint-based loading only if necessary.
- Increase Database Checkpoint Intervals (Decrease the number of checkpoints).
- Configure the flat file target that is local to the Integration Service process node.

### What is the difference between bulk load and normal load?

The main difference between normal and bulk load is, in normal load Informatica repository service create logs and in bulk load log is not being created. That is the reason bulk load loads the data fast and if anything goes wrong the data cannot be recovered.

### Which is better in terms of performance Informatica Joiner transformation vs lookup?

In case of Flat file, generally, sorted joiner is more effective than lookup, because sorted joiner uses join conditions and caches less rows. In case of database, lookup can be effective if the database can return sorted data fast and the amount of data is small, because lookup can create whole cache in memory.

**Can we use aggregate functions in expression transformation?**

You can also use aggregate functions as window functions in an Expression transformation. To use an aggregate function as a window function when you run a mapping on the Spark engine, you must configure the transformation for windowing.