# # Get aggregate time series data from CDF

Time series typically store large quantities of time-bound measurements (pressure, rpm, power generation measures, temperature, wind, etc.) as data points at millisecond-level precision. To extract insights from the data points, you typically compute aggregates from the raw data, such as averages over time intervals, variance, etc.

Cognite Data Fusion (CDF) pre-calculates the most common **aggregates** for numerical data points in time series. These aggregates are available with short response time even when you are querying across very large data sets.

Instead of downloading and manually aggregating data in Power BI, you can query CDF for the aggregates. Specify the **time range** and the **granularity** (the unit of time to compute the aggregates on) and let CDF do the computational work for you.

**In this article:**

## # Get aggregates for a single time series

Follow these steps to retrieve aggregates for **a single time series** from CDF in the Power Query Editor:

In the Power Query Editor, get the time series table using the Cognite Power BI connector.

Use filtering to find the time series you want aggregates from, and then in the

**Aggregate**column select**Function**.Specify the time range and granularity, and then select

**OK**.The Power Query Editor displays a table with the aggregates for the specified time range. Remove the columns you don’t need, then select

**Close and apply**.

## # Get aggregates for multiple time series

To retrieve aggregates from **multiple time series** in the same time range, use the Cognite Power BI connector and the **TimeseriesAggregate** function. Instead of calling the function on a singles time series, you first create a time series table with all time series you want to retrieve aggregates from:

Import your

**Time series**data and the**TimeseriesAggregate**function using the Cognite Power BI connector.Use filtering to find the time series you want aggregates from.

In the Queries panel, select the

**TimeseriesAggregate**function, set the**granularity**,**start**time, and**end**time. Then select**Choose column**.In the Select Column window, select your

**Timeseries**table, and the**Id**column from the table to specifiy which time series to fetch aggregates from in CDF. Then select**OK**.Select

**Invoke**to start retrieving the aggregates.The Power Query Editor displays a new table named

**Invoked Function**in the Query panel with the aggregates for the specified time range. Remove the columns you don’t need, then select**Close and apply**.

### # Tips for the TimeseriesAggregate function

If the **TimeseriesAggregate** function takes a long time to finish, or fails after a long time:

Make sure you have filtered the time series table so that it contains only the time series you need.

Consider if you are using the right granularity. The granularity greatly affects the query performance.

Make sure that you are requesting a realistic number of data points. Aggregating large numbers of time series with short granularities is likely to return huge amounts of data points, and Power BI does not handle multiple gigabytes of data in a single query.