SQL code snippets
Quickly transform columns, create rolling averages, build bins, or run more complex window functions
When working with Tellius, you may need to perform data transformations beyond simple aggregations or filters. SparkSQL (the SQL engine used by Tellius) offers a host of built-in functions and advanced window operations—like converting time zones, lag/lead comparisons, rolling averages, and more. Below, we highlight several commonly needed transformations, their use cases, and sample SQL queries.
You can adapt them to your own datasets in the Tellius SQL editor (check out this page for more details on how to access and apply these transformations in your workflow).
1. Converting a time column to UTC
Scenario: You have a timestamp column in a specific time zone and want to standardize it to UTC for consistent analysis.
SELECT
*,
to_utc_timestamp(origin_timestamp, origin_timezone) AS time_utc
FROM table_name
Function:
to_utc_timestamp()
Explanation: Converts
origin_timestamp
, which is inorigin_timezone
, to UTC.Example: If
origin_timezone
is 'America/Los_Angeles', thentime_utc
becomes the corresponding UTC timestamp.
2. Calculating Lag/Lead
Scenario: Compare current row values to a previous (lag) or next (lead) row. Useful for identifying changes or trends (e.g., comparing week-over-week or month-over-month sales).
Using LAG
SELECT
*,
LAG(Sales) OVER (PARTITION BY Order_Id ORDER BY Date) AS Prev_Sales
FROM table_name
Function:
LAG(column) OVER(...)
Explanation: Retrieves the prior Sales value for the same
Order_Id
, ordered byDate
.Example: Compare this week’s Sales to the previous week’s (
Prev_Sales
) to see the difference.
Using LEAD
SELECT
*,
LEAD(Sales) OVER (PARTITION BY Order_Id ORDER BY Date) AS Next_Sales
FROM table_name
Function:
LEAD(column) OVER(...)
Explanation: Retrieves the next Sales value for the same
Order_Id
, ordered byDate
.Example: Forecast or compare today’s Sales to a post-period’s Sales.
3. Creating rolling averages
Scenario: Track moving averages (e.g., a 2-week rolling average of Sales), excluding the current row to gauge past performance.
SELECT
*,
AVG(Sales) OVER (
PARTITION BY Order_Id
ORDER BY Date
ROWS BETWEEN 2 PRECEDING AND 1 PRECEDING
) AS Two_Weeks_Rolling_Average
FROM table_name
Function:
AVG(column) OVER(...)
with a window frame definition.Explanation: Looks at the two preceding rows (excluding current row) to compute an average.
Note: Adjust the frame (e.g.,
ROWS BETWEEN 3 PRECEDING AND 1 PRECEDING
) for a 3-day/week/month rolling window.
4. Creating bins/buckets with NTILE
Scenario: Segment numeric values (e.g., net_sales
) into a fixed number of buckets (like quartiles or deciles) for each product category.
SELECT
product_category_name,
month,
net_sales,
NTILE(4) OVER (
PARTITION BY product_category_name
ORDER BY net_sales DESC
) AS net_sales_group
FROM table_name
Function:
NTILE(n) OVER(...)
Explanation: Divides the ordered partition into
n
buckets. Here, 4 bins give you quartiles.Example: Quickly categorize product categories into 1 (highest net sales) through 4 (lowest net sales).
5. Creating an average in the same table
Scenario: You want to compare each row’s Sales
to the state-wide average.
SELECT
a.*,
b.State_Sales_Average
FROM table_name a
LEFT JOIN (
SELECT
State,
AVG(Sales) AS State_Sales_Average
FROM table_name
GROUP BY State
) b
ON a.State = b.State
Explanation: Sub-query or common table expression (CTE) for computing aggregated values, then join back to the main table. Each row in
a
now has a new columnState_Sales_Average
for contextual comparison.
6. Using CASE statements
Scenario: Conditionally update column values based on custom logic.
SELECT
Employee_Name,
CASE
WHEN StateCode = 'AR' THEN 'FL'
WHEN StateCode = 'GE' THEN 'AL'
ELSE StateCode
END AS StateCode
FROM table_name
Function:
CASE WHEN ... THEN ... ELSE ... END
Explanation: If
StateCode
is 'AR', set it to 'FL'. If 'GE', set it to 'AL'. Otherwise, keep existing value.
7. Handling NULLs with COALESCE
SELECT
COALESCE(Sales, 0) AS Sales_No_Nulls,
COALESCE(Comments, 'N/A') AS Comments_Filled
FROM table_name
Function:
COALESCE(column, default_value)
Explanation: Replaces
NULL
with a default (numeric or string) value.
8. Date/Time truncation
SELECT
date_trunc('month', timestamp_col) AS month_start,
COUNT(*) AS num_events
FROM table_name
GROUP BY date_trunc('month', timestamp_col)
Function:
date_trunc('unit', column)
Explanation: Rounds down the timestamp to a specified boundary (hour, day, week, month, quarter, etc.).
9. Calculating time differences
SELECT
start_time,
end_time,
datediff(end_time, start_time) AS diff_in_days
FROM table_name
Function:
datediff(end, start)
Explanation: Returns the difference in days between two date/timestamp columns.
These SQL snippets illustrate some of the powerful transformation capabilities available in SparkSQL—particularly beneficial for preparing and enriching your data in Tellius before deeper analysis. By combining basic functions (e.g., CASE
, COALESCE
) with advanced window operations (LAG
, LEAD
, NTILE
, OVER
clauses), you can build sophisticated data transformations directly in the Tellius SQL editor.
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