fix: Remove deprecated old-format CAggs information (#3989)

Author: fabriziomello

use-timescale/continuous-aggregates/about-continuous-aggregates.md

@@ -212,30 +212,22 @@ Continuous aggregates consist of:
 ### Materialization hypertable
 
 Continuous aggregates take raw data from the original hypertable, aggregate it,
-and store the intermediate state in a materialization hypertable. When you query
-the continuous aggregate view, the state is returned to you as needed.
+and store the aggregated data in a materialization hypertable. When you query
+the continuous aggregate view, the aggregated data is returned to you as needed.
 
 Using the same temperature example, the materialization table looks like this:
 
-|day|location|chunk|avg temperature partial|
+|day|location|chunk|avg temperature|
 |-|-|-|-|
-|2021/01/01|New York|1|{3, 219}|
-|2021/01/01|Stockholm|1|{4, 280}|
+|2021/01/01|New York|1|73|
+|2021/01/01|Stockholm|1|70|
 |2021/01/02|New York|2||
-|2021/01/02|Stockholm|2|{5, 345}|
+|2021/01/02|Stockholm|2|69|
 
 The materialization table is stored as a Timescale hypertable, to take
 advantage of the scaling and query optimizations that hypertables offer.
 Materialization tables contain a column for each group-by clause in the query,
-a `chunk` column identifying which chunk in the raw data this entry came from,
-and a `partial aggregate` column for each aggregate in the query.
-
-The partial column is used internally to calculate the output. In this example,
-because the query looks for an average, the partial column contains the number
-of rows seen, and the sum of all their values. The most important thing to know
-about partials is that they can be combined to create new partials spanning all
-of the old partials' rows. This is important if you combine groups that span
-multiple chunks.
+and an `aggregate` column for each aggregate in the query.
 
 For more information, see [materialization hypertables][cagg-mat-hypertables].
 
@@ -248,13 +240,6 @@ unblocks other transactions, and materializes the aggregates. The first
 transaction is very quick, and most of the work happens during the second
 transaction, to ensure that the work does not interfere with other operations.
 
-When you query the continuous aggregate view, the materialization engine
-combines the aggregate partials into a single partial for each time range, and
-calculates the value that is returned. For example, to compute an average, each
-partial sum is added up to a total sum, and each partial count is added up to a
-total count, then the average is computed as the total sum divided by the total
-count.
-
 ### Invalidation engine
 
 Any change to the data in a hypertable could potentially invalidate some

use-timescale/continuous-aggregates/create-a-continuous-aggregate.md

@@ -75,14 +75,6 @@ the hypertable. The time bucket allows you to define a time interval, instead of
 having to use specific timestamps. For example, you can define a time bucket as
 five minutes, or one day.
 
-When the continuous aggregate is materialized, the materialization table stores
-partials, which are then used to calculate the result of the query. This means a
-certain amount of processing capacity is required for any query, and the amount
-required becomes greater as the interval gets smaller. Because of this, if you
-have very small intervals, it can be more efficient to run the aggregate query
-on the raw data in the hypertable. You should test both methods to determine
-what is best for your dataset and desired bucket interval.
-
 You can't use [time_bucket_gapfill][api-time-bucket-gapfill] directly in a
 continuous aggregate. This is because you need access to previous data to
 determine the gapfill content, which isn't yet available when you create the

use-timescale/continuous-aggregates/materialized-hypertables.md

@@ -9,7 +9,7 @@ tags: [materialized views]
 # Materialized hypertables
 
 Continuous aggregates take raw data from the original hypertable, aggregate it,
-and store the intermediate state in a materialization hypertable. You can modify
+and store the aggregated data in a materialization hypertable. You can modify
 this materialized hypertable in the same way as any other hypertable.
 
 ## Discover the name of a materialized hypertable