Integrate: Improve guidance for data import / ingest / load

docs/howto/index.md

@@ -41,7 +41,7 @@ Instructions how to get tasks done with CrateDB.
 - {ref}`Using JMeter with CrateDB <jmeter>`
 - {ref}`langchain-usage`
 - {ref}`metabase-usage`
-- {ref}`pandas-efficient-ingest`
+- {ref}`pandas-bulk-import`
 - {ref}`PyCaret and CrateDB <pycaret>`
 - {ref}`rill-usage`
 - {ref}`marquez-usage`

docs/integrate/dask/usage.md

@@ -1,6 +1,6 @@
 (dask-usage)=
-(dask-efficient-ingest)=
-# Efficient data ingestion with Dask and CrateDB
+(dask-bulk-import)=
+# Efficient bulk imports with Dask
 
 ## Introduction
 Dask is a parallel computing library that enables distributed computing for tasks such as data processing and machine learning.

docs/integrate/kafka/docker-python.md

@@ -4,7 +4,7 @@
 This walkthrough demonstrates how to load data from a Kafka topic into a
 CrateDB table, using a Python consumer and CrateDB's HTTP interface.
 
-## Starting services
+## Start services
 
 Start Kafka and CrateDB using Docker Compose.
 ```yaml
@@ -45,7 +45,7 @@ networks:
 docker compose up -d
 ```
 
-## Provisioning CrateDB and Kafka
+## Provision CrateDB and Kafka
 
 * CrateDB Admin UI: `http://localhost:4200`
 * Kafka broker (inside-compose hostname): kafka:9092
@@ -86,9 +86,9 @@ EOF
 
 Messages are newline-delimited JSON for simplicity.
 
-## Loading data
+## Data loading
 
-### Create a simple consumer using Python
+Create a simple consumer using Python.
 
 ```python
 # quick_consumer.py
@@ -146,7 +146,7 @@ python quick_consumer.py
 This shows the custom client path: transform/filter as you like, do idempotent upserts on (device_id, ts), and batch writes for speed.
 :::
 
-## Verifying the data
+## Explore data
 
 Use `curl` to submit a `SELECT` statement that verifies data has been stored in CrateDB.
 ```bash

docs/integrate/pandas/efficient-ingest.md

@@ -1,5 +1,5 @@
-(pandas-efficient-ingest)=
-# Guide to efficient data ingestion to CrateDB with pandas
+(pandas-bulk-import)=
+# Efficient bulk imports with pandas
 
 ## Introduction
 Bulk insert is a technique for efficiently inserting large amounts of data into a database by submitting multiple rows of data in a single database transaction. Instead of executing multiple SQL `INSERT` statements for each individual row of data, the bulk insert allows the database to process and store a batch of data at once. This approach can significantly improve the performance of data insertion, especially when dealing with large datasets.

docs/integrate/pandas/index.md

@@ -37,8 +37,8 @@ data structures and operations for manipulating numerical tables and time series
 - {ref}`pandas-tutorial-start`
 - {ref}`pandas-tutorial-jupyter`
 - {ref}`arrow-import-parquet`
-- {ref}`pandas-efficient-ingest`
-- See also: {ref}`dask-efficient-ingest`
+- {ref}`pandas-bulk-import`
+- See also: {ref}`dask-bulk-import`
 - See also: [Efficient batch/bulk INSERT operations with pandas, Dask, and SQLAlchemy]
 
 :::{rubric} Code examples