Fix capitalization in file titles (#1341)

docs/examples/qdrant_zendesk/qdrant_zendesk.py

@@ -1,6 +1,6 @@
 """
 ---
-title: Similarity Searching with Qdrant
+title: Similarity searching with Qdrant
 description: Learn how to use the dlt source, Zendesk and dlt destination, Qdrant to conduct a similarity search on your tickets data.
 keywords: [similarity search, example]
 ---

docs/website/docs/build-a-pipeline-tutorial.md

@@ -1,5 +1,5 @@
 ---
-title: Pipeline Tutorial
+title: Pipeline tutorial
 description: Build a data pipeline with dlt from scratch
 keywords: [getting started, quick start, basics]
 ---

docs/website/docs/dlt-ecosystem/file-formats/csv.md

@@ -4,7 +4,7 @@ description: The csv file format
 keywords: [csv, file formats]
 ---
 
-# CSV File Format
+# CSV file format
 
 **csv** is the most basic file format to store tabular data, where all the values are strings and are separated by a delimiter (typically comma).
 `dlt` uses it for specific use cases - mostly for the performance and compatibility reasons.

docs/website/docs/dlt-ecosystem/file-formats/parquet.md

@@ -4,7 +4,7 @@ description: The parquet file format
 keywords: [parquet, file formats]
 ---
 
-# Parquet File Format
+# Parquet file format
 
 [Apache Parquet](https://en.wikipedia.org/wiki/Apache_Parquet) is a free and open-source column-oriented data storage format in the Apache Hadoop ecosystem. `dlt` is capable of storing data in this format when configured to do so.
 

docs/website/docs/dlt-ecosystem/verified-sources/index.md

@@ -1,5 +1,5 @@
 ---
-title: Verified Sources
+title: Verified sources
 description: List of verified sources
 keywords: ['verified source']
 ---

docs/website/docs/general-usage/credentials/config_providers.md

@@ -1,13 +1,10 @@
 ---
-title: Configuration Providers
+title: Configuration providers
 description: Where dlt looks for config/secrets and in which order.
 keywords: [credentials, secrets.toml, secrets, config, configuration, environment
       variables, provider]
 ---
 
-# Configuration Providers
-
-
 Configuration Providers in the context of the `dlt` library
 refer to different sources from which configuration values
 and secrets can be retrieved for a data pipeline.

docs/website/docs/general-usage/credentials/config_specs.md

@@ -1,12 +1,10 @@
 ---
-title: Configuration Specs
+title: Configuration specs
 description: How to specify complex custom configurations
 keywords: [credentials, secrets.toml, secrets, config, configuration, environment
       variables, specs]
 ---
 
-# Configuration Specs
-
 Configuration Specs in `dlt` are Python dataclasses that define how complex configuration values,
 particularly credentials, should be handled.
 They specify the types, defaults, and parsing methods for these values.

docs/website/docs/general-usage/credentials/configuration.md

@@ -1,12 +1,10 @@
 ---
-title: Secrets and Configs
+title: Secrets and configs
 description: What are secrets and configs and how sources and destinations read them.
 keywords: [credentials, secrets.toml, secrets, config, configuration, environment
       variables]
 ---
 
-# Secrets and Configs
-
 Use secret and config values to pass access credentials and configure or fine-tune your pipelines without the need to modify your code.
 When done right you'll be able to run the same pipeline script during development and in production.
 

docs/website/docs/general-usage/glossary.md

@@ -38,7 +38,7 @@ The data store where data from the source is loaded (e.g. Google BigQuery).
 Moves the data from the source to the destination, according to instructions provided in the schema
 (i.e. extracting, normalizing, and loading the data).
 
-## [Verified Source](../walkthroughs/add-a-verified-source)
+## [Verified source](../walkthroughs/add-a-verified-source)
 
 A Python module distributed with `dlt init` that allows creating pipelines that extract data from a
 particular **Source**. Such module is intended to be published in order for others to use it to

docs/website/docs/general-usage/schema-contracts.md

@@ -1,11 +1,9 @@
 ---
-title: 🧪 Schema and Data Contracts
+title: 🧪 Schema and data contracts
 description: Controlling schema evolution and validating data
 keywords: [data contracts, schema, dlt schema, pydantic]
 ---
 
-## Schema and Data Contracts
-
 `dlt` will evolve the schema at the destination by following the structure and data types of the extracted data. There are several modes
 that you can use to control this automatic schema evolution, from the default modes where all changes to the schema are accepted to
 a frozen schema that does not change at all.

docs/website/docs/general-usage/schema-evolution.md

@@ -1,24 +1,25 @@
 ---
-title: Schema Evolution
+title: Schema evolution
 description: A small guide to elaborate on how schema evolution works
 keywords: [schema evolution, schema, dlt schema]
 ---
 
-# Schema evolution
-
 ## When to use schema evolution?
+
 Schema evolution is a best practice when ingesting most data. It’s simply a way to get data across a format barrier.
 
 It separates the technical challenge of “loading” data, from the business challenge of “curating” data. This enables us to have pipelines that are maintainable by different individuals at different stages.
 
 However, for cases where schema evolution might be triggered by malicious events, such as in web tracking, data contracts are advised. Read more about how to implement data contracts [here](https://dlthub.com/docs/general-usage/schema-contracts).
 
 ## Schema evolution with `dlt`
+
 `dlt` automatically infers the initial schema for your first pipeline run. However, in most cases, the schema tends to change over time, which makes it critical for downstream consumers to adapt to schema changes.
 
 As the structure of data changes, such as the addition of new columns, changing data types, etc., `dlt` handles these schema changes, enabling you to adapt to changes without losing velocity.
 
 ## Inferring a schema from nested data
+
 The first run of a pipeline will scan the data that goes through it and generate a schema. To convert nested data into relational format, `dlt` flattens dictionaries and unpacks nested lists into sub-tables.
 
 We’ll review some examples here and figure out how `dlt` creates initial schema and how normalisation works. Consider a pipeline that loads the following schema:

docs/website/docs/reference/command-line-interface.md

@@ -1,11 +1,9 @@
 ---
-title: Command Line Interface
-description: Command Line Interface (CLI) of dlt
+title: Command line interface
+description: Command line interface (CLI) of dlt
 keywords: [command line interface, cli, dlt init]
 ---
 
-# Command Line Interface
-
 ## `dlt init`
 
 ```sh

docs/website/docs/reference/frequently-asked-questions.md

@@ -1,5 +1,5 @@
 ---
-title: Frequently Asked Questions
+title: Frequently asked questions
 description: Questions asked frequently by users in technical help or github issues
 keywords: [faq, usage information, technical help]
 ---

docs/website/docs/tutorial/grouping-resources.md

@@ -1,5 +1,5 @@
 ---
-title: Resource Grouping and Secrets
+title: Resource grouping and secrets
 description: Advanced tutorial on loading data from an API
 keywords: [api, source, decorator, dynamic resource, github, tutorial]
 ---

docs/website/docs/walkthroughs/zendesk-weaviate.md

@@ -1,10 +1,10 @@
 ---
-title: 'Import Ticket Data from Zendesk API to Weaviate'
+title: 'Import ticket data from Zendesk API to Weaviate'
 description: How to Import Ticket Data from Zendesk API to Weaviate
 keywords: [how to, zendesk, weaviate, vector database, vector search]
 ---
 
-# How to Import Ticket Data from Zendesk API to Weaviate
+# How to import ticket data from Zendesk API to Weaviate
 
 Zendesk is a cloud-based customer service and support platform. Zendesk Support API, which is also known as the Ticketing API lets’s you access support tickets data. By analyzing this data, businesses can gain insights into customer needs, behavior, trends, and make data-driven decisions. The newest type of databases, vector databases, can help in advanced analysis of tickets data such as identifying common issues and sentiment analysis.