refactor project directory structure (#170)

* Rename `infrastructure/terraform-gcp/` to `infrastructure/gcp/`
* Rename `assets/` to `.assets`/
* Rename `visualization/` to `dataviz/` (metabase and superset modules)
* Update Terraform - Google provider version from `6.x` to `7.19.0`
* Update main README to reflect the new directory structure

Author: iobruno

assets/terraform-service-account-pt1.png .assets/terraform-service-account-pt1.pngassets/terraform-service-account-pt1.png renamed to .assets/terraform-service-account-pt1.png

@@ -11,7 +11,7 @@ Refer to the modules below for covered topics and tools.
 * Rust data ingestion
 * [ELT Ingestion with Airbyte](./module1-data-ingestion/airbyte/)
 * [data load tool (dlt)](./module1-data-ingestion/data-load-tool/)
-* [IaC with Terraform (Google Cloud Platform)](./infrastructure/terraform-gcp/)
+* [IaC with Terraform (Google Cloud Platform)](./infrastructure/gcp/)
 
 ### [Module 2: Workflow orchestration](./module2-workflow-orchestration/)
 * Workflow orchestration with Airflow 3.x
@@ -24,21 +24,26 @@ Refer to the modules below for covered topics and tools.
 
 ### [Module 4: Analytics engineering](./module4-analytics-engineering/)
 * [BigQuery and dbt](./module4-analytics-engineering/bigquery/)
+* Snowflake and dbt
 * Databricks and dbt
 * [Redshift and dbt](./module4-analytics-engineering/redshift/)
 * [ClickHouse and dbt](./module4-analytics-engineering/clickhouse/)
 * [PostgreSQL and dbt](./module4-analytics-engineering/postgres/)
 * [DuckDB and dbt](./module4-analytics-engineering/duckdb/)
-* [Data visualization with Superset/Metabase](./module4-analytics-engineering/visualization/)
+* [Data visualization with Metabase](./module4-analytics-engineering/dataviz/metabase/)
+* [Data visualization with Superset](./module4-analytics-engineering/dataviz/superset/)
 
 ### [Module 5: Batch processing](./module5-batch-processing/)
-* [PySpark 4.x](./module5-batch-processing/pyspark-4.x/)
+* [PySpark 4.x + Spark Connect](./module5-batch-processing/pyspark-4.x/)
+* [PySpark 3.x + Spark Connnect](./module5-batch-processing/pyspark-3.x/)
 * Spark + Kotlin API
 * Spark (Scala)
 
 ### [Module 6: Stream processing](./module6-stream-processing/)
+* PyFlink
 * [Stream processing with Kafka, ksqlDB and Kotlin](./module6-stream-processing/kotlin/)
 * [Kafka Streams with ksqlDB](./module6-stream-processing/ksqldb/)
 
 ### Extras
 * [LakeHouse with Delta, Iceberg, Hive](https://github.com/iobruno/lakehouse-labs/)
+* [Data Catalog with DataHub](https://github.com/iobruno/data-catalog-labs)

assets/terraform-service-account-pt2.png .assets/terraform-service-account-pt2.pngassets/terraform-service-account-pt2.png renamed to .assets/terraform-service-account-pt2.png

@@ -16,8 +16,8 @@ On the GCP Console, create a new `Service Account` with the roles of:
 
 Next, access the created service_account, and create a 'New Key' with `Key type: JSON`, and save it somewhere safe on your workstation
 
-![terraform-service-account](../../assets/terraform-service-account-pt1.png)
-![terraform-service-account](../../assets/terraform-service-account-pt2.png)
+![terraform-service-account](../../.assets/terraform-service-account-pt1.png)
+![terraform-service-account](../../.assets/terraform-service-account-pt2.png)
 
 
 Now, export the environment variables `GOOGLE_APPLICATION_CREDENTIALS` pointing to the full path where the .json credentials file was downloaded/saved:
@@ -33,7 +33,7 @@ export GOOGLE_APPLICATION_CREDENTIALS=/path/to/gcp-credentials.json
 
 In [Google Cloud Storage](https://console.cloud.google.com/storage/browser?hl=en&project=iobruno-gcp-labs), create a bucket that Terraform will use as its backend to save state:
 
-![terraform-state-bucket](../../assets/terraform-tfstate-bucket.png)
+![terraform-state-bucket](../../.assets/terraform-tfstate-bucket.png)
 
 
 **2.** Configure Terraform backend for GCS: