Snowflake & dbt advice added

Added Snowflake and dbt advice

README.md

@@ -196,7 +196,9 @@ Find the change log with all recent updates here: [SEE UPDATES](sections/10-Upda
   - [Apache Drill](sections/03-AdvancedSkills.md#apache-drill)
   - [StreamSets](sections/03-AdvancedSkills.md#streamsets)
 - [Store](sections/03-AdvancedSkills.md#store)
-  - [Data Warehouse vs Data Lake](sections/03-AdvancedSkills.md#data-warehouse-vs-data-lake)
+	-[Analytical Data Stores](03-AdvancedSkills.md#analytical-data-stores)
+		- [Data Warehouse vs Data Lake](sections/03-AdvancedSkills.md#data-warehouse-vs-data-lake)
+		- [Snowflake and dbt](sections/03-AdvancedSkills.md#snowflake-and-dbt)
   - [SQL Databases](sections/03-AdvancedSkills.md#sql-databases)
     - [PostgreSQL DB](sections/03-AdvancedSkills.md#postgresql-db)
     - [Database Design](sections/03-AdvancedSkills.md#database-design)

sections/03-AdvancedSkills.md

@@ -72,7 +72,9 @@ Advanced Data Engineering Skills
   - [Apache Drill](03-AdvancedSkills.md#apache-drill)
   - [StreamSets](03-AdvancedSkills.md#streamsets)
 - [Store](03-AdvancedSkills.md#store)
-  - [Data Warehouse vs Data Lake](03-AdvancedSkills.md#data-warehouse-vs-data-lake)
+  - [Analytical Data Stores](03-AdvancedSkills.md#analytical-data-stores)
+    - [Data Warehouse vs Data Lake](03-AdvancedSkills.md#data-warehouse-vs-data-lake)
+    - [Snowflake and dbt](03-AdvancedSkills.md#snowflake-and-dbt)
   - [SQL Databases](03-AdvancedSkills.md#sql-databases)
     - [PostgreSQL DB](03-AdvancedSkills.md#postgresql-db)
     - [Database Design](03-AdvancedSkills.md#database-design)
@@ -1070,21 +1072,21 @@ it a lot easier to configure the resource management.
 
 ### Samza
 
-![Link to Apache Samza Homepage](http://samza.apache.org/)
+[Link to Apache Samza Homepage](http://samza.apache.org/)
 
 ### AWS Lambda
 
-![Link to AWS Lambda Homepage](https://aws.amazon.com/lambda/)
+[Link to AWS Lambda Homepage](https://aws.amazon.com/lambda/)
 
 
 ### Apache Flink
 
-![Link to Apache Flink Homepage](https://flink.apache.org/)
+[Link to Apache Flink Homepage](https://flink.apache.org/)
 
 
 ### Elasticsearch
 
-![Link to Elatsicsearch Homepage](https://www.elastic.co/products/elastic-stack)
+[Link to Elatsicsearch Homepage](https://www.elastic.co/products/elastic-stack)
 
 ### Graph DB
 
@@ -1133,12 +1135,12 @@ https://neo4j.com/use-cases/
 
 ### Apache Solr
 
-![Link to Solr Homepage](https://lucene.apache.org/solr/)
+[Link to Solr Homepage](https://solr.apache.org)
 
 
 ### Apache Drill
 
-![Link to Apache Drill Homepage](https://drill.apache.org/)
+[Link to Apache Drill Homepage](https://drill.apache.org)
 
 
 ### Apache Storm
@@ -1156,13 +1158,65 @@ https://storm.apache.org/
 
 ## Store
 
-### Data Warehouse vs Data Lake
+### Analytical Data Stores
+
+#### Data Warehouse vs Data Lake
 
 | Podcast Episode: #055 Data Warehouse vs Data Lake
 |------------------|
 |On this podcast we are going to talk about data warehouses and data lakes? When do people use which? What are the pros and cons of both? Architecture examples for both Does it make sense to completely move to a data lake?
 | [Watch on YouTube](https://youtu.be/8gNQTrUUwMk) \ [Listen on Anchor](https://anchor.fm/andreaskayy/episodes/055-Data-Warehouse-vs-Data-Lake-e45iem)|
 
+#### Snowflake and dbt
+
+![Snowlfake thumb](/images/03/Snowflake-dbt-thumbnail.jpeg)
+
+In the rapidly evolving landscape of data engineering, staying ahead means continuously expanding your skill set with the latest tools and technologies. Among the myriad of options available, dbt (data build tool) and Snowflake have emerged as indispensable for modern data engineering workflows. Understanding and leveraging these tools can significantly enhance your ability to manage and transform data, making you a more effective and valuable data engineer. Let's dive into why dbt and Snowflake should be at the top of your learning list and explore how the "dbt for Data Engineers" and "Snowflake for Data Engineers" courses from the Learn Data Engineering Academy can help you achieve mastery in these tools.
+
+##### The Power of Snowflake in Data Engineering
+
+Snowflake has revolutionized the data warehousing space with its cloud-native architecture. It offers a scalable, flexible, and highly performant platform that simplifies data management and analytics. Here’s why Snowflake is a critical skill for data engineers:
+
+1. **Cloud-Native Flexibility:** Snowflake’s architecture allows you to scale resources up or down based on your needs, ensuring optimal performance and cost-efficiency.
+2. **Unified Data Platform:** It unifies data silos, enabling seamless data sharing and collaboration across the organization.
+3. **Integration Capabilities:** Snowflake integrates with various data tools and platforms, enhancing its versatility in different data workflows.
+4. **Advanced Analytics:** With its robust support for data querying, transformation, and integration, Snowflake is ideal for complex analytical workloads.
+
+The "Snowflake for Data Engineers" course in my Learn Data Engineering Academy provides comprehensive training on Snowflake. From the basics of setting up your Snowflake environment to advanced data automation with Snowpipes, the course equips you with practical skills to leverage Snowflake effectively in your data projects.
+
+Learn more about the course [here](https://learndataengineering.com/p/snowflake-for-data-engineers).
+
+![Snowlfake thumb](/images/03/Snowflake-ui.jpeg)
+
+
+##### Why dbt is a Game-Changer for Data Engineers
+
+dbt is a powerful transformation tool that allows data engineers to transform, test, and document data directly within their data warehouse using simple SQL. Unlike traditional ETL tools, dbt operates on the principle of ELT (Extract, Load, Transform), which aligns perfectly with modern cloud data warehousing paradigms. Here are a few reasons why dbt is a must-have skill for data engineers:
+
+1. **SQL-First Approach:** dbt allows you to write transformations in SQL, the lingua franca of data manipulation, making it accessible to a broad range of data professionals.
+2. **Collaboration:** Teams can collaborate seamlessly, creating trusted datasets for reporting, machine learning, and operational workflows.
+3. **Ease of Use:** With dbt, you can transform, test, and document your data with ease, streamlining the data pipeline process.
+4. **Integration:** dbt integrates effortlessly with your existing data warehouse, such as Snowflake, making it a versatile addition to your toolkit.
+
+In my Learn Data Engineering Academy you find the perfect starting point for mastering dbt with the course "dbt for Data Engineers". The course covers everything from the basics of ELT processes to advanced features like continuous integration and deployment (CI/CD) pipelines. With hands-on training, you'll learn to create data pipelines, configure dbt materializations, test dbt models, and much more.
+
+Learn more about the course [here](https://learndataengineering.com/p/dbt-for-data-engineers).
+
+![Snowlfake thumb](/images/03/dbt-ui.jpeg)
+
+##### dbt and Snowflake: A Winning Combination
+
+When used together, dbt and Snowflake offer a powerful combination for data engineering. Here’s why:
+
+1. **Seamless Integration:** dbt’s SQL-first transformation capabilities integrate perfectly with Snowflake’s scalable data warehousing, creating a streamlined ELT workflow.
+2. **Efficiency:** Together, they enhance the efficiency of data transformation and analytics, reducing the time and effort required to prepare data for analysis.
+3. **Scalability:** The combined power of dbt’s model management and Snowflake’s dynamic scaling ensures that your data pipelines can handle large and complex datasets with ease.
+4. **Collaboration and Documentation:** dbt’s ability to document and test data transformations directly within Snowflake ensures that data workflows are transparent, reliable, and collaborative.
+Get right into it with our Academy!
+
+By integrating Snowflake and dbt into your skill set, you position yourself at the forefront of data engineering innovation. These tools not only simplify and enhance your data workflows but also open up new possibilities for data transformation and analysis.
+
+
 ### SQL Databases
 
 #### PostgreSQL DB
@@ -1358,7 +1412,7 @@ Google Trends Grafana vs Kibana:\
 
 #### Apache Impala
 
-![Apache Impala Homepage](https://impala.apache.org/)
+[Apache Impala Homepage](https://impala.apache.org/)
 
 #### Kudu
 

sections/10-Updates.md

@@ -3,6 +3,10 @@ Updates
 
 What's new? Here you can find a list of all the updates with links to the sections
 
+- **2024-07-08**
+  - Added new secton "Analytical Data Stores" to Advanced skills.
+  - Added large article about Snowflake and dbt [click here](03-AdvancedSkills.md#analytical-data-stores)
+
 - **2024-03-20**
   - Added roadmap for Software Engineers / Computer Scientists [click here](01-Introduction.md#roadmap-for-software-engineers)
   - Added many questions and answers from my interview on the Super Data Science Podcast (plus links to YouTube and the Podcast) [click here](01-Introduction.md#Interview-with-Andreas-on-the-Super-Data-Science-Podcast)