Analytics engineering with Snowflake and dbt

This 2-day training is dedicated to data analysts, analytics engineers & data engineers, who are interested in learning how to build and deploy Snowflake data transformation workflows faster than ever before.

With dbt you can transform your data in Snowflake simply with the power of SQL select statements. If you support it with deployment automations and engineering best practices, you’ll get a production ready data value chain for your data products. Data modeling strategies, automated testing, metadata management and performance optimization are only a few of the topics that will be covered.

Training outcome

Ingest data into Snowflake using modern data integration tools
Build end-to-end data transformation models for your Snowflake-based data
Apply dbt to configure and build Snowflake data pipelines in a modern way: choose Snowflake-specific table materialization options, manage job dependencies, data quality issues, generate and view documentation as you develop
Leverage Snowpark Python integration with dbt's Python models for non-SQL workflows
Schedule data pipelines with Apache Airflow

Course agenda^*

Day 1

Session #1 - Introduction to Snowflake & dbt
- What is Snowflake? Intro
- Key components of Snowflake ecosystem
- Core concepts of dbt
- Data models
- Seeds, sources
- Tests
- Documentation
- Jinja and Macros
- Hands-on exercises
Session #2 - Simple end-do-end data pipeline
- Data discovery (data search, usage statistics, data lineage)
- Data profiling & exploration
- Transforming data using SQL with dbt
- Hands-on exercises

Day 2

Session #3 - Data pipeline - ingestion, scheduling & deployment
- Data ingestion with modern data integration tools
- Apache Airflow as a workflow scheduler
- Data quality testing with dbt test
- Hands-on exercises
Session #4 - Data pipeline - advanced models & data observability
- Snowpark with dbt Python models
- Orchestrating Snowpark with Apache Airflow
- Anomaly detection & data observability (using e.g. Elementary, Soda)
- Hands-on exercises

^* GetInData reserves the right to make any changes and adjustments to the presented agenda.

Klaudia Wachnio

klaudia@getindata.com

off

Testimonials

Completed in half the estimated time and with a fivefold improvement on data collection goals, the robust product has exponentially increased processing capabilities. GetInData’s in-depth engagement, reliability, and broad industry knowledge enabled seamless project execution and implementation.

Wojciech Ptak

CTO

GetInData had been supporting us in building production Big Data infrastructure and implementing real-time applications that process large streams of data. In light of our successful cooperation with GetInData, their unique experience and the quality of work delivered, we recommend the company as a Big Data vendor.

Miłosz Balus

CTO

GetInData delivered a robust mechanism that met our requirements. Their involvement allowed us to add a feature to our product, despite not having the required developer capacity in-house.

Stephan Ewen

CTO

Their consistent communication and responsiveness enabled GetInData to drive the project forward. They possess comprehensive knowledge of the relevant technologies and have an intuitive understanding of business needs and requirements. Customers can expect a partner that is open to feedback.

Wilson Yu Cao

Development Team Manager

We sincerely recommend GetInData as a Big Data training provider! The trainer is a very experienced practitioner and he gave us a lot of tips regarding production deployments, possible issues as well as good practices that are invaluable for a Hadoop administrator.

Mariusz Popko

Platform Manager

The engineers and administrators at GetInData are world-class experts. They have proven experience in many open-source technologies such as Hadoop, Spark, Kafka and Flink for implementing batch and real-time pipelines.

Kostas Tzoumas

CEO

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