The document discusses the evolution of data analytics infrastructure, emphasizing a modern ELT approach with tools like Fivetran, Databricks SQL Service, and dbt. It highlights the separation of storage and compute, allowing for agile data pipelines and democratizing data access for various team members. By moving the data transformation step into the warehouse, companies can leverage automation and best practices for efficient data modeling.

1. Speeding Time to Insight with a
Modern ELT Approach:
Fivetran + Databricks SQL Service + dbt

2. Introductions
Sean Spediacci
Fivetran, Sr. Product Marketer
Amy Deora
Fishtown Analytics (keepers of dbt), Head of
Alliances

3. In 2010, the focus of many analytics teams was on
infrastructure, compute and storage for large data sets
● “How do we build / scale our ETL
infrastructure?”
● “How will we control storage
costs?”
● “How will we design a data
warehouse that is performant?”
“Big Data”

4. The landscape has shifted
● Constraining compute (On-
premises)
● Perpetual licensing
● ETL outside the database
● Separation of storage +
compute (on-premise)
● Subscription licensing
● Complex pipelines required
for large datasets
Single Node Databases Scalable Databases
● Auto scale cloud DBMS
● Usage based licensing
● Separation of storage and
compute allows for agile
data pipelines
Modern Cloud
Destinations
Pipeline architecture is adapting to the modern cloud technology.

5. ETL ELT
Sources
Transformation (Modeling)
Process
Raw DataExtraction Process Clean, Usable Datasets
Moving Data Into the Warehouse (EL) is a
highly automatable process.
Data Transformation is different for
every company - it cannot be fully
automated.

6. The Modern Data Stack

7. Moving the data transformation step to the
warehouse has another benefit - democratizing
access to data.
The data transformation step is now more accessible to more
members of the data team - from BI developers to data
scientists.
No more waiting for an ETL process that happens out of sight.

8. ➔Automatic Data
Updates (DML)
➔Automatic Schema
Migrations (DDL)
➔Automated Recovery
from Failure
(Idempotent)
➔Micro-batched
architecture
➔Extensible Cloud
Functions
About Fivetran

9. About dbt
dbt is an open source transform tool that allows
anyone comfortable with SQL to author their own data
pipelines

10. ● Users write SQL with “super powers” from python (e.g., loops, macros,
local variables).
● Wraps the right DDL, DML around your SQL to materialize data
models in any warehouse.
● Infers the data lineage graph (DAG) as you code.
● Supports multiple environments, and git-based version control.
● Integrates testing into your pipeline.
● Automates documentation
dbt allows data engineers/analysts to work like
software developers

11. The modern data stack - with Fivetran, Delta Lake,
Databricks SQL Service and dbt
● Fivetran automates the integration with operational
systems with zero configuration required
● dbt provides a flexible data modeling environment with
best practices from DevOps.
● Connecting directly to the new databricks SQL service
makes building your pipeline fast and easy.

12. 1. Ingest data via a Fivetran automatic connector.
2. Connect dbt to start transforming your data in-
warehouse.
3. Use a a Fivetran dbt package to jump-start your
modeling process.
Speeding time to insight with the modern stack

13. Demo: Instant Insight from github data

14. Feedback
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15. Color Palette
Primary
Colors

16. Bringing it all together

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19. Code example

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21. Attribution Format
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22. Databricks simplifies data and AI
so data teams can innovate faster

23. Logos

24. Databricks Logos

25. Open Source Logos