enables, empowers
a data analytics company
Why Migrate Your Data Platform?- Outgrow Limitations:
Escape the performance bottlenecks, high costs, and scalability issues of legacy on-premise or outdated cloud systems.- Reduce Total Cost of Ownership (TCO):
Eliminate expensive hardware maintenance and restrictive licensing fees by moving to a flexible, pay-as-you-go cloud model.- Unlock Advanced Analytics:
Position your business for the future by enabling powerful AI, machine learning (ML), and real-time analytics capabilities.- Enhance Security & Governance:
Leverage state-of-the-art cloud security, automated compliance, and centralized data governance features.- Democratize Your Data:
Empower your teams with self-service analytics and faster access to reliable data, fostering a data-driven culture.
from legacy to modern data platforms
dlt
and dbt
dlt
(Data Load Tool)
- dbt
(Data Build Tool).Together, they form the backbone of a flexible and scalable ELT (Extract, Load, Transform) architecture.
elt = lambda: ('dlt', 'dbt')
is the formula for modern data stack
What are dlt and dbt?
Think of building a data pipeline as a two-step process:
- first, you move the raw materials.
- second, you manufacture the finished product.dlt (Data Load Tool):
dlt is a Python library that handles the "EL" (Extract, Load) in ELT.It's the foundation: dlt delivers the raw data, perfectly staged and ready for the next step.dbt (Data Build Tool):
dbt takes over once the data has landed. It is the definitive tool for the "T" (Transform) in ELT.It doesn't move data; it refines, models, and tests the raw data that dlt has delivered, turning it into clean, reliable, and business-ready datasets.
The Analytics Development Lifecycle (ADLC)
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