Loading Data from mux
to clickhouse
using Python dlt
Library
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This documentation provides a technical guide on how to load data from Mux
to ClickHouse
using the open-source Python library, dlt
. Mux
is a robust solution that simplifies the complex tasks faced by software teams when building video platforms, ranging from live-streaming to on-demand video catalogs. On the other hand, ClickHouse
is a high-speed, open-source, column-oriented database management system that enables real-time generation of analytical data reports using SQL queries. Leveraging dlt
, users can efficiently transfer data from Mux
to ClickHouse
. For more information about Mux
, visit https://www.mux.com/.
dlt
Key Features
-
Pipeline Metadata:
dlt
pipelines leverage metadata to provide robust governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Load IDs enable incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read more about lineage. -
Schema Enforcement and Curation:
dlt
empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. Read more: Adjust a schema docs. -
Schema evolution:
dlt
enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, such as table or column alterations,dlt
notifies stakeholders, allowing them to take necessary actions. Read more about schema evolution. -
Scalability via iterators, chunking, and parallelization:
dlt
offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Read more about scalability. -
Implicit extraction DAGs:
dlt
incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. This extraction DAG determines the optimal order for extracting the resources to ensure data consistency and integrity. Read more about implicit extraction DAGs.
Getting started with your pipeline locally
0. Prerequisites
dlt
requires Python 3.8 or higher. Additionally, you need to have the pip
package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.