# Analytics Pipeline Published articles for Analytics Pipeline. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Good Data For Bad Golf DevFeed: [Good Data For Bad Golf]() Original publisher: [Read original article]() Author: jgamblin Published: 2026-05-13T14:05:06Z Content type: article Language: en Sources: [Jerry Gamblin]() Topics: [Python](), [CSV](), [GitHub Pages](), [GitHub Actions](), [Self-hosted]() Tags: [analytics-pipeline](), [csv](), [github-actions](), [github-pages](), [pipeline](), [python](), [self-hosted](), [uncategorized]() ### AI overview The article presents jgamblin/golf, a self-hosted Python analytics pipeline that converts Garmin Golf app CSV exports into a multi-page GitHub Pages dashboard. It describes features including session analysis, club-level consistency, session replay, gapping, coaching recommendations, and data-quality tracking, with deployment automated through GitHub Actions. ### Source excerpt I play golf. I am not good at golf. But I have a Garmin Approach R10 launch monitor, a Python interpreter, and too much free time, so naturally I spent way more time building a dashboard to analyze my swing data than I did actually swinging a club. The result is jgamblin/golf, a self-hosted analytics ... Read more ## Why Flock Safety turned to ClickHouse for real-time vehicle traffic analytics DevFeed: [Why Flock Safety turned to ClickHouse for real-time vehicle traffic analytics]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-04-29T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [data-engineering](), [Amazon Redshift](), [Architecture & Design](), [data]() Tags: [amazon-redshift](), [analytics](), [analytics-pipeline](), [clickhouse](), [dashboards](), [data-architecture](), [data-engineering](), [real-time]() ### AI overview This developer case study explains why Flock Safety moved its vehicle traffic analytics platform from an Amazon Redshift, DBT, Prefect, and QuickSight SPICE pipeline to ClickHouse Cloud. The former architecture refreshed data daily, required up to four hours to process some datasets, and made data unavailable during refreshes. ClickHouse enabled real-time analytics for camera traffic without the previous size constraints or row-level security limitations. ### Source excerpt "With ClickHouse, our customers now have real-time analytics for their camera traffic, and there are no more constraints on size or row-level security." ~ Leon Kozlowski, Data Engineering Manager ## Continuous Deployment for AWS Glue DevFeed: [Continuous Deployment for AWS Glue]() Original publisher: [Read original article]() Author: Mohammed Published: 2020-10-15T15:51:25Z Content type: tutorial Language: en Sources: [Brave New Geek]() Topics: [AWS Glue](), [Continuous Deployment (CD)](), [GitHub Actions](), [Jupyter Notebook](), [Amazon S3]() Tags: [analytics](), [analytics-pipeline](), [aws](), [aws-glue](), [ci-cd](), [continuous-delivery](), [continuous-deployment](), [etl](), [github](), [github-actions](), [jupyter](), [jupyter-notebook](), [s3](), [serverless]() ### AI overview A tutorial for automating continuous deployment of AWS Glue ETL jobs. It uses GitHub Actions to generate a Python script from a Jupyter notebook, copy it to Amazon S3, and update the Glue job to use the new script. ### Source excerpt AWS Glue is a managed service for building ETL (Extract-Transform-Load) jobs. It's a useful tool for implementing analytics pipelines in AWS without having to manage server infrastructure. Jobs are implemented using Apache Spark and, with the help of Development Endpoints, can be built using Jupyter notebooks. This makes it reasonably easy to write ETL processes in an interactive, iterative fashion. Once finished, the Jupyter notebook is converted into a Python script, uploaded to S3, and then run as a Glue job. ## Implementing ETL on GCP DevFeed: [Implementing ETL on GCP]() Original publisher: [Read original article]() Author: Deepmala Published: 2020-07-15T20:53:17Z Content type: tutorial Language: en Sources: [Brave New Geek]() Topics: [DataOps](), [Cloud](), [BigQuery](), [data loss prevention](), [Low code](), [No-code](), [olap]() Tags: [analytics](), [analytics-pipeline](), [bi-tools](), [bigquery](), [cdap](), [cloud-data-loss-prevention](), [cloud-dataflow](), [cloud-dataprep](), [cloud-dataproc](), [cloud-pub-sub](), [cloud-storage](), [cloud-tasks](), [data-analytics](), [data-fusion](), [data-lake](), [data-loss-prevention](), [elt](), [etl](), [gcp](), [no-code](), [serverless](), [sql]() ### AI overview A practical guide to building ETL pipelines on Google Cloud Platform using Google-managed services. It explains a two-phase architecture with Cloud Storage as a data lake, Cloud Data Loss Prevention for sensitive-data detection or redaction, and BigQuery as the curated data warehouse, with attention to low-code and no-code approaches. ### Source excerpt ETL (Extract-Transform-Load) processes are an essential component of any data analytics program. This typically involves loading data from disparate sources, transforming or enriching it, and storing the curated data in a data warehouse for consumption by different users or systems. An example of this would be taking customer data from operational databases, joining it with data from Salesforce and Google Analytics, and writing it to an OLAP database or BI engine.