# elt

Published articles for elt.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Implementing ETL on GCP

DevFeed: [Implementing ETL on GCP](<https://devfeed.tech/articles/implementing-etl-on-gcp-22998.md>)

Original publisher: [Read original article](<https://bravenewgeek.com/implementing-etl-on-gcp/>)

Author: Deepmala

Published: 2020-07-15T20:53:17Z

Content type: tutorial

Language: en

Sources: [Brave New Geek](<https://devfeed.tech/sources/brave-new-geek.md>)

Topics: [DataOps](<https://devfeed.tech/topics/dataops.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [BigQuery](<https://devfeed.tech/topics/bigquery.md>), [data loss prevention](<https://devfeed.tech/topics/data-loss-prevention.md>), [Low code](<https://devfeed.tech/topics/low-code.md>), [No-code](<https://devfeed.tech/topics/no-code.md>), [olap](<https://devfeed.tech/topics/olap.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [analytics-pipeline](<https://devfeed.tech/tags/analytics-pipeline.md>), [bi-tools](<https://devfeed.tech/tags/bi-tools.md>), [bigquery](<https://devfeed.tech/tags/bigquery.md>), [cdap](<https://devfeed.tech/tags/cdap.md>), [cloud-data-loss-prevention](<https://devfeed.tech/tags/cloud-data-loss-prevention.md>), [cloud-dataflow](<https://devfeed.tech/tags/cloud-dataflow.md>), [cloud-dataprep](<https://devfeed.tech/tags/cloud-dataprep.md>), [cloud-dataproc](<https://devfeed.tech/tags/cloud-dataproc.md>), [cloud-pub-sub](<https://devfeed.tech/tags/cloud-pub-sub.md>), [cloud-storage](<https://devfeed.tech/tags/cloud-storage.md>), [cloud-tasks](<https://devfeed.tech/tags/cloud-tasks.md>), [data-analytics](<https://devfeed.tech/tags/data-analytics.md>), [data-fusion](<https://devfeed.tech/tags/data-fusion.md>), [data-lake](<https://devfeed.tech/tags/data-lake.md>), [data-loss-prevention](<https://devfeed.tech/tags/data-loss-prevention.md>), [elt](<https://devfeed.tech/tags/elt.md>), [etl](<https://devfeed.tech/tags/etl.md>), [gcp](<https://devfeed.tech/tags/gcp.md>), [no-code](<https://devfeed.tech/tags/no-code.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [sql](<https://devfeed.tech/tags/sql.md>)

### 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.

## Regular Expressions and Grouping Sets

DevFeed: [Regular Expressions and Grouping Sets](<https://devfeed.tech/articles/regular-expressions-and-grouping-sets-34563.md>)

Original publisher: [Read original article](<https://tapoueh.org/blog/2017/08/regular-expressions-and-grouping-sets/>)

Author: Dimitri Fontaine PostgreSQL Major Contributor; Author

Published: 2017-08-14T14:37:53Z

Content type: tutorial

Language: en

Sources: [Dimitri Fontaine](<https://devfeed.tech/sources/dimitri-fontaine.md>)

Topics: [PostgreSQL](<https://devfeed.tech/topics/postgresql.md>), [Regular expression](<https://devfeed.tech/topics/regular-expression.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [data-processing](<https://devfeed.tech/topics/data-processing.md>), [Open Data](<https://devfeed.tech/topics/open-data.md>), [Structured-data](<https://devfeed.tech/topics/structured-data.md>), [CSV](<https://devfeed.tech/topics/csv.md>), [elt](<https://devfeed.tech/topics/elt.md>), [etl](<https://devfeed.tech/topics/etl.md>)

Tags: [array](<https://devfeed.tech/tags/array.md>), [csv](<https://devfeed.tech/tags/csv.md>), [data-processing](<https://devfeed.tech/tags/data-processing.md>), [elt](<https://devfeed.tech/tags/elt.md>), [etl](<https://devfeed.tech/tags/etl.md>), [functions](<https://devfeed.tech/tags/functions.md>), [postgresql](<https://devfeed.tech/tags/postgresql.md>), [regexp](<https://devfeed.tech/tags/regexp.md>), [regular-expressions](<https://devfeed.tech/tags/regular-expressions.md>), [sql](<https://devfeed.tech/tags/sql.md>), [table](<https://devfeed.tech/tags/table.md>)

### AI overview

A PostgreSQL tutorial showing how to split hierarchical, comma-separated values from a messy column with regular-expression functions, normalize the results, and analyze category distributions with grouping sets, CUBE, and ROLLUP. It also contrasts ETL with ELT.

### Source excerpt

There's a very rich set of PostgreSQL functions to process text, you can find them all at the String Functions and Operators documentation chapter, with functions such as overlay, substring, position or trim. Or aggregates such as string_agg. And then regular expression functions, including the very powerful regexp_split_to_table.