Start Data Engineering
JULY 11, 2020
Intro A very common use case in data engineering is to build a ETL system for a data warehouse, to have data loaded in from multiple separate databases to enable data analysts/scientists to be able to run queries on this data, since the source databases are used by your applications and we do not want these analytic queries to affect our application (..)
Data Engineering Podcast
NOVEMBER 4, 2021
Summary The precursor to widespread adoption of cloud data warehouses was the creation of customer data platforms. Acting as a centralized repository of information about how your customers interact with your organization they drove a wave of analytics about how to improve products based on actual usage data.
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Data Engineering Podcast
JANUARY 7, 2022
If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Brian Leonard about Grouparoo, an open source framework for managing your reverse ETL pipelines Interview Introduction How did you get involved in the area of data management?
Grouparoo
OCTOBER 26, 2021
This includes the different possible sources of data such as application APIs, social media, relational databases, IoT device sensors, and data lakes. This may include a data warehouse when it’s necessary to pipeline data from your warehouse to various destinations as in the case of a reverse ETL pipeline.
Ascend.io
DECEMBER 21, 2022
The last three years have seen a remarkable change in data infrastructure. ETL changed towards ELT. Now, data teams are embracing a new approach: reverse ETL. Cloud data warehouses, such as Snowflake and BigQuery, have made it simpler than ever to combine all of your data into one location.
Grouparoo
FEBRUARY 9, 2022
ETL testing is also used to verify that the ETL process runs smoothly without any bottlenecks or major performance issues. The testing process is often performed during the initial setup of a data warehouse after new data sources are added to a pipeline and after data integration and migration projects.
ProjectPro
SEPTEMBER 30, 2014
"Hadoop is a key ingredient in allowing LinkedIn to build many of our most computationally difficult features, allowing us to harness our incredible data about the professional world for our users," said Jay Kreps, Principal Engineer, LinkedIn.
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