François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. How can we interoperate between the data domains ? How do we govern all these data products and domains ? It will be illustrated with our technical choices and the services we are using in the Google Cloud Platform.

Architecture 130

Architecture Technology Google Cloud Metadata 130

Data Engineering Podcast

JULY 17, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Data Management 130

Data Management Management Metadata MongoDB 130

Hepta Analytics

FEBRUARY 14, 2022

This week, we got to think about our data ingestion design. We looked at the following: How do we ingest – ETL vs ELT Where do we store the data – Data lake vs data warehouse Which tool to we use to ingest – cronjob vs workflow engine NOTE : This weeks task requires good internet speed and good compute.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Monte Carlo

JUNE 14, 2023

The modern data stack era , roughly 2017 to present data, saw the widespread adoption of cloud computing and modern data repositories that decoupled storage from compute such as data warehouses, data lakes, and data lakehouses.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Monte Carlo

SEPTEMBER 20, 2022

Here’s how Prefect , Series B startup and creator of the popular data orchestration tool, harnessed the power of data observability to preserve headcount, improve data quality and reduce time to detection and resolution for data incidents. But a growing company means growing data needs. Image courtesy of Prefect.

Big Data 52

Big Data Data Warehouse Data Data Governance 52

AltexSoft

NOVEMBER 7, 2022

DevOps tasks — for example, creating scheduled backups and restoring data from them. Airflow is especially useful for orchestrating Big Data workflows. Airflow is not a data processing tool by itself but rather an instrument to manage multiple components of data processing. Metadata database.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

Databand.ai

DECEMBER 29, 2022

In his current role as Senior Director of Product Management at Google, he focuses on BigQuery, Cloud Dataflow, Cloud DataProc, Cloud DataPrep, Cloud PubSub, and Cloud Composer. She also posts frequently on LinkedIn about data analytics, data strategy, data governance, and data engineering.

BI 52

BI Consulting Data Science Data Governance 52