Data Engineering Podcast

NOVEMBER 23, 2020

Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. How much time could you save if those tasks were automated across your cloud platforms?

Hadoop 100

Hadoop Data Pipeline BI Data 100

Christophe Blefari

MAY 12, 2023

As the same time in Paris we organised last Tuesday the May Airflow meetup. I really liked Benoit and Samy presentation about Cloud Composer —Managed Airflow on GCP. Actually OpenAI deal with Microsoft was probably the best deal they could have go for. Please read it twice before running it blindly.

Data 130

Data Data Storage SQL Coding 130

Data Engineering Podcast

APRIL 24, 2022

If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. By the time errors have made their way into production, it’s often too late and damage is done. Struggling with broken pipelines?

Data Warehouse 100

Data Warehouse Data Lake Data Pipeline BI 100

ProjectPro

NOVEMBER 15, 2021

Apache Beam Source: Google Cloud Platform Apache Beam is an advanced unified programming open-source model launched in 2016. To execute pipelines, beam supports numerous distributed processing back-ends, including Apache Flink, Apache Spark , Apache Samza, Hazelcast Jet, Google Cloud Dataflow, etc.

Big Data 52

Big Data Project Metadata Programming Language 52

AltexSoft

JULY 18, 2023

To some, the word Apache may bring images of Native American tribes celebrated for their tenacity and adaptability. These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. What is Apache Spark? Apache Spark components.

Big Data 52

Big Data Data Process Process Hadoop 52

ProjectPro

JANUARY 31, 2022

Data scientists usually invest up to 80% of their time seeking, extracting, merging, filtering, and preparing data. Developing new predictive features can be- difficult and time-consuming, requiring domain knowledge, demanding familiarity with each model's specific requirements, etc.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52