Pickl AI

JULY 8, 2024

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Dataconomy

MAY 16, 2023

With their technical expertise and proficiency in programming and engineering, they bridge the gap between data science and software engineering. By recognizing these key differences, organizations can effectively allocate resources, form collaborative teams, and create synergies between machine learning engineers and data scientists.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

Tableau

OCTOBER 8, 2021

With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics. Connecting directly to this semantic layer will help give customers access to critical business data in a safe, governed manner. Direct connection to Google BigQuery.

Tableau 137

Tableau Analytics Analytics Machine Learning 137

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Tableau

OCTOBER 8, 2021

With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics. Connecting directly to this semantic layer will help give customers access to critical business data in a safe, governed manner. Direct connection to Google BigQuery.

Tableau 98

Tableau Analytics Analytics Machine Learning 98

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Machine Learning Machine Learning 52

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Data Scientist Machine Learning 52