Pickl AI

OCTOBER 17, 2024

Introduction ETL plays a crucial role in Data Management. This process enables organisations to gather data from various sources, transform it into a usable format, and load it into data warehouses or databases for analysis. Loading The transformed data is loaded into the target destination, such as a data warehouse.

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ETL Data Warehouse Data Quality Data Governance 52

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Precisely

JULY 18, 2024

Without access to all critical and relevant data, the data that emerges from a data fabric will have gaps that delay business insights required to innovate, mitigate risk, or improve operational efficiencies. You must be able to continuously catalog, profile, and identify the most frequently used data.

Data Lakes 52

Data Lakes Data Warehouse Data Governance Machine Learning 52

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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AWS Database ETL AI 93

Alation

FEBRUARY 13, 2020

Important evaluation features include capabilities to preview a dataset, see all associated metadata, see user ratings, read user reviews and curator annotations, and view data quality information.

Data Lakes 64

Data Lakes Data Analysis Data Analysis Big Data 64

AWS Machine Learning Blog

SEPTEMBER 18, 2024

It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

AWS 112

AWS Machine Learning Machine Learning ML 112

Alation

FEBRUARY 20, 2020

In 2016, people will realize the importance of scaling the generation of insights in parallel with the data – and finally have the ability to manage sprawl and realize new levels of insights from the data. 2016 will be the year of the “logical data warehouse.”

Data Warehouse 52

Data Warehouse Hadoop Data Science ETL 52

Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. The existing Data Catalog becomes the Default catalog (identified by the AWS account number) and is readily available in SageMaker Lakehouse.

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SQL Data Analyst Data Warehouse AWS 137

Alation

FEBRUARY 20, 2020

We recently announced an integration with Trifacta to seamlessly integrate the Alation Data Catalog with self-service data prep applications to help you solve this issue. Bringing best of breed self-service data preparation together with data cataloging is a natural combination.

Data Lakes 40

Data Lakes ETL Data Analyst Data Preparation 40