Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Central to this transformation are two shifts. Let’s take a closer look.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Monte Carlo

FEBRUARY 15, 2023

Since the inception of the cloud, there has been a massive push to store any and all data. On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Meltano

OCTOBER 5, 2022

Automation Automation is an essential factor in data management, as it helps save both time and money while increasing efficiency and reducing errors. Meltano enables the automation of data delivery from various sources at the same time. Testing Data Quality Untested and undocumented data can result in unstable data and pipeline debt.

Data Engineering 52

Data Engineering Data Engineer Engineering Raw Data 52

Edureka

FEBRUARY 9, 2023

Certified Azure Data Engineers are frequently hired by businesses to convert unstructured data into useful, structured data that data analysts and data scientists can use. Microsoft Azure is a modern cloud platform that provides a wide range of services to businesses.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

ProjectPro

JANUARY 19, 2022

Companies frequently hire certified Azure Data Engineers to convert unstructured data into useful, structured data that data analysts and data scientists can use. Data infrastructure, data warehousing, data mining, data modeling, etc.,

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

ProjectPro

NOVEMBER 30, 2021

ETL (Extract, Transform, and Load) Pipeline involves data extraction from multiple sources like transaction databases, APIs, or other business systems, transforming it, and loading it into a cloud-hosted database or a cloud data warehouse for deeper analytics and business intelligence.

Process 52

Process Data Pipeline Data Warehouse AWS 52

ProjectPro

DECEMBER 7, 2021

It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. ADF does not store any data on its own.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52