Ascend.io

DECEMBER 19, 2023

We’ll talk about when and why ETL becomes essential in your Snowflake journey and walk you through the process of choosing the right ETL tool. Our focus is to make your decision-making process smoother, helping you understand how to best integrate ETL into your data strategy. But first, a disclaimer.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

Knowledge Hut

MARCH 28, 2024

Implement ETL processes to load data into the data warehouse from various source systems. Education & Skills Required Proficiency in SQL and experience with database development, preferably with Azure SQL Database or SQL Data Warehouse. Familiarity with ETL tools and techniques for data integration.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Data Engineering Weekly

DECEMBER 29, 2022

Data catalogs are the most expensive data integration systems you never intended to build. Data Catalog as a passive web portal to display metadata requires significant rethinking to adopt modern data workflow, not just adding “modern” in its prefix. How happy are you with your data catalogs?

Metadata 62

Metadata Data Warehouse ETL Tools Data Workflow 62

Knowledge Hut

NOVEMBER 17, 2023

Cloud Platform Skills A strong grasp of Microsoft Azure, covering a spectrum of services for seamless deployment, scaling, and management of data solutions, leveraging the power of the cloud. Data Integration and ETL Tools As an Azure Data Engineer, master data integration and ETL tools crucial for seamless data processing.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Ascend.io

MAY 31, 2023

Data Ingestion Data ingestion is the first step of both ETL and data pipelines. In the ETL world, this is called data extraction, reflecting the initial effort to pull data out of source systems. The data sources themselves are not built to perform analytics.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

Meltano

OCTOBER 5, 2022

Meltano is based on the extract, load, transform (ELT) principle to simplify the extraction of data from various sources and load it into data warehouses or databases, using Singer taps and targets. Data Orchestration Data orchestration allows businesses to streamline and automate the way they derive insights from data.

Data Engineering 52

Data Engineering Data Engineer Engineering Raw Data 52

Meltano

OCTOBER 5, 2022

In the same way, a DataOps engineer designs the data assembly line that enables data scientists to derive insights from data analytics faster and with fewer errors. DataOps engineers improve the speed and quality of the data development process by applying DevOps principles to data workflow, known as DataOps.

Engineering 52

Engineering Raw Data Data Pipeline ETL Tools 52