Dataconomy

MARCH 26, 2025

ETL pipelines consist of three main phases: extraction, transformation, and loading. These stages ensure that data flows smoothly from its source to its final destination, typically a data warehouse or a business intelligence tool.

ETL 91

ETL Data Pipeline Business Intelligence Business Intelligence 91

The MLOps Blog

MAY 17, 2023

We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one. What is an ETL data pipeline in ML? Xoriant It is common to use ETL data pipeline and data pipeline interchangeably.

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ETL Data Pipeline ML ML 59

ODSC - Open Data Science

FEBRUARY 24, 2023

Great Expectations provides support for different data backends such as flat file formats, SQL databases, Pandas dataframes and Sparks, and comes with built-in notification and data documentation functionality. You can watch it on demand here.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. What does a modern data architecture do for your business?

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

phData

JANUARY 10, 2024

Imagine you wanted to build a dbt project for your existing source data warehouse in your migration to Snowflake. You could leverage the data source tool to profile your source, apply a template against the generated metadata, and automatically create a dbt project with models for each table!

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Data Warehouse Data Profiling Data Pipeline Database 52