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3 Reasons Extract, Load & Transform is a Bad Idea

Extract, Load, Transform (ELT) technology makes it easy for organizations to pull data from databases, applications, and other sources, and move it into a data lake. But companies pay for this convenience in many ways. ELT solutions can have a negative impact on data privacy, data quality, and data management.

ETL with Apache Airflow

Written in Python, Apache Airflow is an open-source workflow manager used to develop, schedule, and monitor workflows. Created by Airbnb, Apache Airflow is now being widely adopted by many large companies, including Google and Slack. Being a workflow management framework, Apache Airflow differs from other frameworks in that it does not require exact parent-child relationships. Instead, you only need to define parents between data flows, automatically organizing them into a DAG (directed acyclic graph).

Create a Salesforce ETL Pipeline in 30 Minutes

Salesforce is one of the world’s most popular CRM (customer relationship management) software platforms, helping businesses of all sizes and industries beat their competitors and better serve their clients. But instead of keeping your Salesforce data inside the CRM platform itself, you can make better use of this information by moving it into a target data warehouse.

ELT: Easy to Deploy, Easy to Outgrow

Extract, load, transform (ELT) technology is a type of data pipeline that ingests data from one or more sources, loads the data into its destination (typically a data lake), and then allows end-users to perform ad-hoc transformations on it as needed. ELT can perform mass extraction of all data types, including raw data, without the need to set up transformation rules and filters before data loading.