You have started your data journey. You know you need to somehow collect data from various sources and land them into a data warehouse or data lake of some sort. Right now you’re browsing tools and calculating costs - there’s one for extraction, another one for transformations, there’s an ETL tool. What if we told you there’s a better way?

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.

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).

Data cleansing is a fundamental aspect of knowing what data you retain and using it most effectively. The process of cleansing reduces the potential for error and enhances data reliability. Here's how to get started.

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.

What should you know before getting started with ETL in Heroku? In this article, we provide a comprehensive guide to using Heroku ETL.

Some popular data integration tools compared.

Make sure your data integration solution can connect to critical data sources.