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.

Transferring files between two or more machines is an essential part of the ETL (extract, transform, load) process. Of course, there are multiple ways to move data, including flat file databases. For example, you can physically copy the data onto a USB drive or send it to the recipient via email. But methods like these are far less efficient than sending data via FTP. So what is FTP exactly, and how do you use it to transfer files and data? Keep reading for all the answers.

ELT is a three-step process that first extracts raw, structured, and unstructured data from source databases, applications, data stores, and other repositories. It then loads that data into a data lake and transforms it as needed by analysts. Since it doesn't move the data to an intermediate staging area or transform it before loading, the extraction process is speedy. You don’t need to pick and choose what data loads into the data lake or wait for it to be processed.

Use these criteria to choose the best ETL tool for your data integration needs.

What do Xplenty, Snowflake software, and dbt (data build tool) have in common? When used together, they merge the best of ETL (extract, transform, load) and ELT (extract, load, transform) into a powerful, flexible and cost-effective ETLT (extract, transform, load, transform) strategy. In this guide, we’ll show you how to create an ETLT strategy with Xplenty, Snowflake software, and dbt. But first, we’ll explain why you'd want to use this strategy to build an ETLT data transformation stack.

How do data integration architectures work, and how are they continuing to evolve?

ETL tools and Heroku Connect both offer bidirectional data connections to Salesforce. So it would be natural to assume that you only need one or the other for your Salesforce integration. But, in fact, each tool has its own particular strengths that make the two systems complementary. Heroku is a software development platform and cloud service provider that empowers developers who build, deploy and scale web applications.

Salesforce is the world's #1 CRM (customer relationship management) platform. The service provides access to valuable data by logging and collecting customer interactions, regardless of the channel in which they take place. Whether it gets the information from phone calls, website transactions, or social media posts, Salesforce delivers customer data in real-time so business owners can gain essential insights.

Extract, Load, Transform, or ELT, is a process that extracts data from the source, loads it directly into a data warehouse or data lake, and then transforms it to make it available for business intelligence tools. It supports all data types, from raw to structured. ELT is a popular way to ingest large volumes of raw data quickly, but it brings many security concerns with it.

TL;DR: Yes, you can do it. And no, you don’t need a separate tool for it. “Reverse ETL” is a fairly recent addition to the data engineer’s dictionary. While you can read articles upon articles about it (there’s a pretty good ‘primer’ in the Memory Leak blog), it can be summarized as being the art and science of taking data from your data warehouse and sending it somewhere other than BI - generally into other tools and systems where it becomes operational.