ETL

What Is ETL?

Extract, transform, load — better known as ETL — is a data integration process used to copy, combine,
and convert data from different sources and formats and load it into a new destination such as a data warehouse or
data lake. Once it’s there, the data can be analyzed to help drive business decisions.

ELT — extract, load,
transform — is similar, but data is transformed after it’s loaded to the new destination.

Why Is ETL Important?

ETL’s ability to extract and integrate data from a variety of source systems — including customer, geospatial,
and demographic data — means less of a burden on IT and more opportunity for self-service analytics.

ETL is a vital part of any
data management strategy and is often used to migrate data in the case of an acquisition or system upgrade. While it
allows businesses to react quickly, it also provides a historical view that puts data into context.

How ETL Works

ETL is an easy, accessible, and automated way to aggregate diverse data, whether in different formats or from
different systems or data sources, and make it analysis-ready.

A key part of the process,
data governance, outlines the policies and procedures surrounding data handling. This includes infrastructure and
technology as well as the people responsible for overseeing the entire process. Data governance is crucial for
businesses because it allows for more reliable data; reduced costs; a single source of truth; and regulatory, legal,
and industry compliance.

Extract: Automated data extraction improves efficiency and provides valuable insights faster. During the extraction process, structured and unstructured data is pulled from multiple sources and likely in multiple formats (JSON, XML, non-relational databases, scraped websites, etc.). Before pulling the data, validate its accuracy and quality to ensure any analysis that follows is sound; this is especially important when dealing with legacy systems and outside data.

 

Transform: Data transformation brings together data of different formats and stores it in required formats so it can be used across an organization. For it to be successful, the technical requirements of the target destination and the needs of users need to be considered. This could mean checking what character sets are supported by the system, what type of coding the warehouse uses, or creating a new value relevant to a specific analysis. Data cleansing is another vital step to transformation and includes removing duplicates, unwanted nulls, and whitespaces and modifying data type and size.

 

Load: Loading involves writing transformed data to its storage location, whether a data warehouse or a data lake, on premises or in the cloud. With a recurring ETL process, such as storing new employee details, businesses can choose to overwrite existing information or append new data with a timestamp. Once data is loaded, make sure all data was migrated and check for errors to verify the data quality.

The Future of ETL

Traditional ETL tools, reliant on SQL, manual coding, and IT
experts, result in a rigid, siloed environment that prevents speed and efficiency. As business needs change, data
— and the ability to analyze it quickly and accurately — is more important than ever. Modern ETL
programs allow for analytics automation, a more efficient way to transform raw data from different
sources into valuable insights that drive decisions.

Getting Started With ETL

A finely-tuned ETL program can allow for faster, more educated decision-making. Alteryx Analytics Automation makes
the ETL process easy, auditable, and efficient, and its low-code, no-code, drag-and-drop interface means anyone can
use it.

The flexibility of the Alteryx Platform allows businesses to:

• Extract data from multiple sources such as Snowflake, Tableau, Azure, and AWS using the Input Data Tool or prebuilt connectors. The open API also allows users to build their own API connections.
• Transform messy, disparate data using a suite of drag-and-drop automation tools such as Filter, Data Cleansing, and Summarize
• Receive powerful predictive, spatial, and statistical analytics
• Load data to its target destination using the Output Data or Write Data IN-DB Tools, a process that can be easily reproduced.