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ELT (Extract, Load, Transform) is a newer method that extracts the data and immediately loads it onto the target or destination system before the data is transformed.

ELT (Extract, Load, Transform) is a newer method that extracts the data and immediately loads it onto the source system before the data is transformed before it's dumped into your warehouse.

Choosing the right ETL tool is critical to your overall data warehouse structure, but you’re not alone in your search. There are a few options depending on their overall ETL needs, data schemas, and operational structure.

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If your business has a data warehouse, you've used ETL (Extract, Transform, Load). Whether loading data from your sales stack into your warehouse or constructing simple pipelines between essential apps, ETL is the lever that unlocks the value of your data warehouse.

But, what is ETL? And what kind of ETL tool should you choose to help you flesh out your ETL experience?

More than 64.2 zettabytes of data will be up for grabs by next year. For businesses, that data represents golden opportunities for growth and success, both immediately and in the future. Businesses tapping into big data are seeing profits jump, particularly as more businesses ramp up in this post-pandemic era.

CEOs have been saying for years that failure to embrace big data will leave companies crippled and at a significant disadvantage. But now, 97% of businesses are investing in big data. Already, companies are using data warehouses at ever-greater numbers, with 54% of organizations already adopting data solutions.

A massive 90% of business leaders say that data literacy is a key component of their company's success and continued growth. They also plan to use the data to make better business decisions about the efficacy of their marketing efforts and what’s going on with their customers.

To leverage big data meaningfully, brands have three core tools:

While the data warehouse is the storage place for all your data, and BI tools serve as the mechanism that consumes the data to give you insights, ETL is the intermediary that pushes all the data from your tech stack and customer tools into the data warehouse for analysis. The ETL phase is where your business will spend a good chunk of its time and energy developing a warehouse solution.

But how does ETL work, and do you need to use an ETL tool to load data from one system to the next successfully? As we explain the importance of ETL and data warehouse, you’ll better understand how to analyze and use your data.

ETL is a data integration process that encompasses three steps — extraction, transformation, and loading. In a nutshell, ETL systems take large volumes of raw data from multiple sources, convert them for analysis, and load that data into your warehouse. Let's cover the three primary ETL steps.

In the first step, extracted data sets come from a source (e.g., Salesforce, Google AdWords, etc.) into a staging area. The staging area is a buffer between the data warehouse and the source data. Since data may come from multiple sources, it's likely in various formats, and directly transferring the data to the warehouse may cause corrupted data. The staging area is used for data cleansing and organization.

A big challenge during data extraction is how your ETL tool handles structured and unstructured data. Those unstructured items (e.g., emails, web pages, etc.) can be difficult to extract without the right tool. You may have to create a custom transfer solution if you choose a tool with poor unstructured data capabilities.

The data cleaning and organization stage is the transformation stage. Data from multiple source systems will be normalized and converted to a single format, improving data quality and compliance.

ETL yields transformed data through these methods:

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Finally, data extracted to a staging area and transformed is loaded into your data warehouse. Depending on your business needs, data can be loaded in batches or all at once. The exact nature of the loading will depend upon the data source, ETL tools, and various other factors.

Extract, Transform, Load (ETL) is the primary, older method of loading data from one system to another, but you may choose to use another method. ELT (or Extract, Load, Transform) is a newer method that extracts the data and immediately loads it onto the source system before the data is transformed.

There are pros and cons to both ETL and ELT. Most use cases will leverage ETL, as not all warehouses are able to support the transformations via ELT.

Data engineers have been using ETL for more than two decades to integrate diverse data types into online analytical processing (OLAP) data warehouses. The reason for doing this is simple: to make data analysis easier.

Typically, business applications use online transactional processing (OLTP) database systems. These are optimized for writing, updating, and editing the information inside them. They’re not good at reading and analysis. However, online analytical processing database systems are excellent at high-speed reading and analysis. ETL must transform OLTP information to work with an OLAP data warehouse.

During the ETL process, information is:

ELT and data lakes take advantage of the high-powered processing offered by modern, cloud-based data warehousing solutions like Snowflake, Google BigQuery, and Redshift. These data warehousing solutions are so powerful that they can perform data transformations on the fly, allowing ELT to skip the staging area and transform only the data you need to analyze at the moment.

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