Place the five steps of the ETL process in order
From extracting, transforming and loading basics to architecture and transformation and automation. Show
The Extract, Transform, and Load process (ETL for short) is a set of procedures in the data pipeline. It collects raw data from its sources (extracts), cleans and aggregates data (transforms) and saves the data to a database or data warehouse (loads), where it is ready to be analyzed. In this blog, we are going to guide you through engineering best practices and cover all the steps of setting up a successful ETL process:
1. Extract explainedThe “Extract” stage of the ETL process involves collecting structured and unstructured data from its data sources. This data will ultimately lead to a consolidated single data repository. Traditionally, extraction meant getting data from Excel files and Relational Management Database Systems, as these were the primary sources of information for businesses (e.g. purchase orders written in Excel). With the increase in Software as a Service (SaaS) applications, the majority of businesses now find valuable information in the apps themselves, e.g. Facebook for advertising performance, Google Analytics for website utilization, Salesforce for sales activities, etc. Extracted data may come in several formats, such as relational databases, XML, JSON, and others, and from a wide range of data sources, including cloud, hybrid, and on-premise environments, CRM systems, data storage platforms, analytic tools, etc. But today, data extraction is mostly about obtaining information from an app’s storage via APIs or webhooks. Now let’s look at the three possible architecture designs for the extract process. 1.1. Extract architecture designWhen designing the software architecture for extracting data, there are 3 possible approaches to implementing the core solution:
Below are the pros and cons of each architecture design, so that you can better understand the trade-offs of each ETL process design choice: 1.2. Extract challengesThe data extraction part of the ETL process poses several challenges. Knowing them can help you prepare for and avoid the issues before they arise.
With the increasing dependency on third-party apps for doing business, the extraction process must address several API challenges as well:
Pro tip to skip the API challenges: Automate data collection from third-party apps or databases with Keboola’s Extractors, hundreds of ready-to-use integrations and Generic Extractor - this component can be configured to extract data from virtually any sane web API. Create a free account, go to Flows, and select data sources from which you want to pull your data from. Follow the instructions in our demo for the next steps! 2. Transform explainedThe “Transform” stage of the ETL process takes the data that has been collected at the extractor stage and changes (transforms) it before saving it to the analytic database. There are multiple transformations:
In reality, though, the majority of work is done via data cleaning. If you would like to dig deeper into the intricacies of data cleansing, check out The Ultimate Guide to Data Cleaning. 2.1. Transform architecture designWhen designing the architecture of data transformation, there are multiple things to consider:
2.2. Transform challengesThere are several challenges when dealing with transformations:
Pro tip: Keboola helps you automate any data transformation process: clean data, aggregate it, compute metrics, remove outliers, and many more.Create your ETL pipeline today, and let us sweat the details of your ETL process. 3. Load explained“Load” involves taking data from the transform stage and saving it to a target database (relational database, SQL, NoSQL data store, data warehouse, or data lake), where it is ready for big data analysis. 3.1. Load architecture designThere are three possible designs for architecting data being loaded into a destination warehouse/database or other target systems: Full load, or incremental load (batch and stream). Here, we explore them alongside
their pros and cons: 3.2. Load challengesThere are several challenges in the loading stage of the data pipeline:
The best way to reduce errors, save time and overcome the challenges of the ETL process is to automate all the steps using an ETL tool. Use them to set up your ETL process once and reuse it forever. Speaking of forever… Keboola offers a forever-free no-questions-asked account you might want to try out if you are building an ETL pipeline. With hundreds of components available and drag’n’drop capabilities, you will build a data flow in minutes. Create a free account and try it for yourself. 4. ETL vs ELTWithin the ETL (traditional process), data is extracted to the staging area (either in-memory data structures or temporary databases) before it is transformed and loaded into the analytic (OLAP) database for analysis. ELT (extract-load-transform) takes advantage of the new data warehousing technologies (e.g. BigQuery, Amazon Redshift, Snowflake…) by loading the raw data into a data warehouse or data lake first and transforming the data on the fly when it is needed for analysis. ELT is preferred for operations working with extremely large volumes of data or with real-time data. To put it another way. The main conceptual difference is the final step of the process: in ETL, clean data is loaded in the target destination store. In ELT, loading data happens before transformations - the final step is transforming the data just before data analysis. Even though the end result is the same (i.e. data is ready for analysis), there is a tradeoff between ETL and ELT, which needs to be made clear: Regardless of your preference (ETL or ELT), there are several architectural considerations to keep in mind. 5. Benefits a well engineered ETL processA thought-out ETL process can drive true business value and benefits such as:
6. Which ETL tool should you choose?There are plenty of ETL tools which automate, accelerate, and take care of your ETL processes for you. What makes them valuable is that they:
Picking the right tool for your company is a whole new chapter in your ETL process journey, and you can choose your adventure by either:
But why not take a shortcut and check out a solution that covers all the steps of your ETL process and then some! 6. Automate your ETL process and all your data operationsKeboola is a holistic data platform as a service built with ETL process automation in mind. The top functionalities that will eliminate manual work (and errors) of setting up your ETL pipeline and data integration strategy are:
Watch this tutorial to see just how easy it is to build ETL pipelines: Going beyond the classic ETL process, Keboola features out-of-the-box solutions for a variety of data needs and will enable you to:
Keboola is designed to speed up and automate all your data processes so you can finally stop working on your data infrastructure, and start using it instead. Set up your first ETL process today in just a couple of clicks. For free. Try Keboola today. What is ETL explain the steps of ETL?ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.
What is the first stage of the ETL process?The first step of the ETL process is extraction. In this step, data from various source systems is extracted which can be in various formats like relational databases, No SQL, XML, and flat files into the staging area.
What are the steps of ETL process and why is it important for data warehousing?ETL (or Extract, Transform, Load) is a process of data integration that encompasses three steps — extraction, transformation, and loading. In a nutshell, ETL systems take large volumes of raw data from multiple sources, converts it for analysis, and loads that data into your warehouse.
What does an ETL process require?Extract, transform, and load (ETL) process
The data transformation that takes place usually involves various operations, such as filtering, sorting, aggregating, joining data, cleaning data, deduplicating, and validating data. Often, the three ETL phases are run in parallel to save time.
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