ETL Process & Best Practices for Data Warehouse
ETL (Extract, Transform, Load) is a data integration method where data is collected from different sources, transformed into usable formats for analysis, and then loaded into a central storage system, like a data warehouse.
ETL pipelines use data warehousing to store processed data. Data engineers can then analyze and interpret it to gain critical insights related to business goals.
A managed cloud data warehouse can simplify data management, streamline data analytics, and lead to faster data-driven business decisions.
In this guide, you will learn how the ETL process works and the key differences between ETL and ELT.
We'll look at the most popular data warehouses for ETL, the best practices to create an efficient ETL pipeline, the benefits of using ETL tools, and three factors to help you pick the right one.
How the ETL process works
The ETL process involves three main stages: extract, transform, and load.
Extract
Data extraction is the first step of data integration. In this stage, data is collected from multiple sources and loaded into a staging area.
A data source is any internal or external system that generates information for your business. This could include a customer-facing app, internal SaaS application, website, ERP solution, CRM software, and many others. It also includes in-house sources like relational databases and flat files.
How much data is collected during the extraction process, when, and where depends on your company's data needs.
ETL tools use connectors to link to source systems and ingest data. Most major ETL tools have pre-built connectors for popular applications. Organizations can connect other data sources, like niche long-tail applications, using custom ETL connectors.
Source data can be structured (like SQL databases or Excel spreadsheets) or unstructured (like emails, web pages, and social media posts). You need ETL connectors that can extract varying data types without hindering data integrity or corrupting the data sets.
Transform
Data transformation is the process of cleaning, normalizing, and organizing raw data for analysis. Extracted data sets from applications and web platforms can be in different formats that are incompatible with analytical tools.
Analytics teams and business intelligence (BI) tools require structured, formatted data to produce accurate insights and generate visualizations. This is where transformation comes in.
Data transformation includes:
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Cleaning
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Sorting
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Filtering
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Joining
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Splitting
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Deduplication
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Summarization
Transformation can involve simple normalization (such as converting units of measurement), data cleansing to remove errors, or complex data validation with multiple rules.
For example, structured data might already include metadata, while semi-structured or unstructured data might need a schema applied.
Data teams typically create custom data models for transformation. These models boost data quality, improving reporting and analysis.
Load
Transformed data is loaded into a target system. This is typically a central data repository, like a data warehouse or data lake, which then share data with analytics and BI tools.
Most modern data stacks use cloud data warehouses for storage because they are scalable, affordable, and managed by the vendor.
Loading involves moving large volumes of data into a target database. For better loading performance, data engineers can decide how data is loaded: in batches or in real-time. This depends entirely on the use case and how fast the organization needs data to be accessible.
To maintain data integrity, the loading process must have recovery mechanisms that restart loading at the point of failure and prevent data corruption or loss.
ETL vs. ELT
ETL has been used for more than 20 years to extract and load data, but modern data integration solutions use ELT or extract, load and transform. In the ELT process, data is extracted and loaded into storage.
Since cloud data stores have larger computing power, they're more effective at applying transformations. The transformation can be part of the ELT process and happen immediately upon loading or can be done later as needed by data teams.
The ELT process is preferred since it simplifies data integration and adds more flexibility by decoupling the extraction and transformation processes. ELT also allows for faster data availability and is easier to scale.
Given all these advantages, most modern ETL data integration tools also use the ELT process to move and store data. They load data from sources onto cloud platforms, like Snowflake or Amazon Redshift, and then aggregate this data for analysis.
These days, most "ETL" tools use the modern ELT process behind the scenes.
Using ETL with data warehouses
Cloud data warehouses are vital for the success of your ETL pipelines. Amazon Redshift, Snowflake, and Google BigQuery are the most popular data warehouses for ETL tools.
All three enterprise data warehouses are affordable, easy to scale, and fully managed, enabling data teams to focus on analysis and innovation that propel business growth.
Here's a brief overview of the three platforms.
Snowflake
Snowflake is a flexible cloud data warehouse that works with multiple data ecosystems, including AWS, Google Cloud, and Microsoft Azure. It integrates with hundreds of third-party apps and uses a pay-as-you-use pricing model.
Businesses choose Snowflake because it supports both ETL and ELT data pipelines. It also separates the storage and computing processes, allowing data teams to run multiple operations simultaneously.
The platform also supports JSON functions and has machine learning optimizations to improve load times and reduce costs.
The only downside to Snowflake is the daily loading limits, which could prevent mass data migration at once.
Amazon Redshift
This column-oriented, OLAP (online analytical processing) database warehouse is part of Amazon Web Services (AWS).
Redshift is among the most affordable storage solutions out there. It's known for its innovative features and integrations that can speed up your data processing and automate processes.
However, only the modern Redshift RA3 node separates computing and storage, which could result in delays.
Redshift also only works within the AWS infrastructure, has limited support for JSON functions, and requires significant administrative monitoring to manage the granular settings of each of your data processes.
BigQuery
BigQuery is part of the Google Cloud Platform. It uses a serverless architecture and works with most major business intelligence tools.
Like Snowflake, BigQuery separates storage and computing, enabling data engineers to compute data without unnecessary replication.
The platform also has built-in SQL queries that allow teams to unlock machine learning capabilities easier. It also saves all changes made to tables for seven days, so analysts can quickly re-check and make corrections when needed.
BigQuery works best within the Google Cloud Infrastructure. It is more expensive than its competitors and less flexible since it automatically determines your settings.
Companies can get the best ETL data warehouse by researching and comparing the features of every tool. But this could be a lengthy and headache-inducing process given the hundreds of options on the market today. Instead, they can accelerate their data migration by hiring a data warehouse consultant.
Best practices for ETL
There are a few best practices to follow when setting up effective ETL pipelines.
Use ETL logging for better accuracy
Maintaining and monitoring a log of the events that occur during the ETL process is vital for efficiency and data integrity. ETL logging includes:
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Start and end times for the ETL process
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The status of each step in the process, including success or failure
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Errors and anomalies
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The amount of data synced
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Testing and debugging information
Data engineers can audit data after loading to check if the logs are accurate and that there aren't any missing or corrupt files.
Accurate ETL logging can help create ETL processes tailored to get results for your organization rather than adapting a cookie-cutter approach and hoping for the best.
Logging also helps debug the ETL process when data integrity issues occur.
Include tools for structured and unstructured data
Since modern businesses need information from multiple data sources, your ETL process must be able to handle structured, semi-structured, and unstructured data.
In-house applications, CRM, PostgreSQL, and more data sources often contain incompatible data structures for analysis. To combat this, your ETL architecture can incorporate tools and connectors to properly extract unstructured data.
Your ETL software must also collect data from niche industry or client-specific long-tail applications.
Use architecture built for scalability
As businesses grow, the amount and types of data they collect and analyze also increase. When building your ETL architecture, you can enable easier scaling by:
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Adopting cloud-native ETL tools
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Using a distributed systems architecture to process big data workloads
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Implementing proper pagination practices to avoid dropped or missing records
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Respect API rate limits
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Aggregating data before loading
Your ETL solution must also support scaling down, like removing resources and components you no longer need to save money on storage and cloud-server processing costs.
Benefits of an ETL tool
Using an ETL solution, especially one that integrates with a fully-managed, cloud-native platform, offers several advantages.
Platform agnostic: Popular cloud data warehouses for ETL are platform-agnostic, meaning they can be linked with other platforms to help you build an effective data pipeline. While some systems work within specific infrastructures, they still provide integrations to streamline data integration.
Speed: ETL solutions have the architecture and features to speed up both setting up an ETL process and data availability. Fully-managed solutions also take away the headache of monitoring your processes and let you focus on the data instead.
Compliance: Manually ensuring the data compliance of your ETL processes with GDPR, CCPA, HIPAA, and any other local or international regulation is a never-ending task and can become overwhelming very quickly. It's best to use an ETL tool that's already compliant with existing laws.
Simplicity: ETL tools can be used "out of the box" for data extraction and loading, eliminating the need for complex manual setups. Using a no-code platform further simplifies the building and managing of ETL processes. One platform to effortlessly manage all your processes improves your data analytics and business intelligence.
Extensibility: Data teams can extend the capabilities of ETL tools via integrations and Python scripts to cater to different use cases and workflows. Portable's team will also build custom connectors for your long-tail applications on demand.
Choosing ETL tools
When choosing an ETL tool, you'll need to make a few decisions as to which features matter most.
Buy vs. build
You can either manually build, implement, monitor, and manage ETL processes on your own or buy a pre-built ETL solution.
Building an ETL data pipeline requires significant investment in time, labor, and money. You need a team to plan your data flow and hand-code your data integration pipeline from scratch. This could take weeks or months.
A 2021 survey reported that companies spend around $520,000 a year for a data engineering team to build and maintain data pipelines.
Moreover, data engineers are forced to focus on building new pipelines and maintaining existing ones instead of more high-value tasks, like core projects and innovation. Companies also lose the chance to capitalize on new opportunities when their data teams are already overburdened with pipeline-related activities.
Buying an ETL solution is much quicker and more affordable. ETL tools provide pre-built connectors they maintain for you, and some tools like Portable add new data sources and manage existing pipelines effortlessly. Buying a tool can help data teams focus on their strengths rather than building ETL processes.
Cloud vs. on-premise
Organizations can set up their ETL pipeline using an on-premise infrastructure or through a cloud-based tool.
There's a clear winner here: cloud-based tools offer greater flexibility, security, and scalability. They can integrate with data sources in the cloud as well as on-premise sources if need be.
Open-source vs. proprietary
There are two options for a purchased ETL tool: open-source or proprietary.
Open-source ETL tools are often free or affordable and give data engineers easy access to the source code. These tools offer pre-built connectors and other features on a budget and allow users to add additional functionalities. Open-source tools are most common in on-premise infrastructure.
Most cloud-based tools are proprietary. These platforms have all the features of open-source tools but also offer hundreds of connectors that are fully managed, which means all the connectors are maintained and updated by the vendor.
Batch vs. real-time
Batch ETL processing involves periodically collecting and storing data in batches based on a predefined schedule. For example, your ETL process can collect data from specific sources once a week and load it into a data warehouse.
In real-time ETL, data is extracted as soon as it is generated at a source and loaded into the data pipeline for processing using incremental load technologies like change data capture (CDC).
Batch data integration is ideal for business use cases where the data does not need to be updated instantly. For example, batch processing can update a company's document index.
Streaming or real-time ETL is helpful for businesses that need to perform data analysis by the second, such as with stock prices. While ETL has traditionally been associated with batch ingestion, modern ETL tools can easily support real-time operations.
ETL for Data Warehouse: The Bottom Line
ETL data integration allows businesses to centralize all their data and perform accurate analysis that can elevate internal and external operations. These insights fuel data-driven decisions that can help companies grow.
To build an effective ETL system, you must choose the right tools and data storage options. Cloud data warehouses are the best storage choice for ETL data pipelines, as they can effectively handle large amounts of data and have the features to simplify data analytics.
Portable is the best ETL solution for data warehouses, with 300+ pre-built connectors and a committed team that develops custom connectors quickly and maintains them so you don't have to.