Every company needs to integrate complex data from many different sources into a single, unified view. Salesforce, Adobe Experience Cloud, social media and website analytics APIs—all of these data sources need to combine in a cloud data warehouse to transform into actionable business intelligence. Extract, transform, and load (ETL) data integration tools bring this raw data together and make sure it’s in the right format.
This process makes large volumes of data useful for analysis, decision-making, and product development. Place ETL at the center of your data integration strategy and you’ll start eliminating data silos and incompatible datasets while improving overall data quality.
The ETL process empowers businesses of all sizes—from enterprises to startups. Need to move all your product data into a cloud data warehouse? ETL does that at any scale. And once you integrate all of your data in a central location, you can create an analytics dashboard that updates in real time, build new software features faster, and identify previously invisible business opportunities.
Here’s a closer look at how the ETL process works, how it fits into a data integration strategy, and the ETL tools you can use to get started.
What is the ETL process?
The ETL process is a combination of technology and methodologies that move datasets from periphery and external data sources into a central data warehouse. And it doesn’t just extract the data from source to storage, it transforms extracted data into usable formats and schema. Then it loads the transformed datasets into your cloud data warehouse, data lake, or databases.
Its three main steps happen exactly as they sound:
- Extract: Extract data from a source system
- Transform: Transform data into a useful format
- Load: Load data into a target system (like your cloud data warehouse or data lake)
ETL isn’t new—it’s been around in some form since the 1970s. But it’s more valuable and commonly used today because of the drastic increase in volume and complexity of business data.
Instead of building ETL data pipelines manually like in the old days, your DataOps team can set up and automate a variety of ETL processes quickly. Many companies maintain teams of dedicated data engineers, data analysts, machine learning specialists, and data scientists. Together, they use modern ETL tools as part of a larger approach to data integration to transform big data into useful insights.
With a modern data stack and automated data ingestion processes like ETL, data analytics teams now have constant streams of accurate and up-to-date data. That means there's more data coming from more sources than ever, and it’s also why data integration is a common milestone on technology roadmaps.
What is data integration?
Data integration is the initiative to unify all data sources at an organization into a usable dataset. This includes everything from legacy databases to new data being moved and transformed by your ETL tools.
If you’re an established company it can take a lot of data cleansing to get a unified view of your data. In some ways, startups have an advantage with big data because they can include data integration from the start.
One important example of data integration is the unification of customer data. You need ETL tools to move info about each customer from many systems—everything from CRM tools to social media analytics. Once the data is transformed and loaded into your cloud data warehouse, you can create a singular view of customer metrics and behaviors. And then you can even use Reverse ETL to move that customer info from your data warehouse back out to other systems.
While there will be other tools, processes, and methods involved in your data integration strategy, ETL is one of the most useful.
Why use ETL for data integration?
Whenever you have multiple data sources—like Salesforce, Adobe Marketo Engage, Hubspot, Google Ads, Facebook ads, social media, website analytics, APIs, or cloud apps—you need ETL. That’s because automated ETL pipelines move and transform the data before loading it in a cloud data warehouse to meet your requirements for data integration.
Once you have ETL pipelines set up you can start to create models for intelligence like lifetime customer value, brand loyalty metrics, and advanced customer journey insights. When all your data sets are extracted, transformed into compatible formats, and loaded into your central data warehouse, those advanced business metrics become possible to calculate, visualize, and use for decision-making.
Here’s a list of benefits your business can get from using ETL for data integration:
- Increased decision-making speed
- Improved data quality
- Faster and more efficient data integration
- More accurate data analysis
- Better data visualizations
- New datasets for machine learning analysis
- Central source of truth for company data
With integrated and accurate datasets, you can build new software products and features faster. For example, when your product data is cleansed, structured, and accessible, you can quickly iterate on new searches and filter ecommerce features. Every time you use ETL to move data from a new source to your data warehouse, you add to the list of possible opportunities for your business.
Key steps in the ETL data integration process
ETL sounds simple—extract, transform, and load—and in some ways it is. You’re basically extracting large volumes of data from the target source, transforming extracted data formats into formats your data warehouse understands, and loading data into a data warehouse or data lake. But it’s also a complex process with important technical details wrapped up in each step. For example, data transformation can include data cleansing, validation, and deduplication—3 different tasks in that single step of the ETL process.
Here are a few important details of each step.
Extract data
In the first step, structured and unstructured data is extracted from a data source and consolidated into a staging area. Once in the staging area, data is transformed.
Here are some common data sources:
- Customer relationship management (CRM) tools
- Analytics tools
- Website analytics
- Product information databases
- Advertising and marketing platforms
- Customer data platforms (CDPs)
Transform data
This step transforms data to meet the quality and schema requirements of the data warehouse. It’s crucial to transform data because all your extracted data exists in different formats, organizations, and varying degrees of completeness.
Here are some of the common sub-processes that make up data transformation.
- Data standardization: Applies the formatting rules of your data warehouse to the extracted data set
- Data cleansing: Cleans up errors, missing values, and inconsistencies in the data
- Data validation: Scans the data for errors and anomalies, and removes unusable data
- Data deduplication: Removes duplicate or redundant data
- Custom data tasks: Runs any custom tasks on the data to meet specific company DataOps requirements
Load data
The last step in an ETL process happens in one of two different ways—full loading or incremental loading.
A full load process in your ETL pipeline puts all transformed data into new records in your cloud data warehouse. This can be useful but it’s generally riskier because full loading grows your datasets exponentially and makes your data warehousing harder to manage.
Incremental loading is a safer approach. It compares incoming data with existing records in your target database and only creates new records for unique loaded data.
Which ETL tools should I use?
There’s a wide variety of ETL tools to choose from—everything from enterprise-ready solutions to open-source ETL and cloud-based ETL tools. Before you choose, define your use cases, budget, desired capabilities, and data sources to make the best decision for your data integration strategy.
Fivetran
Fivetran replicates applications, databases, events, and files into high-performance cloud warehouses. It’s a popular cloud ETL tool that’s known for its ease of setup (connecting data sources with destinations)—making it one of the most intuitive and efficient data pipeline tools.
With Fivetran, you can pull data from over 5,000 cloud application sources and add new data sources quickly. It supports advanced data warehouses like Snowflake, Azure, Amazon Redshift, BigQuery, and Google Cloud.
Fivetran is a godd ETL tool for data integration initiatives. It’s quick to set up and easy to use so you can spend your time focused on what’s really important. Companies of any size that want to move data from dozens of data sources into warehouses without unnecessary hassle can use Fivetran.
Pros:
- Automated ETL pipelines with standardized schemas
- No training or custom coding required
- Access all your data in SQL
- Add new data sources easily
- Complete replication by default
- Customer support via a ticket system
Cons:
- Tricky to figure out the final cost of the platform
- Limited data transformation support (most Fivetran users also need to use dbt)
- Lacks some enterprise data capabilities in data governance and data quality
Apache Airflow
This is the most popular and most used open-source cloud ETL tool. Apache Airflow lets you monitor, schedule, and manage your workflows using a modern web application.
These ETL pipelines are defined in Python, meaning users must use standard Python features to create data workflows and dynamically generate tasks. That’s great news for seasoned data engineers because Python gives users full flexibility when building workflows.
Apache Airflow is a great option for data engineers and other more technical users who frequently work on creating complex ETL pipelines.
Pros:
- Excellent functionality for building complex ETL pipelines
- Extensive support via Slack
Cons:
- Slow to set up and learn to use
- Requires knowledge of Python
- Modifying pipelines is difficult once they have been created
Stitch
This cloud-based ETL platform extracts data from multiple SaaS applications and databases and then moves it into data warehouses and data lakes. Stitch is an easy-to-set-up ETL tool. With minimal requirements and efforts—teams quickly get their data projects off the ground and start moving data through the ETL process.
Stitch is a great option for both DataOps teams and non-engineering teams who use lots of data, like marketing. It comes with a central UI to manage and monitor your ETL processes. Stitch’s data integrations make it a great ETL tool for any company that needs to extract, transform, and load data from multiple sources.
Pros:
- Easy-to-use and quick setup for non-technical teams
- Scheduling feature loads tables on predefined time
- Allows users to add new data sources by themselves
- In-app chat support to all customers and phone support is available for enterprise users
- Comprehensive documentation and support SLAs are available
Cons:
- Lacks some data transformation options
- Large datasets may impact performance
- No option to use or deploy services on-premise
Shipyard Data Orchestration
Shipyard integrates with Snowflake, Fivetran, and dbt Cloud to build error-proof data workflows in 10 minutes without relying on DevOps. Data engineers at any company can use these tools to quickly launch, monitor, and share resilient data workflows. Then your data integration strategy can drive value at record speeds (without the headache).
Shipyard is a powerful cloud ETL tool that aligns data teams and ensures they can scale and customize their data pipelines. It integrates with a long list of cloud applications, enterprise solutions, and data tools. Shipyard also comes with super-easy data transformations, visual interface, and customer support.
Pros:
- Simple and intuitive UI makes it easy for experienced and new users to adopt the tool
- Build advanced workflow automations with low-code templates and visual interface
- Integrates with a variety of data sources—e.g., Snowflake, Fivetran, dbt Cloud, Airtable, Amazon S3, spreadsheets, and more
- Robust reporting capabilities to track inefficiencies, update processes, or make improvements instantly
- Real-time notifications about critical breakages
- Secure data handling with zero data loss
- Modify your data pipelines with new logic immediately and scale as your data load grows
Cons:
- Fewer integrations for pure data ingestion
You can read more about which ETL data integration tools work best for your business needs. Or you can get started with an ETL solution today.
Get started with ETL data integration
Any of these ETL tools might be the missing piece of your ETL data integration strategy—it just depends on your current data infrastructure. We built Shipyard’s data automation tools and integrations to work with your existing data stack or modernize your legacy systems.
If you want to see for yourself, sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start to build data workflows in 10 minutes or less, automate them, and see if Shipyard solves your DataOps challenges.
