What Is Data Mapping in ETL and How Does It Work?

What is data mapping in ETL?

Data mapping in ETL is the process of matching fields in multiple datasets into a schema or centralized database as part of data migration from different sources to the data warehouse.

In other words, it’s a breakdown of where data comes from all the way through where it lands.

Enterprise data mapping is an essential part of the ETL process (extract, transform, load). Businesses collect data from a number of different sources and often transmit that data back to a variety of destinations, like data warehouses and data lakes.

Having clear guidelines around what information is extracted, how it’s transformed, and where it’s loaded helps keep things neat and organized.

When designing Whatagraph’s data mapping feature, we had two types of users in mind:

• Small non-tech teams
• Marketing agencies that handle hundreds of clients, each with their own data stacks

In both cases, Whatagraph saves the time needed to load data from multiple sources to Google BigQuery.

We simplified the data transfer process to the point-and-click level so anyone can do it.

No code knowledge or data engineering is required.

Data mapping is not a new concept in the data-driven business world. However, as the amount of data and the complexity of the systems are growing, the data mapping process has become more time-consuming. This led to more and more businesses using powerful ETL automation tools to accelerate their data mapping.

3 types of data mapping

So, how do you create the roadmap from your data source all the way to its destination?

There are three data mapping techniques you should be aware of. The option that works best for you depends on your company size, your team’s expertise, and your business needs.

Manual data mapping

This is the basic approach for developing a data mapping tool in your company. Manual mapping requires developers to create links between the source data and the target data. This approach works for one-time data transfers or custom data types that are not very common.

Still, the size of most datasets and the pace of data transformation in today’s ecosystems makes the manual approach unsuitable for handling complex data mapping procedures. This is why businesses often resort to partial or even complete automation.

Semi-automated data mapping

There are data processing tools that can accelerate your efforts by creating a connection between your data sources and target database — based on guidelines that you provide. With a semi-automated approach to ETL mapping, you eliminate the manual process of copying and transferring data.

So what’s the catch? The semi-automatic mapping model still takes time and effort to establish how the different bits of data are related, which data points are identical, what should be combined, ignored, etc.

This ultimately means that you still need data specialists to set up your data mapping and keep it running. Also, depending on your use case, you may run into some inefficiencies by switching between automated and manual work modes.

Automated data mapping

Automated data mapping is nowadays preferred by businesses that want to smoothly upload new data and match it to their existing schemas. Most data mapping software display this progress in a graphical user interface (GUI), so end users can understand the steps their data is passing through.

High-end programs allow customers to input data from thousands of different sources and load data to a data warehouse like Google BigQuery, Snowflake, or Microsoft Azure.

A completely automated data mapping system allows nontechnical staff to set up and monitor data mapping. This way, teams can observe how their data is being mapped, which allows them to discover mistakes on the go and easily optimize the process.

Benefits of proper ETL data mapping

Automated reporting of your data analytics

Your ETL mapping can reduce the workload of your data analytics operations. This is especially true if you use a semi-automated or fully automated data mapping system. This way, you can delegate much of the time-consuming workload to the software.

Let’s say that your marketing team has a set of KPIs they need to report on every week. To create this report, they need to navigate between multiple platforms and add these numbers into Excel spreadsheets manually. A good ETL tool such as Whatagraph can extract this data in a few clicks and make reporting much easier.

More time for in-depth analysis

Solid ETL mapping allows data analysts to spend less time extracting, transforming, and loading data manually, and invest more time in the analyses. When your data is accessible and reliable, teams reach for deeper insights relevant to your business operations and strategy.

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For example, when you need to look into client behavior to establish a level of churn risk. The right data mapping process will help you quickly pull the data models you need and focus on the analysis.

Increased data quality and more reliable insights

If you want to be able to act upon your data, it needs to be reliable. You can’t afford to use inaccurate data in your operations. A proper ETL mapping structure ensures all data is clean and trustworthy, making it easier for your team to lean on it.

For example, your sales and success teams have different definitions for customer activation. Because of this, it’s not clear which team is responsible for communicating with a segment of your customers. ETL mapping offers clearer definitions of which user lands in which team’s pool of contacts, enabling you to close those gaps.

Smoother adoption of a new data stack,

So you want to build a modern data stack to redefine the ways you source, ingest, store, transform, model, analyze, and activate data?

There are a lot of pieces that need to fall in the right place, so having a good ETL mapping structure makes any data migration painless.

Let’s say you’ve decided to invest in a data warehouse to centralize your business and make data from different tools accessible to every team. With the right ETL mapping, your data warehouse can become a reliable single source of truth.

4 tips for effective ETL data mapping

1. Identify data: You need to identify the data you need to map and also identify data that might not be part of the mapping process. Define the data relationships and any pre-processing that you may need. Then define the frequency and priority of the mapping process. You may want to map some of the data first and other data later.

For this, you need to know the semantics of your metadata and how they act as an indicator of facts. Finally, outline the mapping instructions and procedures.

1. Run data mapping: Identify the data flow. Map data from source to destination relevant formats. Keep logs at the required granularity and keep an eye on errors or bottlenecks.
2. Transform your data: If needed, your data needs to be transformed at the destination so you can store it and use it efficiently later. For example, if your data fields are being collected from different time zones, you need to change them into a Common Standard Time format before you can analyze them.
3. Test and deploy: Data testing includes visual, manual, or automated testing. Automated testing is often needed these days due to the sheer volume and diversity of data being processed. Once you’re satisfied with the tests, you can deploy the data — move it to a data warehouse for analysis.
4. Maintain and update: As you add new data sources, the mapping process needs maintenance and updating. In other words, you need a data mapping tool that scales to your business needs.

5 best ETL data mapping tools

1. Whatagraph

Whatagraph is an automated data pipeline that helps marketing teams load data from diverse sources such as Google Ads or Facebook Ads to BigQuery.

Automate your data mapping with Whatagraph, and you not only just eliminate manual work but also complete your data transfer in just four clicks:

1. Connect the destination
2. Choose the integration
3. Set the schema
4. Schedule the transfer

At this point, you can also set the schedule — when and how often you want your data moved to BigQuery.

And when you need to create a report on our data, you can also use Whatagraph. Our visualization tool helps to create custom drag-and-drop dashboards using your BigQuery data. Use one of our pre-loaded templates or create your own!

Switch between different types of graphs and tables, add and combine sources, metrics, and dimensions, and much more.

Another perk of using Whatagraph for data mapping is that BigQuery transfers are available even in the basic Whatagraph pricing plan.

If you’re looking for an easy-to-use solution to connect data from various marketing sources, keep your marketing data safe, or want to visualize your BigQuery data, book a demo call and find out how Whatagraph can help.

2. Integrate.io

Integrate.io is a cloud-based data integration, ETL, and ELT platform that allows you to create simple, visualized data pipelines for your data warehouse. Integrate.io allows users to integrate data from more than 100 data sources and SaaS applications to store it in SQL data stores, NoSQL databases, and cloud-based data warehouses.

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Integrate.io comes with an intuitive graphic interface that helps you implement ETL, ELT, or data replication solutions. Orchestrate and schedule data pipelines using Integrate.io’s workflow engine that provides connectors for applications, databases, and data warehouses.

3. Skyvia

Skyvia is a no-code business intelligence platform that enables a wide range of data integration use cases, including ETL, ELT, Reverse ETL, data sync, workflow automation, etc.

It allows users to integrate different data formats from CSV files, relational database tables, and cloud applications.

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Use multiple connectors to design comprehensive data pipelines. Designing pipelines apart, you can use Skyvia to perform complex mapping and multi-level transformations, too.

Skyvia allows users to visually design data flows and easily export and import data from various sources.

4. CloverDX

CloverDX is an open-source data mapping and data integration tool that runs on the Java platform. You can use it to transform, map, and manipulate data. It has enough flexibility that teams can use it as a standalone app for data warehousing, command-line tool, or server application.

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CloverDX allows companies to efficiently create, test, deploy, and automate the data loading process from source to destination. It offers visual and coding interfaces for developers to map and transform data.

5. Talend

Talend is a data management platform that offers real-time enterprise-level data integration. It helps teams connect, access, and transform any data. It can perform data integration across the cloud or on-premises.

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Talend offers more than 900 pre-built components for seamless integration with any environment. It features a simple user interface that provides ETL testing tools for schema mapping, collaboration, scheduling, and monitoring. Talend lets you extract data from data structures like relational databases, CRMs, JSON, XML, and flat files.

Conclusion

ETL data mapping helps businesses get a bird’s eye view of their data transfers on both ends of the pipeline. Done right, data mapping results in in-depth analytics processes that yield relevant and accurate insights.

However, manual data mapping has always been a time-consuming process.

But now you can use Whatagraph to do the heavy lifting.

With Whatagraph, your data transfers become a 4-click routine:

Select the destination, select the source, set the schema, and run the transfer.