Data warehousing aggregates data from disparate sources so you can run real-time reports for greater business intelligence. However, a data warehouse does more than generate big data analytics. How about using it as a data source rather than just a destination? You can move data from your warehouse to other systems in your networks, such as Salesforce or Zendesk, and improve existing operations. 

Moving data from a warehouse is a relatively new process called operational analytics, and it all relies on a data integration method called reverse ETL. Here, the 'extract' and 'load' stages of conventional ETL take place inside a warehouse before big data pipelines move it to a second location. 

Reverse ETL won't suit all businesses. It's more complicated than regular ETL and can present data conflict and compliance issues. However, moving data from a warehouse can enable advanced data profiling and strengthen your existing operations. 

Below, learn how to operationalize your data warehouse with reverse ETL and the benefits of doing so for real-time data insights. 

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Read more: Reverse ETL: What You Need to Know

Why Should You Operationalize Your Data Warehouse?

For years, cloud data warehouses such as Snowflake and AWS Redshift served one purpose: to store data so businesses can run analytics on that data via business intelligence (BI) tools. 

Extract, Transform, Load (ETL) remains the most popular method for data warehousing. It involves big data pipelines that extract data from its source, transform data into a format suitable for metrics, and load it to a warehouse so data no longer exists in silos. At this point, businesses can use BI tools such as Looker, Marketo, and Tableau to discover trends and patterns within data sets from dashboards in the warehouse. Some of the most popular ETL data transformation tools include, Fivetran, and Matillion. 

Now, organizations realize the benefits of operationalizing their data warehouse. By using a data warehouse as a source, they can move data sets to other locations for operational analytics to learn more about day-to-day tasks such as sales, marketing, field services, or customer services. 

Here are some examples of operationalizing a data warehouse:

  • As well as moving Salesforce data to Redshift with regular ETL, you can move customer data to Salesforce via reverse ETL. This process helps you optimize customer profiles, engage with customers, and reduce churn. 
  • You can move warehouse data to the Oracle Database and optimize product usage data for your marketing team. 
  • You can move Google BigQuery data to HubSpot CRM. Now sales teams have access to more information so they can improve customer experiences. 

To move data from a warehouse for operational analytics, you need to know about reverse ETL. 

Read more: State of the Reverse ETL

What Is Reverse ETL?

Reverse ETL turns the concept of ETL on its head. ETL data pipelines extract data from a conventional data source, transform it into a format for analytics, and load data to a warehouse, which brings better functionality and automation. Reverse ETL pipelines, on the other hand, extract data from a warehouse, transform the data inside the warehouse, and load it to an external system for operational analytics. 

You might use reverse ETL if a data warehouse, perhaps inadvertently, has become your primary data store. All your data might exist in a warehouse, and there's little you can do with that data other than run reports via BI tools. This issue won't affect organizations that solely want to use a warehouse to generate business intelligence. However, other companies might want to operationalize their warehouse by moving data to a separate data platform. This process enables advanced data profiling in a customer relationship management (CRM) system, relational database, operational system, or transactional database. 

It also depends on how you define the term 'single source of truth.' If you are happy for your warehouse to serve as the primary location for data in your organization, you might not have any reason to move data sets to other places. However, if you want a system such as Salesforce as your source of truth, you should operationalize your warehouse via reverse ETL.

Read more: Use Cases for Reverse ETL

Challenges of Reverse ETL

Reverse ETL can pose significantly more challenges than regular ETL. That's because you need to build complex data pipelines that transfer data from a warehouse to your chosen destination. Creating these pipelines requires lots of coding and data engineering knowledge and might take weeks or months to implement, rendering this process useless for smaller companies and startups. 

There are other issues.

Data in a warehouse has likely already changed into a format for analytics during the ETL process. Moving that data to a second location can result in a schema change, affecting all the applications that use that schema. There might also be data conflict problems, causing a database mismatch that could jeopardize your organization. Plus, consider data governance legislation and whether you can even use data in a warehouse for operational analytics. For example, customers might not have permitted you to use their raw data for marketing.

Using a reverse ETL tool can solve many of these challenges for data teams. First, you won't have to create complex data pipelines because the tool automates the process, freeing up time and labor in your organization. Plus, you can avoid schema changes, data conflicts, data governance issues, and other problems that arise when using data. 

How Helps makes reverse ETL less of a challenge with its streamlined interactive interface that lets you build data pipelines from your warehouse to new destinations. With over 100 native connectors, you can move data to multiple locations across your enterprise for better operational analytics such as CRM systems, relational databases, data lakes, and SaaS apps. Its Salesforce-to-Salesforce connector moves Salesforce data to a warehouse and then back again without any fuss. simple pricing system only charges you for the native connectors you use and not data volume, which could save you money compared to other reverse ETL solutions. Plus, you can benefit from telephone and email customer support, a powerful REST API, and easy workflow creation for defining task dependencies. 

If you want to operationalize your data warehouse, can help. This no-code ETL and reverse ETL platform transfers data from a source to a warehouse and moves it back again. Now you can transport your data wherever you like. Start your 7-day demo now and add to your modern data stack.