App developers, data engineers, and IT teams face recurring challenges when it comes to moving data between applications and storing it for analysis. We all know, however, that if we are wise with our data, our businesses can benefit significantly.
Moving data around has never been easier than it is now. ETL (extract, transform, load) and custom-built integrations have been around for a long time and have evolved. ELT (extract, load, transform) and event streaming, on the other hand, were born out of necessity. These data pipeline tools’ requirements and application cases have become increasingly sophisticated and demanding. Data migration from your data warehouse to the cloud apps your company relies on has emerged in recent years as a fresh (yet sensible) use case as a result of these constantly expanding needs. This demand has led to the development of a new type of data pipeline: reverse ETL.
How is reverse ETL defined?
Moving data from your data warehouse to your cloud applications is a straightforward function of reverse ETL. An API (application programming interface) endpoint that the reverse ETL tool exposes or through integration with other tools like Airflow and dbt are the most common triggers for reverse ETL tools to synchronize data on a recurring schedule (configurable between a few minutes and 24 hours or longer).
What can I accomplish with reverse ETL?
The promise of data science can be realized with the help of reverse ETL technologies. Your data warehouse is the repository for the complicated and valuable modeling and analysis that your data teams perform. The job your data scientists undertake becomes more valuable when you can leverage this enhanced, post-analysis data and automate the updating of your business applications with it. It also assures that their work generates value in a more real-time manner than manual methods now used by most companies.
There are a few things to keep in mind when using reverse ETL tools. Use cases for which reverse ETL solutions are most effective:
- Creating more accurate and detailed profiles of current and prospective customers
- Creating smaller, more targeted audience groups
- Use your business-specific criteria to rank leads.
- Identifying clients that are “at-risk,” or likely to leave your company
- Enabling improved reporting using cloud-based applications
Who are the most prominent providers of reverse ETL?
Reverse ETL tools are available, and they all perform the same functions. An SQL (structured query language) statement (or selection of a table) is used to pick the data to be synchronized, mappings are selected, and a synchronization schedule is specified. Once these steps have been completed you are ready to begin syncing data.
Although reverse ETL systems have identical features, three companies stand out:
- Rudder Stack
Instead of being a reverse ETL tool, Rudder Stack is an event streaming platform. For many years, the company was known as Segment’s open-source alternative. Rudder Stack’s ETL and reverse ETL functionalities were announced earlier this year, making it a rival in the reverse ETL market.
Reverse ETL relies on tools like Segment, Snowplough, or Rudder Stack to deliver data into the warehouse, and this combination of characteristics makes sense because of this. It is only with Rudder Stack’s reverse ETL capabilities that you can import the customer data you require. Additionally, the organization has a much broader selection than either Hightouch or Census of integrations. Streaming event tools require large integration libraries to remain competitive.
Crate & Barrel, Priceline, Acorns, and Hinge are just a few of Rudder Stack’s clients.
However, Segment does not market itself as having reverse ETL capabilities. Segment’s Personas audience builder is required to sync data from your warehouse to your cloud applications via Personas SQL Traits.
Segment Data Lakes, which creates a customer data lake for you, was introduced by Segment at the end of last year. Considering this, the company’s ability to perform reverse ETL becomes less important.
In Hightouch’s view, your data warehouse is your primary source of consumer information. The company makes it simple for you to transfer that data to the cloud services you use. For pure reverse ETL, Hightouch stands out due to its maturity and extensive source and destination integrations. In the previous six to twelve months, the company has also expanded its integration library at a quicker rate than Census. Your company’s ability to employ a wide variety of tools will be limited by how many integrations it has. For reverse ETL, the more integrations there are, the better.
Grafana, Plaid, Zeppelin, and Matter most are examples of high-touch consumers.
Census is perhaps the most widely used reverse ETL tool in the business. High touch, on the other hand, has been around for a longer period and has a more remarkable customer base than Census. Compared to Hightouch, it is a well-established product with many integrations, but it does not have as many as Hightouch has.
Many companies participate in the Census, such as the companies as well as others like dbt and netlify.
Hightouch or Census are the most likely options when comparing reverse ETL technologies. Hightouch and Census have distinct pricing structures, thus your decision criteria will be based on integrations and pricing. Census rates are based on the number of data synchronization workflows you conduct, whereas Hightouch prices are based on the monthly number of records synced.
Possible substitutes for reversing ETL
Customers profiles, audience segmentation, and other procedures cantered on customers benefit greatly from reverse ETL. It makes it reasonable that the real-time requirement for these activities is not rigorous, as loading and analyzing data in real-time is not an appropriate architectural pattern for a data warehouse. Real-time application response is not possible with data warehouses and OLAP databases, which can run complicated queries and models quickly.
Using technologies like Rockset to give real-time analytics to your apps is an emerging solution to these real-time requirements. However, Rockset differs in that it is cloud-native and has an emphasis on SQL compatibility, whereas Elasticsearch functions similarly. This means that you’ll be able to do things like joins that Elasticsearch doesn’t enable, and you’ll be able to scale beyond Elasticsearch’s capabilities.
In a massively multiplayer online game, Rockset can be used to feed statistics to a continuously updated scoreboard. Even though it is extremely difficult to ingest events and calculate millions of individual scores in real-time for millions of concurrent users, this is a common use case for tools like Rockset.