AXS knocks it out of the park with modern data analytics

Learn how Etleap and Looker helped AXS reduce manual ETL work and reporting, allowing them to focus on growth for themselves and their clients.

AXS is a ticketing company for live entertainment

AXS powers the ticket buying experience for over 350 world-wide partners

AXS is a leading ticketing, data, and marketing solutions provider in the US, UK and Europe. The company and its solutions empower more than 200 clients— teams, arenas, theaters, clubs and colleges— to turn data into action, maximize the value of all their events and create joy for fans. It is an enterprise event technology platform that services venues, promoters and sports teams; providing fans the opportunity to purchase tickets directly from their favorite venues via a user-friendly ticketing interface. While customers know them as a destination for tickets, clients recognize AXS for their data services, including transforming, reporting, analyzing, and more.

The data services offered by AXS have always been incredibly helpful for clients. But to make them so valuable, a significant amount of ETL work and reporting was required, which resulted in some challenges for the team. To learn more about those challenges, and how they eventually found a solution in both Etleap and Looker, we spoke with Ben Fischer, the Sr. Director of Business Intelligence and Strategy.

THE CHALLENGES

Ben oversees the Business Intelligence and Strategy team, which manages everything from integrations and building data models to powering the data warehouse and products across AXS. The team’s main objectives are to power AXS’s internal data services, while also delivering data services for clients.

Before Etleap and Looker, the Data Engineering team was spending more and more of their time working on internal and external requests for one-off ingestions and custom data sources. Each data source would take anywhere from half a day to weeks (or even months) to implement, which meant the team was spending most of their time on ETL work, and not enough time on making the data useful.

“With Etleap, we’re able to do the ETL end-to-end and get it directly into the hands of whoever’s trying to use it right away.”

– Ben Fischer, Sr. Director of Business Intelligence

Ben told us, “The whole team was just getting sucked into ETL work constantly, which was not the best use of their time. We wanted to be working on modeling and on the products.”

SEARCHING FOR A BETTER DATA SOLUTION

In order to find the right solutions to fit their needs, the AXS team compared several modern ETL solutions.

When comparing the options, they found that most of the tools were good solutions for getting ETL out of the engineers’ hands, allowing less technical people to consistently bring in the data, while also offering support and monitoring. However, Etleap stood out in two main areas: transformation and transparency.

For AXS, transformation was important because they wanted the ability to not only bring in a new data source, but automatically transform it into something useful for analysts. “Stitch and Fivetran are really focused on the “extract” and “load,” so they’ll bring data in from an outside source and put it into your data warehouse, but they don’t offer much in the way of transformation. You still have to transform it afterwards into something that’s usable, which means you’re still relying on engineering to access the data. With Etleap, we’re able to do the ETL end-to-end and get it directly into the hands of whoever’s trying to use it right away.” said Ben.

Etleap’s data wrangler makes parsing and structuring data take minutes instead of months.

Beyond the transformation aspect, the AXS team was also impressed by Etleap’s level of transparency around reliability. “A lot of the competitors emphasize this idea of 100% reliability. They would say that they would never miss any data, everything would come through perfectly, and you would never have any issues. But we knew that wasn’t the case. No tool is 100% perfect, and when talking with Etleap, they were much more open about what we could expect. They acknowledged that 100% reliability is the objective, but that it’s challenging to achieve in practice, so it’s something they’re continuously working to achieve. Some of the competitors wouldn’t even acknowledge that reliability could possibly be an issue, which makes you feel like they may not support you if anything goes wrong.” said Ben.

To top things off, Etleap was also very straightforward to use. It required very little training and offered reliable support, which meant AXS could get up and running immediately. When the team first started evaluating ETL solutions, they encountered complexity with managing the tools and building integrations. “But with Etleap, it’s pretty straightforward. There’s always somebody available if you need to reach out. That meant we could start using Etleap in just a matter of days, rather than undergoing weeks of training,” said Ben.

Once the team found an ETL solution, it was time to help out the analysts. They looked at a variety of products for business intelligence, and even tried a few different solutions, but Looker stood out in part because it could get report building out of the hands of the analysts. Ben told us, “With Etleap, it was about getting ETL out of engineering. With Looker, it was about getting report building out of analytics, so analysts can spend their time actually forming opinions, defining strategy, doing analyses, and digging into the data, rather than just building reports day in and day out.”

Consistency and confidence are critical to democratizing data, and Looker’s data modeling layer allows people across AXS to pull their own insights and reports very quickly without having to worry about whether the numbers match. This means the Business Intelligence & Strategy team can now stay focused on building models and driving insights, instead of just building reports.

WITH ETLEAP AND LOOKER, THE ENTIRE AXS TEAM IS ABLE TO FOCUS ON HIGH-VALUE TASKS THAT DRIVE THEIR BUSINESS FORWARD.

Since implementing both Etleap and Looker, the impact has positively affected the entire AXS team, as well as their clients.

First, the Data Engineering and Business Intelligence & Strategy teams are spending far less time on manual ETL and reporting work, and much more time on high-value tasks that contribute to internal growth, as well as client successes.

“These tools make our various teams more impactful across the business. For example, if our engineers were just doing all the ETL work manually, we would not be able to do even half of the work that we’re doing to drive the business forward. And the same applies with Looker. Right now, we’ve got people all over the organization looking at reports every day in Looker and answering their own questions about what’s going on with the business.

“Our lives have become much less about pulling reports or bringing in data, and more about really driving value for the company beyond the mundane day to day.” Beyond making life easier and their work more impactful, reporting has also become much faster. Previously, you may have had to wait weeks, or potentially months, to get access to a new data source so you could do your analyses. Now, you can solve that yourself in a couple of hours, without having to wait for other people.

Looker gives companies a single source of truth for all their data.

“With Looker, it was about getting report building out of analytics, so analysts can spend their time actually forming opinions, defining strategy, doing analyses, and digging into the data, rather than just building reports day in and day out.”

– Ben Fischer, Sr. Director of Business Intelligence

“It also makes iteration much faster. We can define something, put it into production, and report off of it. If three days later we realize we forgot something, it’s a two-minute fix rather than going back to engineering, having someone spend half a day on it.” said Ben.

Finally, having access to Etleap allows the team to easily look at data from different angles, making the analysis and insights for clients even more valuable. “Etleap has a function for modeling data, which is useful for reporting, as it allows you to build the aggregations you need to power impactful reports. We can have processes that run everyday and get a quick summary of the data from all different perspectives. Before, it would have taken an engineer a couple of days to build that.” said Ben.

With Etleap and Looker, the AXS team finally has the time and resources to focus on bigger initiatives, including GDPR, internationalization, increasing accessibility across the organization, and providing even more data services to clients. With these tools in their arsenal, the sky is truly the limit.

Etleap Launches Snowflake Integration

I am pleased to announce our integration with Snowflake. This is the second data warehouse we support, augmenting our existing Amazon Redshift data warehouse and our S3/Glue data lake offering. 

Etleap lets you integrate all your company’s data into Snowflake, and transform and model it as necessary. The result is clean and well-structured data in Snowflake that is ready for high-performance analytics. Unlike traditional ETL tools, Etleap does not require engineering effort to create, maintain, and scale. Etleap provides sophisticated data error handling and comprehensive monitoring capabilities. Because it is delivered as a service, there is no infrastructure to maintain.

 

2019.05.07 - Etleap Product Graphic

 

Like any other pipeline set up in Etleap, pipelines to Snowflake can extract from any of Etleap’s supported sources, including databases, web services, file stores, and event streams. Using Etleap’s interactive data wrangler, users have full control over how data is cleaned, structured, and de-identified before it is loaded into Snowflake. From there, Etleap’s native integration with Snowflake is designed to maximize flexibility for users in specifying attributes such as Snowflake schemas, roles, and cluster keys. Once the data is loaded, Etleap’s SQL-based modeling features can be used to further improve the usability and performance of the data for analytics.

Not only does Etleap’s integration with Snowflake provide a seamless user experience, it is also a natural fit technically. Etleap is built on AWS and stores extracted and transformed data in S3. Since Snowflake stores data in S3, loading data into Snowflake is fast and efficient. Architecturally, part of what differentiates Snowflake is its separate, elastic scaling of compute and storage resources. Etleap is built on the same principle, thus enabling it to overcome traditional bottlenecks in ETL by scaling storage and compute resources for extraction and transformation separately and elastically. By taking advantage of AWS building blocks we are able to provide a powerful yet uncomplicated data analytics stack for our customers. 

Etleap is devoted to helping teams build data warehouses and data lakes on AWS, and we offer both hosted and in-VPC deployment options. Like Snowflake, Etleap takes advantage of AWS services such as S3 and EC2 to provide performance and cost benefits not possible with traditional ETL solutions.

As more and more teams building analytics infrastructure on AWS want to use Snowflake as their data warehouse, offering support for Snowflake was a natural next step for us. 

If you would like to explore building a Snowflake data warehouse with Etleap, you can sign up for a demo here.

 

New Features: Models and History Tables

I’m excited to tell you about two new features we’re launching today: Models and History Tables.

 

Models

Etleap has long supported single-source transformations through data wrangling. This is great for cleaning, structuring, and filtering data, and for removing unwanted data, such as PII, before it is loaded to the destination. Today, we’re announcing the general availability of models, which enable transformations expressed as SQL queries. Two primary use cases for models are combining data from different sources to build data views optimized for analytics, and aggregating data to speed up analytics queries.

Etleap models are Redshift tables backed by SQL SELECT queries that you define, running against data that has been loaded to Redshift. Etleap creates tables that are the result of these SELECT queries, and updates these tables incrementally or through full refreshes. Etleap triggers updates based on changes to dependent tables, or on a schedule.

6xabeajztw

 

 

History Tables

For regular pipelines into Redshift, Etleap fetches new and updated records from the source. Following transformation, new rows are appended to the destination table, and updated rows are overwritten. This update strategy is known as type-1 Slowly Changing Dimensions in data warehouse speak.

Sometimes it’s useful to be able to go back in time and query the past state of a record, or to be able to investigate how a record has changed over time. For this, Etleap now provides the ability to retain the history of a record collection. For this, the technique known as type-2 Slowly Changing Dimensions is often used. Here’s how it works in Etleap: An end-date column is added to the table. When a record is initially inserted into the destination table, this column’s value is null. Whenever the record is changed in the source, instead of overwriting the existing record in the destination table, a new row is appended instead with a null end-date value. The existing record’s end-date value is set to the new record’s update timestamp.

Starting today, history tables are available for all pipelines from sources that have a primary key and an update timestamp. To get a history table, check the ‘retain history’ box during single or batch pipeline setup.

 

retainhistorywizard

 

Want to see these features in action? Request a demo here!

Scaling Etleap with funding from First Round Capital, SV Angel, and more

Today we’re excited to share that we’ve raised $1.5M from First Round Capital, SV Angel, Liquid2, BoxGroup, and others to continue to scale our enterprise-grade ETL solution for building and managing cloud data warehouses.

ETL has traditionally been associated with expensive projects that take months of custom development by specialized engineers. We started Etleap because we believe in a world where analytics teams manage their own data pipelines, and IT teams aren’t burdened with complex ETL infrastructure and tedious operations.

Etleap runs in the cloud and requires no engineering work to set up, maintain, and scale. It helps companies drastically lower the cost and complexity of their ETL solution and improve the usefulness of their data.

Over the past few years we’ve spent a lot of time with analytics teams in order to understand their challenges and have built features for integration, wrangling, and modeling. It’s a thrill to see data-driven customers, including Airtable, Okta, and AXS, use them. Their analytics teams are pushing the boundaries of what’s possible today, and we’re hard at work building features to help bring their productivity to new levels.

 


 

Curious how Etleap can solve your analytics infrastructure challenges? Click here to get a demo of Etleap!