Iteration and innovation fuel the data-driven culture at Mercado Libre. In our first post, we presented our continuous intelligence approach, which leverages BigQuery and Looker to create a data ecosystem on which people can build their own models and processes.

Using this framework, the Shipping Operations team was able to build a new solution that provided near real-time data monitoring and analytics for our transportation network and enabled data analysts to create, embed, and deliver valuable insights.

The challenge
Shipping operations are critical to success in e-commerce, and Mercado Libre’s process is very complex since our organization spans multiple countries, time zones, and warehouses, and includes both internal and external carriers. In addition, the onset of the pandemic drove exponential order growth, which increased pressure on our shipping team to deliver more while still meeting the 48-hour delivery timelines that customers have come to expect.

This increased demand led to the expansion of fulfillment centers and cross-docking centers, doubling and tripling the nodes of our network (a.k.a. meli-net) in the leading countries where we operate. We also now have the largest electric vehicle fleet in Latin America and operate domestic flights in Brazil and Mexico.

We previously worked with data coming in from multiple sources, and we used APIs to bring it into different platforms based on the use case. For real-time data consumption and monitoring, we had Kibana, while historical data for business analysis was piped into Teradata. Consequently, the real-time Kibana data and the historical data in Teradata were growing in parallel, without working together. On one hand, we had the operations team using real-time streams of data for monitoring, while on the other, business analysts were building visualizations based on the historical data in our data warehouse.

• This approach resulted in a number of problems:
• The operations team lacked visibility and required support to build their visualizations. Specialized BI teams became bottlenecks.
• Maintenance was needed, which led to system downtime.
• Parallel solutions were ungoverned (the ops team used an Elastic database to store and work with attributes and metrics) with unfriendly backups and data bounded for a period of time.
• We couldn’t relate data entities as we do with SQL.

Striking a balance: real-time vs. historical data
We needed to be able to seamlessly navigate between real-time and historical data. To address this need, we decided to migrate the data to BigQuery, knowing we would leverage many use cases at once with Google Cloud.

Once we had our real-time and historical data consolidated within BigQuery, we had the power to make choices about which datasets needed to be made available in near real-time and which didn’t. We evaluated the use of analytics with different time windows tables from the data streams instead of the real-time logs visualization approach. This enabled us to serve near real-time and historical data utilizing the same origin.

We then modeled the data using LookML, Looker’s reusable modeling language based on SQL, and consumed the data through Looker dashboards and Explores. Because Looker queries the database directly, our reporting mirrored the near real-time data stored in BigQuery. Finally, in order to balance near real-time availability with overall consumption costs, we analyzed key use cases on a case-by-case basis to optimize our resource usage.

This solution prevented us from having to maintain two different tools and featured a more scalable architecture. Thanks to the services of GCP and the use of BigQuery, we were able to design a robust data architecture that ensures the availability of data in near real-time.

Streaming data with our own Data Producer Model: from APIs to BigQuery
To make new data streams available, we designed a process which we call the “Data Producer Model” (“Modelo Productor de Datos” or MPD) where functional business teams can serve as data creators in charge of generating data streams and publishing them as related information assets we call “data domains”. Using this process, the new data comes in via JSON format, which is streamed into BigQuery. We then use a 3-tiered transformation process to convert that JSON into a partitioned, columnar structure.

To make these new data sets available in Looker for exploration, we developed a Java utility app to accelerate the development of LookML and make it even more fun for developers to create pipelines.