Data collation refers to the process of gathering, organizing, and compiling data from various sources into a single, comprehensive dataset for analysis. This may involve collecting data from internal sources, such as customer databases, as well as external sources, such as ad network performance reports, social media, or third-party data enrichment providers.

Data collation is an important step in the marketing data process because it allows marketers to gain a complete view of their target audience, identify trends, and make informed decisions based on accurate data. It also helps ensure that all data is consistent, accurate, and up-to-date, which is essential for effective analysis and decision-making. 

As data volumes increase, the time it takes to do the collation also increases, and this becomes more pronounced with larger volumes. Organizations need to understand their options here to keep time and cost down. Many companies are applying the same technologies and methodologies to data collation as they used in the past, and may not be aware of the hidden ‘taxes’ they are now paying.

In this spirit, Aqfer conducted a benchmarking project to compare the Aqfer approach to data collation against one of the leading methods of collating data. We wanted to answer the following question:  what would the typical solution to data collation look like for the average company doing it themselves, and what would it look like working with Aqfer?  

For this exercise, we compared an industry-standard approach, using Spark on AWS Elastic Map Reduce (EMR) to accomplish the task, to what it looks like when working with Aqfer. The exercise measures the time it would take to collate two identical data sets, one using the Aqfer approach – GoLang on ECS (Go) – vs using Spark on EMR (Spark).

The results are nothing short of eye-opening.  As Spark collation jobs get bigger, the ‘waste’ as represented by the time to complete the job grows exponentially bigger (where the ‘waste’ is represented by the blue shading). Keep in mind, we ran this test at a max data set size of about 38GB.  For those sitting on 100GB or even 1TB data sets, the time discrepancy between Spark and Go is even more pronounced.

Let’s take a look at a graph of the benchmark project:

Reason To Believe

Why did we build our own Big Data processing engine? To put it simply, we recognized that many of the solutions out there were built for what we’ll call Web 2.0. Today’s cloud architectures are rapidly moving into ‘3.0’, with the data collation issue described above as one of many use cases that need a faster, more efficient approach. 

More specifically, today’s business architectural needs create larger and larger performance issues. Case in point: Relative to GoLang, Spark processes data substantially slower as volumes get bigger in the modern cloud architectures and in the deployments that businesses require. And so our value add is that we get comparably faster as the problem gets bigger. We are already built for data volumes that are some of the biggest in the world.  In fact, we count among our customer set multiple companies who process record volumes in the trillions, and who enjoy the advantages that our big data processing engine provides for them. Because most of the cost of Big Data processing comes from collation efforts, it’s critical to cut collating and processing time down exponentially, in order to get you that much faster to market-leading insights.

At a data collation job size of approximately 500MB, the difference in speed between Spark and GoLang is notable – about a 5x slower time to collate between Spark and Go.  At 18GB, Go is almost 9 times faster.  And at 38GB, Go is 16 times faster.

Do More With Aqfer

Aqfer continues to revolutionize how MSPs support today’s data-driven marketers and advertisers. Our Marketing Data Platform-as-a-Service makes it faster, easier, and cheaper to overcome today’s most pressing marketing data collection and management challenges. As demonstrated by the cost efficiencies outlined here, Aqfer is committed to constantly improving our products to ensure our clients have the tools they need to scale their solutions to address challenges both now and in the future. 

Interested in learning more about how Aqfer’s solutions have supported our clients? Visit our Practical Applications resource page for more detailed insight into how our clients have used MDPaaS to enhance identity resolution, universal tag management, analytics and attribution, and more.