Unleashing Data Insights: Transforming Analysis with Milestoning and Time Travel
A Journey Through Milestoning and Time Travel
In the dynamic realm of data engineering and data science, deriving meaningful insights from vast datasets presents a formidable challenge. Picture a scenario where a database houses hundreds of millions of rows, and your mission is to extract valuable insights from this data ocean. This very challenge confronted me during my job recently. This blog chronicles the process of identifying, addressing, and capitalizing on this challenge. While I’ll abstain from delving into intricate code details, I’ll focus on the strategic logic behind the transformative solution.
The Data Conundrum
Every journey begins with recognizing the underlying issue. In our case, we were grappling with the task of conducting efficient and impactful analysis on an immense database. Conventional methods were faltering, leading to bottlenecks, inefficiencies, and incomplete insights. The staggering volume of data made it a Herculean effort to track changes and variations over time. This realization served as the pivotal moment that set us on a path to discover an innovative solution. We needed a way to keep track of the historical changes, a way to see a particular data point at any point of its lifecycle.
We, needed to travel through time.
The Pursuit of Innovation
The next step was to find a feasible way to do this. We needed a way to keep track of the data throughout its lifecycle, we needed to keep track of every change that affected a particular data point. An effective way of doing this would be to take a snapshot of the data whenever it is modified. This is essentially what milestoning means. A way to milestone data and travel through time to those milestones.
Architecting the Data Pipeline
Now we needed a way to implement this. After some initial research we found out that there was a product which offered exactly this service, Apache Iceberg.
Apache Iceberg is a high-performance format for huge analytic tables which allows one to easily query big data tables. Iceberg provides out of the box functionality for time travelling and rollbacks in case of any data corruption.
One very useful feature of Iceberg that compelled us to go ahead with it instead of other market leaders was its robust compression strategies. When working with terabytes of data it is critical to take into account the stress on the storage services. One needs to keep in mind that if the data read/write strategy is sub-optimal a small change could result in massive data rewrites.
In our use case if we just took a snapshot of the table or row whenever it is modified we would’ve quickly racked up storage costs in the millions of dollars.
I will not go into detail about how Apache Iceberg compresses data in this blog but just know that instead of just copying the entire row or table as is it just keeps track of the change made.
Illuminating Insights
Implementing milestoning and time travel precipitated a cascade of insights previously concealed by data volume. Our newfound ability to navigate historical changes delivered an intricate understanding of data evolution over time. This temporal vantage point enabled us to unearth trends, anomalies, and patterns that evaded traditional methods. Suddenly, the past, present, and future of our data converged, fostering enlightened decision-making and strategic foresight.
Conclusion
In the dynamic landscape of data engineering and data science, challenges are par for the course. However, innovation, strategic thinking, and the embrace of cutting-edge technologies empower us to surmount even the most formidable obstacles. Our journey, from pinpointing a challenge to crafting a dynamic solution and reaping the rewards of unprecedented insights, underscores the potency of resolve and creativity.
I hope my experience helps you to think outside the box and utilize novel techniques to gain additional insights on your data.
