Fluctuating, large-scale financial services data with Google Cloud Dataflow
The quest for the more effective way to process data is encouraging; at Connectikpeople, we also believe that, elasticity to the entire process and ensuring accuracy, scale, performance and cost efficiency is a game changer.
We live henceforth in a
connected world where, the erratic nature of financial market data volumes,
often driven by volatility, intensifies the challenges when it comes to scaling
and posting data when and where it’s needed for daily trade reconciliation,
settlement and regulatory reporting.
In fact, these activities must
be predictable, repeatable and measurable to yield maximum value.
Therefore, you need the speed,
flexibility, scalability and efficiency beyond limits of ETL (Extract,
Transform and Load (ETL) activities), as the amount of transactions grows
faster than they can process it.
In our connected world with
data as the most critical, data can come from anywhere and in any format,
creating a series of labor, time and intellectual challenges.
As a result, a series of
powerful tools for a myriad of data transformation duties is indispensable.
The Google Cloud
Dataflow can simplify the mechanics of large-scale
transformation and supports both batch and stream processing using the same
programming model.
In this latest white paper, Connectikpeople has captured some of the main
concepts behind building and running applications that use Dataflow. Google
Cloud Dataflow can allow you to focus on data processing tasks and not cluster
management.
Dataflow can automatically
scale up or down horizontally as much as needed for your exact processing
requirements. As a result, you’ll be able to focus on the data transformations
you need to make rather than on the processing mechanics themselves.
