
Ripple Engineering » Data
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Get technical Highlights from the Ripple Engineering on data science and data transformation. Ripple is the only enterprise blockchain company today with products in commercial use by hundreds of customers across 55+ countries. These businesses have access to alternative liquidity solutions through Ripple's global network, which uniquely uses the XRP Ledger and its native digital asset XRP to..
Ripple Engineering » Data
7M ago
Introduction: Embracing the Future with Ripple's Data Platform Migration
Welcome to a pivotal moment in Ripple's data journey. As leaders at the intersection of blockchain technology and financial services, we're excited to share a transformative step in our data management evolution. We recently embarked on a significant data platform migration, transitioning from Hadoop to Databricks, a move motivated by our relentless pursuit of excellence and our contributions to the XRP Ledger's (XRPL) data analytics.
Our new system on Databricks stands as a beacon of innovation, shedding the constraint ..read more
Ripple Engineering » Data
1y ago
Why do we need a Data Platform?
At Ripple, different business units require a platform to quickly and easily turn their data into insights and compelling customer experiences. For Ripple's product capabilities, the Payments team of Ripple, for example, ingests millions of transactional records into databases and performs analytics to generate invoices, reports, and other related payment operations.
A lack of a centralized system makes building a single source of high-quality data difficult. It can lead to expensive, slow, and unmaintainable systems. The key aspect of any business-centri ..read more
Ripple Engineering » Data
4y ago
At Ripple, we are moving towards building complex business models out of raw data. To do this successfully, we need to automate our historically manual processes. Even with a digital-first approach, many of our internal processes were done by hand, making them great candidates to be automated.
A prime example of this was the process of managing our data transformation workflows. Our data analysts used to schedule queries on BigQuery for transformation workflows and test the transformed data manually. We did not have a single tool that would automate the building, compiling, testing and docume ..read more
Ripple Engineering » Data
4y ago
The Ripple Data Engineering team is expanding, which means higher frequency changes to our data pipeline source code. This means we need to build better, more configurable and more collaborative tooling that prevents code collisions and enforces software engineering best practices. To ensure the quality of incoming features, the team sought to create a pipeline that automatically validated those features, build them to verify their interoperability with existing features and GitLab, and alert the respective owners of any failures in the pipeline. These are pretty standard DevOps require ..read more
Ripple Engineering » Data
4y ago
In our last liquidity monitoring post, we introduced the concept of dislocation as a way to measure the price competitiveness of an XRP-fiat pair. In this post, we introduce the companion depth metric and combine both metrics into a data visualization for assessing liquidity performance.
Depth
Dislocation tells us how competitive an exchange’s XRP prices are, but it ignores the important quantity component of liquidity. Recall that the executed price against an order book is size-dependent. Denser order books give more competitive pricing for larger orders than thin order books. We want to kn ..read more
Ripple Engineering » Data
4y ago
In a recent post, my teammate Jennifer Xia outlined our motivation and initial direction for tracking XRP liquidity in support of RippleNet’s On-Demand Liquidity (ODL) service. ODL leverages the digital asset XRP to facilitate cross-border payments by sourcing destination currencies right at the time of payment. Jennifer’s post introduces the concept of order books and defines the implied FX rate or the FX rate implied by a pair of trades bridged through XRP.
As we scaled the service, comparing rates was not enough. Our team often wanted to understand why implied FX rates differ from spot FX ..read more
Ripple Engineering » Data
4y ago
In part 1 of our series, we discussed the motivation and framework behind measuring the environmental cost of a payment transaction. We also talked about the methodology for credit card networks. In this post, we continue to cover the environmental impact model for Cryptocurrencies and paper money.
Cryptocurrencies
In order to have a mathematical model to determine which ASIC mining rig models are used in a rolling time frame, we must make a few assumptions:
As long as a model can guarantee profitability in electrical terms, it will be used. This means the cost of electricity for running the ..read more
Ripple Engineering » Data
4y ago
Every time we get paid or buy something, we inevitably face the decision of how to pay. But it’s not always clear how those decisions affect the environment. One of this year’s most exciting initiatives at Ripple is our commitment to be carbon net-zero by 2030.
As we set out to evaluate our own carbon footprint, we also wanted to understand the environmental impact of other payment modes such as other cryptocurrencies, credit cards, and cash. It’s imperative that as consumers and community members, we have the information to make environmentally conscious decisions about how we pay.
Elenabsl ..read more
Ripple Engineering » Data
4y ago
Ripple’s mission is to enable payments for everyone, everywhere. One of the ways we look to achieve this mission is through building a product called RippleNet’s On-Demand Liquidity service, or ODL. Traditionally, businesses that facilitate international payments need to hold pre-funded accounts in destination currencies, an expensive and inefficient process. As an alternative solution, ODL leverages the digital asset XRP to source destination currencies right at the time of payment.
Liquidity is the ability to buy and sell desired quantities of an asset without impacting the price significan ..read more
Ripple Engineering » Data
4y ago
Swaapnika Guntaka
Managing one cloud infrastructure isn’t easy. Dealing with multiple clouds is even more challenging. Coming up with a way to retrieve encrypted secrets from a cloud-based security solution using a workflow that involves applications and services in both clouds (AWS, GCP) and doing it all securely—that’s an immense challenge.
For Ripple's Data Engineering team to solve this problem, we not only had to sort through all of those intricate architectural details, we also had to consider the existing infrastructure and long-term goals, and adopt best practices at the same time.. Bu ..read more