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This page lists selected projects and systems I have built or contributed to. Each entry includes a short, factual description and links to source code or live documentation where available.

Lucendex

A neutral, non-custodial execution layer for XRPL trading.

Repository: https://github.com/2pk03/lucendex
Website: https://lucendex.com

Lucendex is a non-custodial, deterministic routing engine for the XRPL decentralized exchange.
It indexes AMM pools and orderbook data, evaluates available paths, and produces quotes using a deterministic QuoteHash mechanism.

The service uses PostgreSQL and PL/pgSQL for indexing and routing logic, and provides an Ed25519-authenticated API access.

docAI Toolkit

A Python toolkit for document analysis workflows.

Repository: https://github.com/2pk03/docai
PyPI: https://pypi.org/project/docai-toolkit/

Provides utilities for loading documents, splitting and preprocessing text, integrating embeddings or ML-based processing, and preparing inputs for AI pipelines or further downstream processing.

Available as a published package on PyPI.

kaf-s3 Connector

A Kafka-to-S3 connector implemented in Python.

Repository: https://github.com/2pk03/kaf-s3
PyPI: https://pypi.org/project/kaf-s3-connector/

Consumes records from Kafka topics, batches them, and writes them to S3 (or compatible object storage) using configurable batching parameters and storage formats.

Published as a package on PyPI.

Scalytics-Federated / Schema→Iceberg Application

Internal system combining data ingestion, schema normalization and processing pipelines to produce “AI-ready” data views for analytics or ML.

Architecture integrates data from arbitrary source systems or message topics, normalizes schema, and writes unified results into an Iceberg-based data lakehouse.
Processing is performed via Apache Flink for streaming / batch workload support.

Apache Wayang (as committer and member of the PMC)

Project: Apache Wayang (incubating) — https://wayang.apache.org/ 

What Apache Wayang is:

Wayang is a unified data processing framework that allows developers to write data workflows in a platform-agnostic way. It translates logical plans into an intermediate representation (WayangPlan), then optimizes them and executes them across one or more processing engines — e.g. relational databases, batch engines, stream engines — without requiring users to write engine-specific code.

Why Wayang matters:

  • Enables cross-platform execution: same data flow code can run on different engines (PostgreSQL, Spark, Flink, etc.) depending on workload and environment. 

  • Provides cost and performance optimization: its optimizer selects the most efficient execution plan across platforms.

  • Supports federated or distributed data scenarios and heterogeneous data infrastructures, useful when data lives in multiple, different storage or processing systems. 

My contributions / involvement:
I contribute as a committer and PMC member of Wayang. I have worked on development, architecture and integration of Wayang, bridging its core engine with data-processing pipelines (batch/stream), data ingestion integrations, and use cases for data-lake / AI-ready datasets.

You can find a detailed write-up of my experience and discussion of performance implications in my blog post: What are performance implications of distributed data processing across multiple engines


If you need help with distributed systems, backend engineering, or data platforms, check my Services.

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