Modern Data Lakehouse from Scratch
Project Overview
At InferStack we architected and engineered a modern data lakehouse from scratch that unified the flexibility of a data lake with the structure and reliability of a data warehouse. Our team delivered the client with a single source of truth for their raw, semi-structured and structured data that formed the foundation for analytics, reporting and machine learning.
Live Project
Confidential / InternalTech Stack
The Challenges
The client’s data was stored in silos and was in different formats, making it hard to analyse and trust the results. Traditional data warehouses are too rigid and costly to scale to their required level, while a data lake does not provide the structure and governance needed to ensure trust in their results. The client needed to enable both traditional analytics and newer self-service querying while being able to store both batch and real-time data without creating two separate systems.
Our Solutions
InferStack built a modern data lakehouse that adds structure, schema, and ACID transactions over low-cost object storage to enable the client to get the reliability of a data warehouse at a data lake’s cost. We built the end-to-end pipeline that will process the raw data and progressively refine it to enable faster querying while storing the raw and transformed data at every stage. Our team established the processes and governance needed to manage and catalogue the metadata at scale.
Core Features & Capabilities
Ability to store both structured and unstructured data. layered data architecture (raw data, enriched data). ACID transactions.
reliability and cost-effective storage.
schema enforcement and evolution.
data cataloging and governance, and the ability to work with data in batch and real-time modes.
Measurable
Business Impact
The final product exceeded all initial KPIs, driving user engagement and streamlining operations for the client.
"Through this InferStack-led initiative, the client has been able to transition from their fragmented data landscape to a single unified repository that supports their current reporting needs while also being ready for their future ML needs. The query performance is faster, and they have achieved lower storage costs than using a traditional warehouse only."