Services

Big data engineering and lakehouse architecture

GrowGenius designs data lake and lakehouse architectures that ingest, store and process large volumes of structured and unstructured data at enterprise scale. The stack spans Delta Lake and Iceberg table formats, real-time streaming with Kafka and Flink, distributed processing with Apache Spark on Databricks or Snowflake, and vector databases for AI and machine-learning workloads.

Table formatsDelta Lake · Apache Iceberg

StreamingApache Kafka · Apache Flink

ProcessingApache Spark on Databricks or Snowflake

For AI workloadsVector databases

What does a big data platform engagement include?

Data lake and lakehouse architecture using Delta Lake or Iceberg
Real-time streaming ingestion with Kafka and Flink
Distributed processing with Apache Spark
Data mesh and federated governance where ownership is distributed
Data quality and observability frameworks
Vector databases for AI/ML and retrieval workloads

Data lake or lakehouse — what is the difference?

A data lake stores raw files cheaply and flexibly, but on its own gives no transactions, no schema enforcement and no reliable updates — which is how lakes turn into swamps nobody trusts.

A lakehouse keeps the cheap open storage and adds a table format (Delta Lake or Iceberg) that brings ACID transactions, schema evolution and time travel. For most organizations this is now the default, because it removes the need to copy everything into a separate warehouse to get correctness.

When does an organization actually need this?

When volume, variety or latency breaks the warehouse: unstructured data that does not fit relational modelling, ingestion rates that batch loading cannot keep up with, or machine-learning workloads that need raw history rather than aggregates.

If none of those apply, a well-designed warehouse with disciplined ETL is the cheaper and more maintainable answer. Being told that is a legitimate outcome of the assessment.

How does the platform support AI workloads?

AI systems need what a lakehouse is good at: raw history for training, governed access for compliance, and vector storage for retrieval-augmented generation. Building the data platform with those requirements in view avoids the common outcome where an AI project stalls because the data it needs exists but cannot be reached or cannot be lawfully used.

Where personal data is involved, the access and retention controls are designed together with AI governance.

Delta Lake Iceberg Kafka Flink Spark Databricks Snowflake Vector DB