Data engineering · agentic AI · lakehouse migration

We lay the bricks for your Databricks lakehouse.

MegaBricksAI LLC designs, builds, and migrates data platforms on Databricks — from raw ingestion to governed, AI-ready data, one course at a time.

What MegaBricksAI does

We're a Databricks-focused consultancy. We build the pipelines that move and shape your data, the AI agents that put it to work, and the migration paths that get legacy platforms onto the Lakehouse — without the guesswork.

01 / Foundation

Data Engineering

Bronze, silver, gold pipelines built with Lakeflow, Auto Loader, and Unity Catalog — reliable data, end to end.

02 / Intelligence

AI & Genie

LangGraph agents, LangChain apps, and Genie spaces that let your teams ask questions in plain language.

03 / Transition

Data Migration

A disciplined six-course method — Analyze, Design, Execute, Activate, Enable, Turn Off.

3
MEDALLION LAYERS
6
MIGRATION COURSES
1
GOVERNED LAKEHOUSE
0
LEGACY LICENSES LEFT BEHIND

Why "bricks"? Every Databricks lakehouse is built the same way ours is named — in layers, set deliberately, each one load-bearing for the next. That's the whole philosophy: nothing skipped, nothing left loose.

Data engineering on the Lakehouse

We build the medallion architecture that turns raw, messy sources into data your business — and your AI agents — can trust.

Bronze

Raw ingestion

Streaming and batch sources landed as-is with Auto Loader — full history, schema drift handled, nothing lost.

Silver

Cleansed & conformed

Deduplicated, validated, joined across sources into a consistent shape teams can build on.

Gold

Curated & business-ready

Aggregated, modeled tables that power dashboards, reports, and AI agents directly.

Ingestion

Auto Loader and structured streaming for files, CDC feeds, and event streams.

Transformation

Lakeflow Declarative Pipelines for testable, declarative ETL with built-in expectations.

Orchestration

Databricks Workflows and Jobs to schedule, chain, and monitor every pipeline.

Governance

Unity Catalog for fine-grained access control, lineage, and a single data catalog.

Quality

Lakehouse Monitoring to catch drift, nulls, and anomalies before they reach a dashboard.

Performance

Liquid Clustering and Photon to keep queries fast as tables grow.

Agentic AI & Genie

We put your governed data to work with agents and conversational tools that sit directly on top of the Lakehouse.

Orchestration

LangGraph

Stateful, multi-step agent graphs for workflows that need memory, branching, and human-in-the-loop checkpoints — deployed on Databricks Model Serving.

Application framework

LangChain

Composable building blocks for retrieval-augmented generation, tool-calling, and LLM apps wired into your Unity Catalog data.

Conversational analytics

Genie

Natural-language questions answered directly against governed tables — no SQL required, with every answer traceable back to its source.

Self-serve scope

Data Spaces

Curated Genie spaces — a defined set of tables, instructions, and sample questions — so each team gets accurate answers within clear boundaries.

Illustrative — defining a Genie space
# genie_space.yaml — example shape only name: "revenue-ops" tables: - sales.gold.orders - sales.gold.customers instructions: "Always filter cancelled orders unless asked." sample_questions: - "What were last quarter's top 10 accounts by revenue?" - "Which region is growing fastest month over month?"

Data migration, the MegaBricksAI way

Every migration follows the same six courses — laid in order, never skipped — so the move to Databricks is predictable instead of risky.

01

Analyze

Inventory the current platform: workloads, dependencies, data volumes, and a cost baseline to measure against.

02

Design

Map the target architecture on Databricks — Unity Catalog structure, medallion layers, compute sizing — and a migration runbook.

03

Execute

Migrate code, data, and pipelines, with parallel runs against the legacy system to validate results.

04

Activate

Cut workloads over to Databricks and redirect downstream consumers, dashboards, and jobs.

05

Enable

Train your teams, document the new platform, and stand up monitoring so you're self-sufficient.

06

Turn off

Decommission the legacy system, retire licenses, and close out the project cleanly.

Modernizing data platforms

We move data, pipelines, and workloads off legacy warehouses and ETL tools and onto the Lakehouse.

SQL Server Databricks

Schema and stored-procedure logic re-platformed into Delta tables and Lakeflow pipelines.

Teradata Databricks

Large-scale warehouse workloads moved with minimal query rewrite and validated side by side.

Oracle Databricks

OLTP and reporting workloads separated, with reporting moved to gold-layer Lakehouse tables.

Snowflake Databricks

Coexistence or full migration, depending on your timeline, with Unity Catalog as the system of record.

SSIS / Informatica Lakeflow

Legacy ETL packages rebuilt as declarative pipelines with native change-data-capture.

On-prem Hadoop Databricks

Spark workloads lifted onto managed compute, with HDFS data migrated into Delta Lake.

Contact us

Tell us about your platform, your data, or the migration you're planning — we'll get back to you.

Address
MegaBricksAI LLC
980 N Michigan Ave Ste 1090 #277858
Chicago, IL 60611