Build a Data Lakehouse

Enterprises are caught in a costly architectural dilemma. Unstructured files, logs, and raw events are dumped into object storage (S3, ADLS), while structured business data is duplicated into expensive proprietary warehouses like Snowflake or legacy SQL engines. Data teams struggle with fragile synchronization jobs between the two systems, inconsistent governance, divergent metric definitions, and astronomical cloud storage and compute bills.

The open data lakehouse fundamentally unifies this architecture. By pairing open-source columnar Parquet storage with open table formats—Apache Iceberg or Delta Lake—you achieve transactional consistency (ACID), partition evolution without rewriting tables, historical time travel, and fast query performance using any computational engine (Databricks Photon, Trino/Presto, Spark, Snowflake, or DuckDB). By organizing data into a disciplined Medallion Architecture(Bronze raw ingestion Silver cleaned and joined Gold business marts), your entire organization queries a single, governed source of truth.

Codersarts provides complete lakehouse design, implementation, migration, and optimization services. Whether you are building a greenfield Databricks Lakehouse with Unity Catalog, transitioning an existing S3 data lake into Apache Iceberg with Apache Polaris or AWS Glue, implementing real-time streaming ingestion directly to Iceberg/Delta tables, or migrating legacy on-prem Hadoop/HDFS clusters to modern cloud object storage, our senior lakehouse architects build scalable, open, and audit-proof platforms.

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Is This You?

Data leaders, chief data officers, and enterprise architects routinely face these data platform hurdles:

• "We are paying twice for storage and compute—once for our S3 data lake and again for our cloud data warehouse."

  
  

  Duplicating data between object lakes and proprietary warehouses wastes money and requires constant sync maintenance. A lakehouse stores data once in open Parquet/Iceberg formats on your own storage, accessible by all query engines.

• "Our S3 data lake has become an unmanageable 'data swamp' with corrupt files and no ACID guarantees."

  
  

  Standard Parquet files lack transactional guarantees. Failed write jobs leave orphaned or partially written files, leading to corrupted analytics. Iceberg and Delta Lake provide atomic ACID transactions, ensuring that queries only see fully committed snapshots.

• "We want to avoid vendor lock-in and ensure any engine—Spark, Trino, Snowflake, or DuckDB—can query our data."

  
  

  Proprietary formats trap your data behind specific compute engines with unpredictable pricing models. Open table formats like Apache Iceberg and Delta Lake are supported across every major modern query engine, giving you total freedom of compute.

• "Our data queries are crawling because our tables have millions of small files and poor partition pruning."

  
  

  Without intelligent storage compaction and clustering, object storage I/O grinds queries to a halt. We implement automated compaction (OPTIMIZE), Liquid Clustering, and metadata min/max statistics that enable instantaneous data skipping.

• "We need to stream real-time events directly into analytical tables without creating massive file fragmentation."

  
  

  Traditional micro-batch streaming creates thousands of tiny files on object storage. We implement streaming ingest into Apache Iceberg and Delta Lake with automated background compaction, delivering sub-minute data freshness without degrading query speeds.

• "We lack unified data governance—permissions, column masking, and lineage are managed separately in 3 different tools."

  
  

  Fragmented security creates compliance risks. We unify your security model using Databricks Unity Catalog or open catalogs (Polaris, Gravitino), providing centralized Role-Based Access Control (RBAC), fine-grained column/row masking, and automated data lineage across all tables.

The Cost of Inaction: Dual-platform architectures double storage costs, introduce silent data discrepancies between lakes and warehouses, stall AI/ML initiatives that require raw data access, and trap enterprise data in proprietary vendor lock-in.

What a Production Data Lakehouse Delivers

An enterprise-grade open data platform engineered for scale, multi-engine interoperability, and unified governance:

• Disciplined Medallion Architecture: Multi-tier data lifecycle (Bronze raw ingestion Silver cleansed/conformed Gold aggregated business marts).

• Open Table Formats (Iceberg / Delta Lake): Full ACID transactional guarantees, partition evolution, schema evolution, and time-travel snapshot audits on open Parquet files.

• Unified Catalog & Data Governance: Centralized access control, column-level masking, automated data lineage, and audit logging via Databricks Unity Catalog, AWS Glue, or Apache Polaris.

• Multi-Engine Interoperability: High-speed query execution across Databricks (Photon), Apache Spark, Trino/Presto, Snowflake (External Iceberg Tables), and DuckDB from a single storage layer.

• Real-Time Streaming Ingestion: Sub-minute streaming from Kafka, Kinesis, or Debezium directly into Bronze lakehouse tables with automated background compaction.

• Storage Compaction & Clustering: Automated file layout optimization (Liquid Clustering, Z-Ordering, VACUUM maintenance) delivering up to 10x faster query runtimes.

• Complete IaC & Governance Runbook: Infrastructure as Code (Terraform) provisioning, catalog RBAC definitions, data dictionary, and an operational team runbook.

Iceberg vs. Delta Lake: Choosing the Optimal Open Table Format

Both Apache Iceberg and Delta Lake represent the pinnacle of modern lakehouse table formats. Codersarts helps you select and implement the architecture that matches your compute ecosystem, team skillsets, and cloud infrastructure:

Origin & Governance

Apache Iceberg: Apache Software Foundation (created at Netflix); completely vendor-neutral governance

Delta Lake: Linux Foundation (created by Databricks); open-source with Databricks enterprise backing

Catalog Architecture

Apache Iceberg: Explicit catalog abstraction (Apache Polaris, AWS Glue, Project Nessie, REST Catalog, Snowflake)

Delta Lake: Historically path/metastore-based; deeply unified with Databricks Unity Catalog

Partition Evolution

Apache Iceberg: Hidden partitioning; partition schemes can be updated (e.g., month to day) without rewriting historical data

Delta Lake: Requires generated columns or table rewrites for structural partition changes

Engine Ecosystem

Apache Iceberg: Superior cross-engine support across Snowflake, Trino, AWS Athena, BigQuery, StarRocks, Spark

Delta Lake: Unrivaled performance and feature depth within the Databricks & Apache Spark ecosystem

Clustering & Compaction

Apache Iceberg: Sorting, Z-Ordering, and partition compaction via Spark/Flink maintenance jobs

Delta Lake: Native Liquid Clustering, auto-compaction, and automated OPTIMIZE in Databricks

Real-Time Streaming

Apache Iceberg: Native support via Apache Flink and Spark Structured Streaming with Iceberg Sink

Delta Lake: First-class integration via Spark Structured Streaming and Delta Live Tables (DLT)

Best Suited For

Apache Iceberg: Multi-engine environments (Snowflake + Trino + AWS), strict open-source neutrality, AWS/GCP stacks

Delta Lake: Teams centered on Databricks, PySpark workloads, and unified lakehouse AI/BI platforms

Note: With Delta Lake UniForm (Universal Format), Delta tables can automatically generate Iceberg metadata, allowing your team to store data in Delta Lake while reading it seamlessly as Apache Iceberg in Snowflake or BigQuery.

The Medallion Architecture Blueprint

We design production lakehouses following the battle-tested Medallion Architecture, establishing clear data hygiene, auditability, and query performance at every stage:

1. Bronze Layer (Raw Storage): The raw landing zone preserving exact historical source data. Tables are append-only, capturing raw JSON payloads, timestamps, and metadata. Serves as an unalterable audit trail for historical reprocessing.

2. Silver Layer (Curated Data): Data is cleaned, enriched, and standardized. Nulls are handled, data types are explicitly cast, duplicate rows are resolved via MERGE INTO, and historical changes are tracked. Serves as the central repository for analytics engineers and ad-hoc query users.

3. Gold Layer (Aggregated Marts): Business-level dimensional models (Kimball star schemas) and aggregated summary tables optimized for extreme query performance. Clustered and compacted for instant consumption by executive dashboards, BI tools, and machine learning feature stores.

Core Lakehouse Engineering Capabilities We Deliver

1. Streaming Ingestion to Apache Iceberg & Delta Lake

• Low-latency streaming ingestion directly into open table formats without causing "small files" degradation.

• We configure Spark Structured Streaming or Apache Flink with Iceberg/Delta sinks:

o Ingesting high-velocity Kafka / Kinesis streams with sub-minute latency.

o Writing micro-batches into temporary staging files with automated background compaction (OPTIMIZE).

o Utilizing write-ahead logs and checkpointing for guaranteed exactly-once processing.

2. Unified Catalog & Data Governance (Unity Catalog / Polaris)

• We establish centralized metadata governance to eliminate fragmented security permissions:

o Databricks Unity Catalog: Centralized governance for tables, volumes (unstructured data), models, and dashboards with cross-workspace identity federation.

o Apache Polaris / AWS Glue: Open REST-based catalog governance providing centralized IAM-authenticated access for Iceberg tables across Snowflake, Trino, and Spark.

o Fine-grained access control: Column-level masking (hashing PII), row-level filtering based on user role, and automated end-to-end data lineage visualization.

3. Storage Compaction, Clustering & Data Skipping

• Raw object storage queries crawl without physical layout optimization.

• We implement automated maintenance pipelines:

o Liquid Clustering / Z-Ordering: Reorganizing Parquet data files along frequently filtered dimensions (e.g., tenant_id, event_date) to maximize Parquet metadata min/max data skipping.

o Automated Compaction: Scheduling recurring jobs that coalesce thousands of small 5MB files into optimal 128MB–512MB columnar files.

o VACUUM & Snapshot Management: Pruning expired table snapshots and orphaned files to reclaim terabytes of unnecessary cloud storage costs while preserving designated time-travel audit windows.

4. Multi-Engine Analytics & Snowflake Iceberg Integration

• Enable hybrid compute by connecting your cloud warehouse directly to your lakehouse storage:

o Configuring Snowflake External Iceberg Tables, allowing Snowflake users to run lightning-fast SQL queries against S3 Iceberg tables without paying Snowflake storage markup.

o Deploying Trino or DuckDB for fast, cost-effective ad-hoc exploratory querying directly against the Silver and Gold lakehouse layers.

What We Deliver (Tangible Engineering Assets)

Every lakehouse implementation engagement delivers an enterprise-ready, fully documented platform:

• Production Lakehouse Architecture & Tables: Fully configured Bronze, Silver, and Gold storage directories on AWS S3, Azure ADLS Gen2, or GCP Cloud Storage using Apache Iceberg or Delta Lake formats.

• Automated Ingestion & Transformation Pipelines: Tested ingestion jobs (batch and streaming via Spark Structured Streaming or Delta Live Tables) with automated schema validation and deduplication logic.

• Unified Catalog & Governance Setup: Configured metadata catalog (Unity Catalog, AWS Glue, or Apache Polaris) with role-based access controls, column masking, and automated lineage tracking.

• Storage Optimization & Maintenance Jobs: Scheduled notebooks or DAGs running file compaction (OPTIMIZE), Liquid Clustering/Z-Ordering, and snapshot retention cleanup (VACUUM).

• Infrastructure as Code (IaC): Complete Terraform scripts defining cloud storage buckets, IAM roles, KMS encryption keys, and catalog configurations for version-controlled deployment.

• Data Dictionary & Architecture Runbook: Comprehensive documentation outlining table schemas, Medallion lineage flows, disaster recovery procedures, and a 60-minute recorded team training session.

What We Need from You

To design and implement your data lakehouse platform efficiently:

• Source Data Inventory: Overview of data sources to be ingested (relational databases, SaaS APIs, event streams, CSV/JSON files, object storage).

• Cloud Infrastructure Access: Administrative or DevOps permissions on your cloud account (AWS, Azure, GCP) or existing Databricks workspace.

• Target Query Tools & Use Cases: Information on downstream consumers (BI tools like Tableau/Looker, data science notebooks, ad-hoc SQL engines like Trino or Snowflake).

• Data Volume & Growth Metrics: Current storage size (GB/TB/PB), daily ingestion volume, and required streaming vs. batch latency targets.

• Security & Compliance Requirements: Industry compliance standards (HIPAA, GDPR, SOC2), encryption key requirements (CMEK), and user access roles.

How It Works: Our 5-Step Lakehouse Implementation Process

We take lakehouses from architectural blueprint to production reality in 5 disciplined phases:

• Step 1: Architectural Blueprint & Format Selection (Days 1–3)

  
  

  We evaluate your data sources, query engines, and team skillsets. We select the optimal table format (Apache Iceberg vs. Delta Lake) and design the Medallion schema taxonomy and catalog hierarchy.

• Step 2: Cloud Storage & Catalog Infrastructure Provisioning (Days 4–6)

  
  

  We provision secure cloud storage buckets (S3/ADLS/GCS), configure KMS encryption, deploy the metadata catalog (Unity Catalog, Polaris, or Glue), and establish Terraform IaC automation.

• Step 3: Bronze & Silver Pipeline Implementation (Days 7–12)

  
  

  We build automated ingestion pipelines (streaming and batch) into Bronze landing tables. We build Silver curation models implementing schema validation, deduplication, and ACID MERGE INTO operations.

• Step 4: Gold Mart Modeling & Query Performance Tuning (Days 13–16)

  
  

  We build dimensional Gold marts, configure Liquid Clustering/Z-Ordering, implement automated compaction maintenance jobs, and connect downstream BI tools and query engines.

• Step 5: Governance Enforcement, Runbook Delivery & Handover (Days 17–20)

  
  

  We configure RBAC permissions and column masking, verify end-to-end data lineage, deliver the operational runbook and Terraform code, and conduct a technical team training session.

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Security, Governance & Data Sovereignty

Open lakehouses provide unmatched data security and complete data ownership:

• 100% Data Sovereignty: All data files reside strictly within your own cloud object storage accounts (AWS S3, Azure ADLS Gen2, GCP GCS). Data never transits through Codersarts infrastructure.

• Open Parquet Formats (No Vendor Lock-In): Data is stored in open Apache Parquet format. If you ever change cloud vendors or analytical engines, your raw storage remains 100% readable without expensive data migration projects.

• Customer-Managed Encryption (CMEK): Full end-to-end encryption at rest using AWS KMS, Azure Key Vault, or Google Cloud KMS with automated key rotation.

• Unified RBAC & Column Masking: Granular access policies managed centrally in Unity Catalog or Polaris, ensuring sensitive PII is masked dynamically based on the querying user's role.

• Strict Mutual Non-Disclosure Agreement (NDA): Comprehensive NDA executed before reviewing any data architectures, credentials, or proprietary business schemas.

• 100% Client Ownership: All Terraform code, transformation pipelines, table schemas, and documentation belong entirely to your enterprise.

Investment & Engagement Models

Transparent, fixed-price project tiers with guaranteed deliverables and zero hidden engineering fees:

Starter Lakehouse Foundation

Typical Scope: Single cloud lakehouse (Iceberg or Delta), Bronze/Silver/Gold pipeline for up to 5 core datasets, basic catalog.

Timeline: 2–3 Weeks

Deliverables Included: Cloud storage setup, Terraform IaC, Medallion pipelines, catalog RBAC, team handover.

Enterprise Databricks / Unity Platform

Typical Scope: Full Databricks Lakehouse, Unity Catalog governance, streaming ingestion, Delta Live Tables, Liquid Clustering.

Timeline: 3–5 Weeks

Deliverables Included: Production Databricks deployment, Unity Catalog federation, automated DLT pipelines, full runbook.

Multi-Engine Iceberg Architecture

Typical Scope: Cross-engine Apache Iceberg platform (Snowflake + Trino + Spark), Polaris/Glue catalog, streaming CDC ingest.

Timeline: 4–6 Weeks

Deliverables Included: Multi-engine architecture, streaming Iceberg sinks, external table configurations, team training.

What Drives the Cost?

• Table Format & Catalog Complexity: Single-engine Delta Lake with Unity Catalog vs. multi-engine Apache Iceberg with custom REST catalog integrations across Snowflake and Trino.

• Ingestion Velocity & Data Volume: Batch daily CSV/JSON ingestion vs. real-time sub-minute streaming from Kafka with high-throughput schema evolution.

• Dataset Scale & Migration Scope: Greenfield lakehouse setup vs. migrating petabytes of legacy data from on-premises Hadoop/HDFS or proprietary databases.

• Governance & Security Depth: Basic IAM role separation vs. multi-workspace identity federation with dynamic row/column-level masking and audit logging.

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Flexible Engagement Options

• Fixed-Price Project: Clear, bounded scope, fixed timeline, and guaranteed outcomes with zero billing surprises.

• Lakehouse Architect on Retainer: Dedicated monthly hours for designing new data domains, optimizing compaction jobs, and tuning query performance as your lakehouse scales. See Hire Developers.

Tech Stack & Tooling

• We engineer open lakehouses using the industry's premier platforms and standards:

Why Choose Codersarts for Lakehouse Implementation

• True Open-Source Independence: We don't push proprietary vendor agendas. We recommend the open format (Iceberg or Delta Lake) and catalog that best fits your long-term business strategy.

• Senior Distributed Systems Architects: Our engineers have architected petabyte-scale lakehouses for Fortune 500 enterprises, fintechs, and high-growth tech companies.

• Performance & Cost Obsessed: We tune physical storage layout from day one—leveraging compaction, Liquid Clustering, and data skipping to prevent bloated cloud storage and compute bills.

• End-to-End Analytics Continuity: We don't just build storage—we ensure your downstream BI tools (Tableau, Looker, Power BI) and AI notebooks query the lakehouse with sub-second response times.

• Fast Turnaround: Binding fixed-price quotes delivered within 24 hours; working Medallion prototypes operating in your cloud within the first two weeks.

• Comprehensive Handover: We deliver complete Terraform automation, architecture blueprints, and video training sessions so your internal team operates the platform with complete autonomy.

Where This Fits in Your Lakehouse Project

The data lakehouse serves as the central, unified storage and computation foundation for your entire data organization. Complementary Codersarts services:

• Build a Real-Time CDC Pipeline: Stream operational database change events from Debezium and Kafka directly into Bronze lakehouse tables in sub-seconds.

• Optimize Slow Spark or Databricks Jobs: Tune PySpark batch pipelines, eliminate shuffle spill, and resolve data skew across your lakehouse workloads.

• Build dbt Models for Your Warehouse: Transform Medallion tables with modular, version-controlled dbt models running natively on Databricks or Trino.

• Optimize Slow SQL Queries: Accelerate SQL queries running against Gold dimensional tables and eliminate remote disk spilling on Databricks SQL or Trino.

• Set Up Pipeline Orchestration: Coordinate complex Bronze-to-Silver-to-Gold Medallion pipelines using Apache Airflow or Dagster with automated retry handling.

• Build a Looker Semantic Layer: Connect governed Looker explores directly to Gold lakehouse dimensional marts for self-service business intelligence.

Building on Databricks specifically? See Implement Databricks. Need governance across the lakehouse? See Set Up a Data Catalog & Lineage.

Frequently Asked Questions

How much does a lakehouse implementation consultant cost?

Pricing depends on the platform choice (Databricks vs. open-source Iceberg on AWS/GCP), the number of source datasets, streaming vs. batch ingestion requirements, and catalog governance complexity. Starter lakehouse foundations with core Medallion pipelines are quoted at a competitive fixed price. Enterprise platform implementations and multi-engine Iceberg architectures are scoped transparently following an initial architecture review. We deliver a binding fixed quote within 24 hours.

How long does a data lakehouse implementation project take?

A starter lakehouse foundation with 3 to 5 core datasets typically takes 2 to 3 weeks. Comprehensive enterprise deployments involving Databricks Unity Catalog, streaming ingestion, Delta Live Tables, and multi-engine query access typically take 3 to 5 weeks.

What is the difference between a Data Lake, a Data Warehouse, and a Data Lakehouse?

A Data Lake stores raw files (Parquet, JSON, images) cheaply on cloud object storage (S3, ADLS) but lacks ACID transactions, schema enforcement, and fast BI query performance, often becoming an unmanageable "data swamp." A Data Warehouse (like Snowflake or BigQuery) offers fast SQL queries, security, and ACID transactions, but stores data in proprietary, expensive storage that cannot natively support AI/ML workloads or raw unstructured data. A Data Lakehouse combines the best of both worlds: it stores all data in open Parquet formats on cheap cloud object storage while using open table formats (Iceberg or Delta Lake) to deliver ACID transactions, schema enforcement, time travel, and sub-second warehouse-grade query performance.

Apache Iceberg vs. Delta Lake: Which one should our organization choose?

If your organization's data stack is heavily centered on Databricks, Apache Spark, and unified AI/BI workloads, Delta Lake (with Unity Catalog) offers the deepest feature integration, native Liquid Clustering, and automated optimization. If your organization operates in a multi-engine ecosystem (querying the same data across Snowflake, Trino, AWS Athena, and Spark) or requires strict vendor neutrality, Apache Iceberg is the industry standard. Furthermore, Delta Lake UniForm allows Delta tables to generate Iceberg metadata automatically, giving you the benefits of both. We evaluate your existing tools to recommend the optimal path.

What is the Medallion Architecture and why is it recommended?

The Medallion Architecture is a data design pattern that organizes tables into three logical quality tiers: Bronze (raw, append-only source data preserving exact historical fidelity), Silver (cleaned, validated, enriched, and conformed data with deduplication and ACID upserts), and Gold (aggregated, business-level star schemas optimized for high-speed BI reporting and AI feature stores). This tiered approach guarantees auditability, isolates raw data ingestion from business logic, and ensures high query performance.

How do you stream data into Apache Iceberg or Delta Lake without creating millions of small files?

High-frequency streaming writes into object storage naturally create the "small files problem," where thousands of tiny files degrade query performance. We resolve this by configuring Spark Structured Streaming or Apache Flink with optimized micro-batch windows (e.g., 30–60 seconds) combined with automated background file compaction (OPTIMIZE in Delta Lake, compaction actions in Iceberg). This writes data rapidly to streaming buffers while continuously consolidating data into optimal 128MB–512MB columnar files in the background.

Can we query our data lakehouse using Snowflake or Trino without moving the data?

Yes. That is the core superpower of an open lakehouse. Using Snowflake's native External Iceberg Tables, Snowflake can query Apache Iceberg tables stored in your own AWS S3 or Azure ADLS buckets with near-native performance. Similarly, distributed SQL engines like Trino, Presto, or DuckDB can query the exact same Parquet files concurrently. You pay only for compute, completely eliminating proprietary storage markup.

What is Unity Catalog and how does it simplify lakehouse governance?

Databricks Unity Catalog provides a centralized governance layer across all data assets (tables, files/volumes, machine learning models, dashboards) within a Databricks lakehouse. It provides a single permission model (ANSI SQL GRANTs), cross-workspace identity federation, automated end-to-end data lineage down to the column level, dynamic data masking for sensitive PII, and centralized audit logging.

What happens to our historical data during a migration to an open lakehouse?

We ensure zero data loss during migrations. Existing raw Parquet files can be converted into Iceberg or Delta Lake tables in-place using metadata conversion commands (CONVERT TO DELTA or Iceberg table migration procedures) without rewriting underlying data files. For proprietary warehouses, we establish automated historical backfill pipelines that load historical records into the Bronze layer while verifying row-level checksums against legacy sources.

Want to learn technical lakehouse architecture and implementation best practices?

Read our technical guides: Iceberg vs Delta Lake comparison, Medallion architecture implementation patterns, and how to stream to Apache Iceberg on the Codersarts Discuss forum.

Build Your Open Data Lakehouse This Week

Stop paying exorbitant proprietary warehouse fees and dealing with chaotic data lakes. Hire a senior Codersarts lakehouse implementation consultant to architect an open, high-performance data lakehouse using Apache Iceberg, Delta Lake, and Medallion architecture.

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