databricks Lakehouse
The Next-Generation Data & AI Architecture

One platform for all your data, analytics & AI.

Unify data engineering, data science, machine learning, and business analytics on an open, scalable lakehouse architecture powered by Delta Lake and Apache Spark™.

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99.99% Uptime SLA
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lakehouse_pipeline.py
Spark 3.5 Active
# Stream real-time events into Delta Lake Table
from pyspark.sql import SparkSession
from databricks.sdk import WorkspaceClient
df = spark.readStream \
  .format("kafka") \
  .option("subscribe", "telemetry-events") \
  .load()
# Execute Delta ACID Merge Operation
df.writeStream \
  .format("delta") \
  .outputMode("append") \
  .option("checkpointLocation", "/mnt/delta/checkpoints") \
  .start("/mnt/delta/events_gold")
Processing Speed:
1.48 PB / sec

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Architectural Superiority

Built from the ground up for high-throughput distributed scale.

Replace complex, fragmented legacy data silos with a single unified platform for data engineering, streaming, SQL analytics, and generative AI.

Delta Lake ACID Transactions

Bring reliability, performance, and governance to your cloud data lake with open-source storage layer ACID transactions, scalable metadata processing, and unified streaming/batch data processing.

OPTIMIZE events_gold ZORDER BY (device_id, timestamp);

Unity Catalog

Fine-grained central governance for data and AI assets across cloud providers with automated column-level lineage tracking.

Mosaic AI

Train, fine-tune, deploy, and evaluate custom LLMs and RAG applications securely on your enterprise data assets.

Photon Engine

Next-generation C++ vectorization engine providing up to 12x performance improvements for modern SQL workloads.

Automated Orchestration & Delta Live Tables

Build reliable data pipelines effortlessly with automatic lineage generation, automated testing, quality metrics, and declarative ETL specifications.

Data Quality Enforcement Passed 0.02ms latency target

Developer First

Notebooks, SQL Editors, & IDE Integrations.

Seamlessly switch between Python, SQL, Scala, and R within the same collaborative workspace. Version control natively integrated with GitHub and GitLab.

  • Co-authoring with real-time multi-user cursor presence
  • Automated MLflow model logging & tracking
  • Native VS Code & PyCharm extension support
# PySpark Structured Streaming with Delta Live Tables
import dlt
from pyspark.sql.functions import *
@dlt.table(comment="Cleaned sensor reading stream")
def clean_sensor_data():
return (
dlt.readStream("raw_iot_events")
.filter(col("temperature").isNotNull())
.withColumn("processed_at", current_timestamp())
)
Cluster: i3.xlarge (16 Cores) Job Succeeded (1.2s)
12x
Faster SQL Query Execution
99.99%
Enterprise Platform Uptime SLA
70%
Reduction in TCO vs Data Warehouses
10k+
Active Enterprise Customers

Targeted Workloads

Tailored for every team in your data organization.

01

Data Engineering

Automate complex batch and streaming ETL workflows with Delta Live Tables and built-in failure recovery mechanisms.

02

Data Science & ML

From experiment tracking with MLflow to real-time feature store registration and serverless GPU model serving.

03

Business Warehousing

Run serverless SQL queries directly on your cloud object storage with sub-second response times and full ANSI compliance.

Transparent Unit Economics

Pay only for compute capacity you use.

Simple pay-as-you-go pricing calculated in Databricks Compute Units (DBUs).

Standard

Essential lakehouse capabilities for small analytics teams.

$0.07 / DBU hour
  • Open Delta Lake Storage
  • Managed Apache Spark
  • Interactive Notebooks
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Most Popular

Premium

Advanced governance, serverless SQL, and fine-grained security.

$0.15 / DBU hour
  • Everything in Standard
  • Unity Catalog Governance
  • Serverless SQL Data Warehouse
  • Column & Row Level Security
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Enterprise

Mission-critical security, HIPAA compliance, and custom SLAs.

Custom
  • Dedicated Private Link Cluster
  • Customer Managed Keys (BYOK)
  • 24/7 Priority Support & SLA
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Frequently Asked Questions

Got questions? We've got answers.

What is a Lakehouse architecture?

A lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the ACID transactions and data management capabilities of data warehouses.

How does Databricks charge for compute?

Databricks charges based on Databricks Compute Units (DBUs), a unit of processing capability per hour. You only pay for the exact virtual machine runtime your processing jobs consume.

Can I run Databricks in my existing AWS, Azure, or GCP cloud account?

Yes! Databricks deploys natively inside your own cloud account (AWS VPC, Azure VNet, or GCP VPC), ensuring your primary data never leaves your secure storage boundary.

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