What is Databricks?
Databricks is a unified analytics platform that combines data engineering, data science, machine learning, and business intelligence in a collaborative lakehouse architecture. Built on Apache Spark and Delta Lake, it provides a cloud-native platform for processing and analyzing massive datasets.
Databricks pioneered the lakehouse architecture, eliminating data silos and enabling all analytics workloads on a single platform. It supports collaborative notebooks, automated MLOps, and enterprise-grade security while providing the performance and reliability needed for production workloads.
Databricks Cost Calculator
Lakehouse Architecture
Databricks pioneered the lakehouse architecture, combining the best of data lakes and warehouses in a unified platform that supports all analytics workloads with ACID transactions and performance optimization.
Unified Storage
Delta Lake provides ACID transactions on cloud storage
Multi-Modal Analytics
BI, ML, and data engineering on the same platform
Open Standards
Built on Apache Spark, Delta Lake, MLflow
# Lakehouse in action - unified analytics
# Stream processing with Delta Lake
df = spark.readStream .format("kafka") .option("kafka.bootstrap.servers", "localhost:9092") .load()
# ACID writes to data lake
df.writeStream .format("delta") .outputMode("append") .table("lakehouse.events")
# Immediate analytics on streaming data
daily_metrics = spark.sql("""
SELECT date, event_type, COUNT(*)
FROM lakehouse.events
WHERE date = current_date()
GROUP BY date, event_type
""")
# ML training on same dataset
from databricks.automl import regress
model = regress(
dataset=spark.table("lakehouse.events"),
target_col="conversion_rate"
)Platform Components
Data Engineering
- • Delta Live Tables for ETL
- • Auto Loader for ingestion
- • Data quality monitoring
- • Workflow orchestration
Machine Learning
- • MLflow for ML lifecycle
- • AutoML capabilities
- • Feature Store
- • Model serving
Data Science
- • Collaborative notebooks
- • Multi-language support
- • Built-in visualizations
- • Version control
Business Intelligence
- • SQL Analytics endpoints
- • Dashboards and reports
- • BI tool integration
- • Photon acceleration
Governance
- • Unity Catalog
- • Data lineage tracking
- • Fine-grained access control
- • Audit logging
Compute
- • Auto-scaling clusters
- • Serverless compute
- • GPU support
- • Spot instance optimization
Real-World Examples
Netflix
Media & Entertainment
Processes 100+ PB of data daily for personalization, content optimization, and quality of experience analytics across global streaming infrastructure.
Comcast
Telecommunications
Unified 60+ data sources into lakehouse architecture, enabling real-time customer experience optimization and predictive analytics for 30M+ customers.
Shell
Energy
Processes IoT sensor data from 45K+ gas stations globally, enabling predictive maintenance and operational efficiency with 25% reduction in downtime.
Regeneron
Pharmaceutical
Accelerates drug discovery with genomic data analysis, processing 500K+ whole genome sequences for precision medicine and clinical trials.
Delta Lake Capabilities
ACID Transactions
Atomic operations ensure data consistency even with concurrent reads/writes
Time Travel
Query historical versions of data for audit trails and debugging
Schema Evolution
Safely evolve table schemas without breaking downstream applications
# Delta Lake operations
from delta.tables import DeltaTable
# ACID updates - millions of records
deltaTable = DeltaTable.forName(spark, "sales.orders")
deltaTable.update(
condition = col("status") == "pending",
set = {"status": lit("processed")}
)
# Time travel queries
df_yesterday = spark.read.format("delta") .option("timestampAsOf", "2024-01-01") .table("sales.orders")
# Schema evolution
new_data.write.format("delta") .option("mergeSchema", "true") .mode("append") .saveAsTable("sales.orders")
# Optimize performance
deltaTable.optimize().executeZOrderBy("customer_id")Best Practices
✅ Do
- •Use Delta Live Tables for declarative ETL pipelines with automatic dependency management
- •Implement proper partitioning and Z-ordering for query performance optimization
- •Use Unity Catalog for centralized governance and fine-grained access control
- •Leverage MLflow for end-to-end ML lifecycle management and model registry
- •Enable auto-scaling and use job clusters for cost optimization
❌ Don't
- •Use all-purpose clusters for production ETL jobs - prefer job clusters for cost efficiency
- •Skip data quality monitoring - use expectations in Delta Live Tables
- •Ignore cluster right-sizing - monitor DBU usage and optimize instance types
- •Mix transactional and analytical workloads on the same clusters
- •Forget to enable Photon for SQL analytics workloads requiring high performance