Migrating to Databricks helps accelerate innovation, enhance productivity and manage costs better with faster, more efficient infrastructure and DevOps

Discover the benefits of migrating from Hadoop to the Databricks Lakehouse Platform — one open, simple platform to store and manage all your data for all your analytics workloads.

While reducing infrastructure and licensing costs is one major advantage, the Databricks Lakehouse Platform has the speed and scale to handle all your critical use cases — helping you meet your SLAs, streamline operations and improve productivity.

Why Migrate with Databricks?
Forrester TEI Study finds 417% ROI for companies switching to Databricks.

47%

Cost-savings from retiring
legacy infrastructure

Retire legacy infrastructure and adopt an open and elastic cloud-native service that doesn’t require excess capacity or hardware upgrades.

5%

Increase in revenue with
Data-driven innovation

Use all enterprise data to build new data products and increase operational efficiencies with powerful artificial intelligence and machine learning capabilities.

25%

Increase in data team
productivity

Minimize the DevOps burden with a fully-managed, performant and reliable 
data and analytics platform.

Cloud-based
Build an open, simple and collaborative lakehouse architecture with Databricks

Cost-effective scale and performance in the cloud

Easy to manage and highly reliable for diverse data

Predictive and real-time insights to drive innovation

Lakehouse
Build an open, simple and collaborative lakehouse architecture with Databricks

Easy to manage
Reliability, security, and performancefor your data lake and all types of data

Massive-scale
On-demand availability and elastic autoscale with optimized Apache Spark

AI-enabled innovation
A collaborative environment for practitioners to run all analytic processes in one place, and manage ML models across the full lifecycle

Enhance productivity

Lower cost at scale

Innovate faster

Databricks Lakehouse Platform

Simple
Unify your data, analytics, and AI on one common platform for all data use cases

Open
Unify your data ecosystem with open source, standards, and formats

Collaborative
Unify your data teams to collaborate across the entire data and AI workflow

Technology mapping Hadoop to Databricks

Data Eng, ML
(Spark)
Databricks jobs/ Delta Lake
(Highly tuned Spark engine: faster, less
compute, one-stop-shop)

ETL, SQL
(Hive, Impala)
Databricks jobs/ Delta Lake/ Spark SQL
(Highly tuned Spark engine: faster, less
compute, one-stop-shop)

Real-time Event Processing
(Storm/Spark)
Databricks Structured Streaming
(Spark Structured Streaming + Delta Lake:
Streaming + Batch ingest)

Batch Process
(MapReduce)
Databricks Spark jobs
(orders of magnitude faster – but may need
manual work)

Scalable apps on Columnar store
(Hbase)
Databricks Spark integrates
w/Hbase on cloud

(Alternatively: use cloud data stores well
integrated with Databricks)

Use Case
  1. As a global CPG company, 
 Reckitt struggled with the 
 complexity of forecasting 
 demand across 500,000 stores
  2. They process over 2TB of data 
 every day across 250 pipelines
  3. Hadoop infrastructure proved to be complex, cumbersome and costly to scale. This legacy 
 system also struggled with performance.
Why Databricks
  1. A unified platform for data 
 science, engineering and business analysts to quickly innovate and deliver 
 ML-powered insights
  2. Delta Lake improved cost optimization and storage space with extreme data compression
Impact
  1. 10x more capacity to support business volume
  2. 98% data compression from 80TB to 2TB, reducing operational costs
  3. 2x faster data pipeline 
 performance for 24x7 jobs
Use Case
  1. Viacom18 needs to process 
 terabytes of daily viewer data to optimize programming
  2. Their on-premises Hadoop data lake was unable to process 90 days of rolling data within SLAs, limiting their ability to deliver on business needs
Why Databricks
  1. Azure Databricks provides fully managed autoscale clusters in the cloud that simplify infrastructure management
  2. Delta Lake caching significantly accelerated query speeds
  3. Collaborative notebooks with built-in ML libraries enable teams to innovate faster
Impact
  1. Significantly lowered costs with faster query time and less devops despite increasing data volumes
  2. Improved team productivity by 26% with a fully managed platform that supports ETL, analytics and ML at scale
Use Case
  1. Sam’s Club needs to process daily bakery data from all their stories to predict food spoilage.
  2. They were running 10+ large hadoop clusters, multiple large instances of Teradata and numerous SQL databases.
  3. This infrastructure was costly, hard to manage and unable to deliver granular daily forecasts in a timely fashion.
Why Databricks
  1. Centralized their data analytics architecture on Azure Databricks
  2. Delta Lake significantly reduced query speeds compared to legacy Teradata and Hadoop enabling real-time forecasts
  3. Collaborative workspaces improved productivity across 10+ workspaces, 100+ users, 1000+ notebooks
Impact
  1. Reduced infrastructure costs by $900K
  2. Fresh processing analysis went from 7 hours to 40 minutes
  3. 10% reduction in fresh food spoilage due to improved forecast (~$100M/year)
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Partner ecosystem

Featured partners

Privacera product demo for
Hadoop migration data privacy
compliance on Databricks
Manage big data pipelines
in the cloud

Databricks and StreamSets have partnered
to bring rapid data pipeline design and testing
to critical cloud workloads
Watch now

Migrating Hadoop analytics to Spark 
in the cloud without disruption
Accelerate your Hadoop migration 
to Databricks with MLens

Migrate your data and your workloads with MLens
Learn more

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