The Forrester Wave™: Data Lakehouses, Q2 2024

Data Lakehouses Vendor Product Comparisons 2024

Key features and pros and cons of top Data Lakehouses products:

Databricks - Leaders

Pros:

Strong in data storage and formats, data ingestion/pipeline, data models, security and governance, data connectivity, data catalog, data processing, data transformation, genAI/LLM, performance, scale, and end-to-end integration
Excels at delivering cutting-edge innovation with particular strength in streamlining and automating lakehouse processes with AI
Puts customers at the heart of innovation strategy, using strong community ties to source new features
Compelling and aggressive roadmap centered on advanced genAI/LLM, self-service, automation, data integration, data quality, multicloud, and data intelligence
Reference customers praise single data platform, ability to scale for complex workloads, ease of use, ML model building, product innovation, and technical support

Cons:

Support for data integration, data quality, and deployment options are on par with others

Google - Leaders

Pros:

Strong capabilities in data storage and format, performance, security and governance, data modeling, data processing, data transformation, genAI/LLM, scale-out optimization, and end-to-end integrated solutions
BigQuery delivers serverless data processing engines with unified access layer for all data
Clear and differentiated vision for seamlessly integrated, intelligent, and automated lakehouse
Distinctive roadmap prioritizes distributed and multicloud environments, advanced intelligence, data freshness, automated cataloging, advanced data products, self-service, and data integration with advanced genAI at core
Reference customers value scalable architecture, separation of compute and storage, data transparency, AI/ML integration, singular platform, and ease of use

Cons:

On par with others in data connectivity, data ingestion/pipeline, data integration, data quality, data catalog, and deployment options

Snowflake - Leaders

Pros:

Strong in security and governance, performance, and end-to-end integrated solutions
User-friendly, self-service, elastic, and scalable cloud data warehouse used by thousands of organizations
Advanced capabilities including flexible data storage, integrated transformation, end-to-end automation, and management across multiple clouds
Robust partner ecosystem plays crucial role in facilitating large and complex lakehouse deployments
Superior roadmap focuses on advanced genAI/LLM, process automation, integrated observability, context-driven data quality, advanced data sharing and collaboration, metadata management, and UI personalization
Reference customers praise scale-up and out compute, user-friendly interface, agility, automation, and support for open table format including Apache Iceberg

Cons:

On par with others for data storage and format, data connectivity, data ingestion/pipeline, data integration, data processing, data transformation, data quality, data catalog, and genAI/LLM

Alibaba Cloud - Contenders

Pros:

Strong in lakehouse performance and scale
Reference customers praise unified storage format, interface, high performance, and elastic computing platform

Cons:

Lags in data integration, data quality, and data consumption
Vision appears stagnant, especially regarding platform's future direction and expansion of potential use cases
Lags behind other vendors in innovation
Most deployments remain in China though solution is available in other regions

Amazon Web Services - Strong Performers

Pros:

On par with other vendors for data connectivity, data ingestion/pipeline, data integration, security and governance, data processing, data transformation, genAI/LLM, performance, scale-out, end-to-end integration, and deployment options
Fully managed platform that delivers flexibility to support any lakehouse workload and use case
Built from several existing AWS products including Amazon S3, AWS Lakeformation, AWS Glue, AWS Redshift, and AWS EMR

Cons:

Many enterprise features trail others evaluated, including data storage and formats, data models, data quality, data catalog, and data consumption
Vision, innovation, and roadmap are on par with those evaluated
Declined to participate in the full Forrester Wave evaluation process

Cloudera - Strong Performers

Pros:

Strong in data storage and format, data processing, data consumption, and scale-out optimization
Hybrid cloud lakehouse built on open source, powered by Apache Iceberg, supporting various data formats
Differentiated roadmap emphasizes advanced automation, automating data quality, genAI, simplifying hybrid and multicloud ecosystem, advanced GPU integration, FinOps, vector search, semantics, and natural language
Reference customers like support for Apache Iceberg, platform as a service, broad lakehouse capabilities, data flow using Apache NiFi, ACID functionality, and high-end scale

Cons:

Lags in deployment options
Some reference customers have concerns about migrations, data catalog, lack of LDAP integration, complexity of the platform, and automation

IBM - Contenders

Pros:

Strong on data connectivity and prebuilt industry data models for banking, financial markets, healthcare, insurance, retail, and telcos
On par with others for data integration, security and governance, data processing, transformation, data quality, data catalogs, consumption, and performance
Data lakehouse built on IBM watsonx.data with open, governed, fit-for-purpose architecture
Reference customers like support, performance, syncing of data capabilities, flexible deployment, and good price/performance

Cons:

Lags in data storage and formats, data ingestion/pipeline, genAI/LLM, scale-out optimization, end-to-end integrated solutions, and deployment options
Vision is on par but lags in roadmap and innovation
Some references concerned about pace of new capability rollouts, upgrade path, lack of feature enhancements, ecosystem partners, and advanced automation

Microsoft - Strong Performers

Pros:

Strong in end-to-end integrated solutions and vision
End-to-end unified analytics platform for storing, managing, and analyzing all kinds of data
Strong vision prioritizes automating, accelerating, and democratizing data through genAI
Roadmap suggests improved integration of data management capabilities, advanced genAI/LLM, broader data connectivity, data quality, and advanced automation

Cons:

Falls short in comprehensive features
Lags in breadth of data storage and formats, data processing, data quality, and performance optimization
Declined to participate in the full Forrester Wave evaluation process

OpenText - Contenders

Pros:

On par with other vendors for data connectivity, data ingestion/pipeline, security and governance, data processing, data transformation, data quality, data consumption, performance, scale, genAI/LLM, and end-to-end integrated solutions
Supports Apache Iceberg and reads/analyzes external data lakehouse sources on multiple clouds and on-premises
One reference customer praised product's ease of use, integrated solution, and customer service

Cons:

Many enterprise features behind competition including data storage and formats, data models, data integration, data catalogs, and deployment options
Vision and roadmap are on par but lags in innovation
Some reference customers expressed concerns about vendor lock-in, slow innovation, handling of unstructured data, lack of third-party tooling, and pricing

Oracle - Strong Performers

Pros:

Strong capabilities in data connectivity, data ingestion/pipeline, security and governance, data quality, and data consumption
Superior vision focuses on unified, intelligent, and highly automated lakehouse
Roadmap focuses on distributed and multicloud architecture, advanced genAI/LLM, data products, automation, and self-service data integration
Reference customers praise data streaming, distributed data processing, reliability, stability, technical support, and data platform capabilities

Cons:

Lags in genAI/LLM and innovation
Lags behind other vendors on flexible data storage and formats

Salesforce - Strong Performers

Pros:

Data Cloud powered by lakehouse architecture offers ability to ingest and virtualize all kinds of data for unified customer 360 view
Integrates with Snowflake and Databricks, enabling customers to leverage data from other leading platforms
Competitive roadmap focuses on expanding capabilities around data processing, data catalogs, data quality, semantics, genAI/LLM, governance, engagement and activation, and self-service

Cons:

Lags in high-end scalability
On par with other vendors for all capabilities except scale-out optimization
Vision and innovation are on par with other competitors
Declined to participate in the full Forrester Wave evaluation process

SAP - Contenders

Pros:

On par with other vendors for data connectivity, data ingestion/pipeline, data models, data ingestion, security and governance, data processing, data transformation, data quality, data catalogs, and performance optimization
Lakehouse comprised of several products including SAP HANA, SAP data catalog, SAP Analytics Cloud, and SAP Data Lake
Roadmap focuses on extending capabilities around hybrid-cloud data management, genAI/LLM, data catalogs, data connectivity, automation, and self-service

Cons:

Limited to supporting SAP-related data, needs to expand focus to broader non-SAP data for business growth
Many enterprise features behind competition in data storage and formats, data consumption, genAI/LLM, scale-out optimization, end-to-end integrated solutions, and deployment options
Lags in innovation
Declined to participate in the full Forrester Wave evaluation process

Teradata - Strong Performers

Pros:

Stands out in data modeling
VantageCloud platform provides cloud-native lakehouse catering to wide array of analytical use cases
Overarching strategy revolves around democratizing access to data and AI for all users
Roadmap focuses on genAI/LLM, process automation, data quality, data sharing/collaboration, metadata management, and UI personalization

Cons:

Lags in innovation
Vision and roadmap are on par with other competitors
Did not provide any reference customers for this evaluation

FAQs

Q: Which vendors are Leaders in 2024?

A: Databricks, Google, Snowflake

Q: What does "Leader" mean in Forrester Wave terms?

A: Leaders in the Forrester Wave are vendors that demonstrate the strongest current offering and strategy in the data lakehouse market. Leaders excel across multiple evaluation criteria including integrated solutions, genAI capabilities, performance at scale, data storage and formats, data processing, governance, transformation, and end-to-end integration. They have clear, differentiated visions for the future of data lakehouses, aggressive and compelling roadmaps, strong customer references, and the ability to support enterprise-wide lakehouse strategies for complex workloads and diverse use cases.

Q: How do I interpret a Forrester Wave?

A: https://www.forrester.com/policies/forrester-wave-methodology/

Report:

Top Vendor Products for Data Lakehouses in 2024

Data Lakehouses Vendor Product Comparisons 2024

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FAQs

Q: Which vendors are Leaders in 2024?

Q: What does "Leader" mean in Forrester Wave terms?

Q: How do I interpret a Forrester Wave?

Reference