Databricks Competitors
Data Infrastructure•Last updated: Apr 12, 2025
Fivetran
fivetran.comdeployment methodHybrid
market positioningFivetran positions itself as a leading player in the data movement industry, specifically recognized
customer satisfaction4.2-4.5
Aerospike
aerospike.comdeployment methodHybrid
product nameAerospike Database
customer satisfaction4.4 to 4.9
Google Cloud Vertex AI
cloud.google.com/vertex-aiinitial price$3.465 - $21.252 per node hour
deployment methodCloud
ongoing support cost$300/month
Data Mechanics
datamechanics.coproduct descriptionData Mechanics offers a managed Spark platform designed for data engineering teams, enabling them to
implementation complexityComplex
product nameOcean for Apache Spark
Snowflake
snowflake.com/en/deployment methodCloud
initial price$23.00
ongoing support cost$40 per TB per month
Databricks, Inc.
databricks.comdeployment methodHybrid
product descriptionDatabricks is a unified data analytics platform designed to streamline data engineering, data scienc
initial price$0.20
Dremio
dremio.comongoing support cost$0.39 per DCU
product nameUnified Lakehouse Platform
customer satisfaction4.1 to 4.6
Google Cloud
cloud.google.com/bigqueryinitial price$0.02 per GB for active storage, $0.01 per GB for long-term storage, $5 per TB for queries
product nameBigQuery
deployment methodCloud
Dremio
dremio.com/product descriptionDremio offers a Unified Lakehouse Platform designed to streamline data analytics and AI initiatives.
initial price$1000 to $2000 per month
product nameDremio Unified Lakehouse Platform
Talend
talend.com/product nameTalend Data Fabric
product descriptionTalend offers a comprehensive platform known as Talend Data Fabric, which integrates data integratio
market positioningTalend positions itself as a leader in the data integration, data quality, and data governance indus
ClickHouse
clickhouse.com/key disadvantages- Worse query performance than competitors like TimescaleDB for most queries, except for complex agg
product descriptionClickHouse is an open-source, real-time data warehouse designed for analytical queries and data proc
deployment methodHybrid
Cloudera
cloudera.com/product nameCloudera Data Platform
ongoing support cost$833
deployment methodHybrid
Yellowbrick Data
yellowbrick.com/market positioningYellowbrick Data positions itself as a high-performance SQL data platform specifically designed for
ongoing support cost$613 per vCPU per year (one-year subscription), $482 per vCPU per year (three-year subscription)
initial price$613 per vCPU per year (one-year subscription), $482 per vCPU per year (three-year subscription)
Alteryx
alteryx.com/product nameAlteryx Designer
initial price$4,950, $5,195
market positioningAlteryx is strategically positioned as a significant player in the enterprise analytics market, hold
Amazon Web Services
aws.amazon.com/redshift/product use cases- **Data Analytics as a Service (DaaS)**: Organizations can package and offer access to their collec
customer satisfaction4.4
ongoing support cost$3 per hour
Teradata
teradata.com/initial price$4.80/hour, $4,800/month, $9,000/month
product nameVantageCloud
ongoing support cost$4.80 per hour, $9,000 per month
Qubole
qubole.com/companies using productExpedia (https://www.expedia.com/)
Merkle (https://www.merkleinc.com/)
Acxiom (https://www.acxiom.co
ongoing support cost$0.168 per QCU per hour (Enterprise Edition), $0.24 per QCU per hour (On-Demand), $108 per user per
initial price$0.168 (Enterprise Edition), $0.24 (On-Demand)
Microsoft Azure Synapse Analytics
azure.microsoft.com/en-us/products/synapse-analyticsongoing support cost$100 per month (Standard support)
deployment methodCloud
product descriptionAzure Synapse Analytics is a comprehensive analytics service provided by Microsoft that combines ent
Apache Spark
spark.apache.org/implementation complexityComplex
product nameApache Spark
initial price0
UN
Unknown Company
Databricks Competitors
Databricks Competitors Comparison (2025)
Competitive Landscape: Databricks Competitors
Benchmarking is essential for strategy. This dataset compares 20 key players in the Databricks Competitors ecosystem, providing the structured data needed for competitive intelligence.
Cluster Analysis
The market clusters around several key poles:
Understanding where a company sits in these clusters is vital for assessing its competitive threat or partnership potential.
Data-Driven Insights
Use the sort and filter functions above to rank companies by size, funding, or location to identify the true market leaders versus the noisy challengers.
Methodology
Our data team employs a rigorous multi-step process to ensure the accuracy and relevance of this directory. We combine automated data ingestion from public filings, verified user submissions, and manual analyst review to maintain a high-signal database.
Data Collection Sources
- Public Filings: SEC reports (10-K, 10-Q) and international registry equivalents.
- Investor Disclosures: Verified funding announcements from venture capital firms and private equity groups.
- Proprietary Crawlers: Our dedicated bots monitor corporate career pages, engineering blogs, and press releases for real-time updates on tech stacks and headcount growth.
Verification Process
Every entry in this list undergoes a quarterly "liveness check." We verify domain activity, leadership continuity, and product availability. Companies that cease operations or pivot out of the sector are flagged for removal or archival.
Company | Product Name | Product Description | Key Advantages | Key Disadvantages | Implementation Complexity | Initial Price | Ongoing Support Cost | Deployment Method | Market Positioning | Customer Satisfaction | Companies Using Product | Product Use Cases | One-Liner | HQ Country | Employee Count | Founding Year |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Fivetran data movement platform sources: 1 | Fivetran is a data movement platform that automates the process of transferring data from various sources to destinations, facilitating faster insights and optimized operations. Key features include over 700 fully-managed connectors, automated data synchronization, and support for incremental data u sources: 1 2 3 4 | Over 300 pre-built connectors for easy integration with various data sources.
User-friendly interface requiring little to no coding, suitable for non-technical users.
Automation of data extraction and transformation processes, reducing manual intervention.
Free plan available, allowing small busines sources: 1 2 | High latency: Fivetran primarily operates on a batch processing model, which may not be suitable for real-time data needs.
High and unpredictable costs: Fivetran is considered one of the most expensive options among modern ELT vendors.
Limited transformation capabilities: Complex data transformation sources: 1 2 3 4 | Complex sources: 1 2 3 4 5 | $0.01 per model run after 5000 runs sources: 1 2 | – | Hybrid sources: 1 2 3 4 | Fivetran positions itself as a leading player in the data movement industry, specifically recognized for its automated data integration solutions. In the 2023 Gartner Magic Quadrant for Data Integration, Fivetran was named a Challenger, reflecting its strong execution capabilities and comprehensive sources: 1 2 3 | 4.2-4.5 sources: 1 2 3 | Oldcastle Infrastructure (https://www.fivetran.com/case-studies/oldcastle-infrastructure-migrates-on-prem-data-to-the-cloud-with-fivetran)
DocuSign (https://www.fivetran.com/case-studies/case-study-docusign)
Crossmedia (https://www.fivetran.com/case-studies/case-study-crossmedia)
Pfizer (https sources: 1 2 3 4 | Analyzing Operational Data in Construction: Companies like Emery Sapp & Sons can migrate data from PostgreSQL and SQL Server to a cloud data warehouse, allowing them to create up-to-date dashboards for operational insights, improving efficiency and decision-making.
Improving Customer Loyalty in Reta sources: 1 2 | Automated data movement platform for seamless integration | – | 1001-5000 | 2012 | |
Aerospike Database sources: 1 2 | Aerospike is a distributed NoSQL database designed for real-time data processing with features that enable businesses to manage large volumes of data efficiently. Its main functionalities include:
Massive Scalability: Aerospike can scale horizontally and vertically, accommodating evolving data need sources: 1 2 3 | 14x greater throughput compared to competitors like Cassandra.
42x lower read latency than Cassandra.
24x lower update latency compared to Cassandra.
8x greater insert throughput than Cassandra.
Flash-optimized in-memory architecture for high performance and low latency.
Supports a schema-free data sources: 1 2 3 | Lack of automation in upgrading clusters from development to production.
Complicated installation process.
Difficulty in handling big data user-friendliness.
No community version available for broader adoption and experimentation.
Limited scalability and elasticity compared to competitors.
Performan sources: 1 2 | Complex sources: 1 2 | – | – | Hybrid sources: 1 2 3 4 | Aerospike positions itself as a leading distributed NoSQL database, primarily targeting enterprises that require real-time data processing and high scalability. With a market share of approximately 0.63% in the NoSQL database sector, it competes against major players like MongoDB, Amazon DynamoDB, a sources: 1 2 3 4 5 | 4.4 to 4.9 sources: 1 2 3 4 5 | EPAM Systems Inc (https://epam.com)
Globant (https://globant.com)
Unity Technologies (https://unity.com)
Accenture PLC (https://accenture.com)
Wayfair (https://www.wayfair.com)
PhonePe (https://www.phonepe.com)
Paytm (https://paytm.com)
Index Exchange (https://www.indexexchange.com)
sources: 1 2 3 4 | Real-time transaction processing: Aerospike 8 introduces high-performance transactions with strict serializability guarantees, making it suitable for applications that require real-time decision-making and high-throughput performance.
Financial services: The database's strong consistency and comm sources: 1 2 | Real-time database with sub-millisecond latency and 10x ingestion volume | – | 201-500 | 2009 | |
Vertex AI sources: 1 | Google Cloud Vertex AI is a fully-managed, unified AI development platform designed to facilitate the creation and deployment of generative AI applications. Key features include:
Machine Learning Models: Offers a wide range of machine learning models, including pre-trained models and the ability to sources: 1 2 3 | Seamless integration with other Google Cloud services, streamlining model training and deployment.
Advanced support for custom models and machine learning pipelines.
AutoML features that allow users to create models with minimal coding, accelerating time-to-market.
Unified platform that consolidates sources: 1 2 3 4 | The platform's wide array of features can overwhelm beginners to AI and machine learning.
Deep integration with Google Cloud may limit flexibility for users of other cloud ecosystems.
The Gemini AI model has frequently given biased responses.
Limited availability of pre-built models compared to comp sources: 1 2 3 | Complex sources: 1 | $3.465 - $21.252 per node hour sources: 1 | $300/month sources: 1 2 | Cloud sources: 1 2 3 4 | Google Cloud Vertex AI is positioned as a leading unified AI development platform within the cloud computing industry, particularly in the generative AI segment. It offers a fully-managed service that simplifies the process of building, deploying, and managing machine learning models. Vertex AI dist sources: 1 2 3 4 5 | 4.4 sources: 1 2 3 4 5 | General Motors (https://www.appsruntheworld.com/customers-database/customers/view/general-motors-company-united-states)
Mercedes-Benz Group (https://www.appsruntheworld.com/customers-database/customers/view/daimler-germany)
Citigroup (https://www.appsruntheworld.com/customers-database/customers/ sources: 1 2 3 | Retail & Consumer Goods:
Best Buy: Utilizes a generative AI-powered virtual assistant for troubleshooting product issues and managing subscriptions.
Etsy: Optimizes search recommendations and ad models to enhance buyer suggestions and seller growth.
Carrefour Taiwan: Implements an AI Sommelier sources: 1 2 3 | Google Cloud Vertex AI is a fully-managed, unified AI development platform designed to facilitate the creation and deployment of generative AI applications. Key features include:
Machine Learning Models: Offers a wide range of machine learning models, including pre-trained models and the ability to | – | – | – | |
Ocean for Apache Spark sources: 1 2 | Data Mechanics offers a managed Spark platform designed for data engineering teams, enabling them to efficiently deploy Apache Spark on Kubernetes within their cloud accounts. Key features include a developer-friendly interface, cost-effective resource management, and seamless integration with exist sources: 1 | Intuitive user interface with a dashboard for logs and metrics.
Dynamic optimizations for infrastructure parameters and Spark configurations.
Automated scaling of Spark applications and Kubernetes clusters.
Fleet of optimized Docker images for Spark with connectors.
Managed service that handles setu sources: 1 2 | Requires significant expertise and setup to make Spark-on-Kubernetes reliable at scale.
Complexity in deployment compared to competitors that offer more straightforward solutions.
Users must run the latest Spark versions, which may not be as user-friendly for all teams. sources: 1 | Complex sources: 1 2 | $0.05 sources: 1 2 | – | Cloud sources: 1 2 3 4 | Data Mechanics positions itself as a developer-friendly and cost-effective managed Spark platform within the data engineering industry. It targets data engineering teams by automating performance tuning and infrastructure management for Apache Spark deployed on Kubernetes in cloud environments. This sources: 1 2 3 4 5 6 7 | 5 sources: 1 | – | Interactive Data Analysis: Users can connect Jupyter notebooks to the Data Mechanics platform to perform interactive data analysis with Spark, allowing for real-time data exploration and manipulation.
Application Development and Submission: Developers can submit Spark applications programmaticall sources: 1 2 3 | Cloud-native Spark platform with 50-75% cost savings | – | 1-10 | 2019 | |
AI Data Cloud sources: 1 2 | Snowflake is a cloud-based data platform designed to facilitate data storage, management, and analysis across various environments. Its main features and functionalities include:
Unified Platform: Snowflake provides a single platform for data warehousing, data lakes, and data sharing, enabling seam sources: 1 2 3 4 | Scalability: Snowflake allows independent scaling of compute and storage resources, outperforming competitors like Amazon Redshift.
Multi-cloud architecture: It supports deployment across multiple cloud platforms (AWS, Azure, GCP), providing flexibility.
User-friendly interface: Snowflake is known f sources: 1 2 3 4 5 6 7 | No on-premises deployment, limiting options for organizations needing on-prem solutions.
Unpredictable pricing model can lead to unexpected costs, especially with high data usage.
Relatively small user community compared to competitors, which may limit support and resources.
Higher operational costs sources: 1 2 3 | Complex sources: 1 2 3 4 | $23.00 sources: 1 2 | $40 per TB per month sources: 1 2 3 | Cloud sources: 1 2 3 | Snowflake positions itself as a leading cloud-based data platform, primarily focusing on scalability, flexibility, and ease of use. Its unique architecture allows for the separation of compute and storage, enabling organizations to scale resources independently, which is a significant advantage over sources: 1 2 3 4 | 4.5 sources: 1 2 | Amazon (https://www.amazon.com)
Walmart (https://www.walmart.com)
Exxon Mobil (https://www.exxonmobil.com)
Apple (https://www.apple.com)
CVS Health (https://www.cvshealth.com)
Berkshire Hathaway (https://www.berkshirehathaway.com)
UnitedHealth Group (https://www.unitedhealthgroup.com)
sources: 1 2 | Advertising, Media, and Entertainment: Media companies like Warner Music Group utilize Snowflake to enhance audience analytics and provide personalized experiences by syndicating large amounts of interaction data while ensuring privacy through data-clean rooms.
Financial Services: Financial firms le sources: 1 2 3 | AI Data Cloud for data collaboration and insights | – | 5001-10000 | 2012 | |
Databricks Lakehouse Platform sources: 1 2 | Databricks is a unified data analytics platform designed to streamline data engineering, data science, and machine learning processes. Its main features include:
Data Processing and Management: It offers tools for scheduling and managing data processing tasks, particularly for ETL (Extract, Transf sources: 1 2 3 | Unified platform that integrates data engineering, data science, and machine learning workflows.
Processes data up to 12X faster than many competitors.
Combines data warehouse, data lake, data pipelines, and data catalogs into a single solution.
Utilizes Apache Spark for big data workloads and MLflo sources: 1 | Steep learning curve and setup complexity, making it less user-friendly for non-technical users.
Requires more administration and deployment expertise compared to competitors like Snowflake.
Complex pricing model that can lead to unpredictable costs.
Dependency on Apache Spark, which necessitates te sources: 1 2 3 4 | Complex sources: 1 2 3 | $0.20 sources: 1 2 | – | Hybrid sources: 1 2 3 | Databricks positions itself as a leader in the data analytics and AI industry by offering a unified platform that integrates data management, analytics, and artificial intelligence. The company emphasizes the democratization of data and AI, enabling organizations to leverage data intelligence across sources: 1 2 | 4.1-4.6 sources: 1 2 3 | AT&T (https://www.databricks.com/customers/att)
Block (https://www.databricks.com/customers/block/unity-catalog)
Burberry (https://www.databricks.com/customers/burberry-snowplow)
Rivian (https://www.databricks.com/customers/rivian)
USPS OIG (https://www.databricks.com/customers/usps-oig)
W sources: 1 2 | Large-Scale Workloads: Databricks enhances performance by using parallel processing to manage large-scale data pipelines, reducing bottlenecks and speeding up insights. It unifies workflows, optimizes compute resources, and boosts productivity with serverless architecture.
Real-Time Insights: The sources: 1 2 3 | Unified platform for data, analytics, and AI | United States | 5001-10000 | 2013 | |
Unified Lakehouse Platform sources: 1 2 | Dremio offers a Unified Lakehouse Platform that enables businesses to create and consume data products powered by Apache Iceberg. Its main features include the ability to connect, govern, and analyze data across various sources, including cloud storage and on-premises systems. Dremio enhances analyt sources: 1 2 3 4 | Superior query performance compared to competitors.
Self-service experience that empowers users.
Cost-effective solution that reduces reliance on traditional data warehouses.
Flexibility in querying data from various sources, including data lakes and cloud storage.
User-friendly design that enhances sources: 1 2 3 4 5 | Lack of support for Delta connector.
Long execution times for large, correlated queries.
Limited support for database views and external decryption libraries.
Users report a steep learning curve and lack of know-how for effective use.
Ranked lower in deployment and operations compared to some compet sources: 1 2 3 | Complex sources: 1 2 3 4 5 | $0.39 per DCU, $6.24 (Small), $12.48 (Medium), $24.96 (Large), $49.92 (X Large) sources: 1 2 | $0.39 per DCU sources: 1 2 3 | Hybrid sources: 1 2 3 4 | Dremio positions itself as a leader in the data analytics industry with its Unified Lakehouse Platform, which integrates data management and analytics capabilities. This platform allows businesses to connect, govern, and analyze data seamlessly, whether in the cloud or on-premises. Dremio's focus on sources: 1 2 3 4 | 4.1 to 4.6 sources: 1 2 3 4 | Amazon (https://www.dremio.com/customers/amazon/)
Henkel (https://www.dremio.com/customers/henkel/)
FactSet (https://www.dremio.com/customers/factset/)
AP Intego (https://www.dremio.com/customers/ap-intego/)
Garmin (https://www.garmin.com)
Grab (https://www.grab.com)
Paytm (https://paytm.com)
IBM (h sources: 1 2 3 4 5 6 | On-Prem to Cloud Migration: Dremio facilitates a seamless transition from on-premises data infrastructure to the cloud, leveraging its scalability and performance benefits.
Data Warehouse Offload: Organizations can optimize data processing and reduce costs by offloading heavy workloads from tradi sources: 1 2 | Intelligent Lakehouse Platform for AI and analytics | – | 201-500 | 2015 | |
BigQuery sources: 1 | Google Cloud's BigQuery is a fully managed, serverless enterprise data warehouse designed for large-scale data analytics. It supports all data types and enables users to perform real-time analytics and data querying across clouds. Key features include built-in machine learning capabilities, business sources: 1 2 | Fully managed and serverless architecture, eliminating infrastructure management.
Supports both structured and unstructured data with open table formats.
Capable of running petabyte-scale queries with results in seconds.
Built-in machine learning and business intelligence capabilities.
Cost-effectiv sources: 1 2 3 4 | Lack of compute customization for query processing to optimize costs.
High costs for storage and queries at scale.
Limited support for complex joins across large datasets due to distributed processing limitations.
Extra costs for data transfer services for scheduling queries.
Minimum time interval a sources: 1 2 3 | Complex sources: 1 2 | $0.02 per GB for active storage, $0.01 per GB for long-term storage, $5 per TB for queries sources: 1 2 | $29.00 or 3% of monthly Cloud charges, whichever is higher for Standard Support; $100.00 or a tiered percentage for Enhanced Support; $15,000.00 or a tiered percentage for Premium Support sources: 1 2 | Cloud sources: 1 | Google Cloud's BigQuery is strategically positioned as a leading player in the data analytics and data warehousing industry. It holds an estimated market share of 12.61%, ranking third among competitors, with Snowflake and Amazon Redshift leading at 20.65% and 15.84%, respectively. BigQuery is recog sources: 1 2 3 | 4.5 sources: 1 2 | Millennium BCP (https://cloud.google.com/customers/millennium-bcp)
Apex Fintech Solutions (Not provided)
Super-Pharm (https://cloud.google.com/customers/super-pharm)
apree health (https://cloud.google.com/customers/apree-health)
Mercado Libre (Not provided)
Commerzbank (Not provided)
Est sources: 1 2 3 | Data Analysis and Business Intelligence: BigQuery allows users to perform descriptive and prescriptive analysis, enabling organizations to derive insights from their data using ANSI-standard SQL queries.
Machine Learning and Predictive Analytics: With BigQuery ML, users can create and train machine sources: 1 2 3 | Google Cloud Vertex AI is a fully-managed, unified AI development platform designed to facilitate the creation and deployment of generative AI applications. Key features include:
Machine Learning Models: Offers a wide range of machine learning models, including pre-trained models and the ability to | – | – | – | |
Dremio Unified Lakehouse Platform sources: 1 2 | Dremio offers a Unified Lakehouse Platform designed to streamline data analytics and AI initiatives. Its main features include the ability to create and consume data products powered by Apache Iceberg, which enhances data management and analytics capabilities. The platform significantly reduces cost sources: 1 | Superior query performance compared to competitors.
Self-service analytics capabilities that empower users.
Cost-effective solution that reduces reliance on traditional data warehouses.
Flexibility in querying data from various sources, including data lakes and cloud storage.
User-friendly design th sources: 1 2 3 4 5 | Lack of support for Delta connector.
Long execution times for large, correlated queries.
Limited support for database views and external decryption libraries.
Users report a steep learning curve and lack of know-how for effective use.
Ranked low in deployment and operations compared to competitors. sources: 1 2 3 | Complex sources: 1 2 | $1000 to $2000 per month sources: 1 2 | $0.39 per DCU sources: 1 2 | Hybrid sources: 1 2 3 4 5 | Dremio positions itself as a leader in the data analytics industry with its Unified Lakehouse Platform, which integrates data management and analytics capabilities. The platform is designed to facilitate self-service analytics and AI initiatives, allowing businesses to create and consume data produc sources: 1 2 3 | 4.1 to 4.6 sources: 1 2 3 4 | Amazon (https://www.amazon.com/)
Henkel (https://www.henkel.com/)
FactSet (https://www.factset.com/)
AP Intego (https://www.apintego.com/)
Garmin (https://www.garmin.com/)
Grab (https://www.grab.com/)
Paytm (https://paytm.com/)
IBM (https://www.ibm.com/) sources: 1 2 3 4 5 6 | On-Prem to Cloud Migration: Dremio facilitates the seamless transition from on-premises data infrastructure to cloud environments, providing scalability and flexibility while reducing operational costs.
Data Warehouse Offload: Organizations can optimize data processing and reduce costs by offloading sources: 1 2 | Intelligent Lakehouse Platform for AI and analytics | – | 201-500 | 2015 | |
Talend Data Fabric sources: 1 2 | Talend offers a comprehensive platform known as Talend Data Fabric, which integrates data integration, data quality, and data governance functionalities. Key features include:
Data Integration: Talend provides tools to connect, transform, and manage data from various sources, enabling seamless data sources: 1 2 3 | Comprehensive data integration capabilities, supporting ETL, ELT, and streaming workflows.
Strong data quality and governance features, ensuring data integrity and compliance.
Flexibility and ease of use, making it suitable for mid-market companies.
Cost-effective entry point compared to competitors sources: 1 2 | Talend has a steep learning curve, making it challenging for beginners to master its features.
The platform may require specific coding skills for pipeline-building, which many teams may lack.
Some users report difficulties in obtaining timely support from Talend's customer service.
Talend's batch-o sources: 1 2 3 4 | Complex sources: 1 2 3 | – | $1,100 sources: 1 2 | Hybrid sources: 1 2 3 4 | Talend positions itself as a leader in the data integration, data quality, and data governance industry by offering a unified platform known as Talend Data Fabric. This platform combines various functionalities, allowing organizations to manage their data effectively and derive actionable insights. sources: 1 2 3 4 | 4.3, 4.6, 3.1, 4.3, 67 sources: 1 2 3 4 5 6 7 8 | Citi (https://www.citigroup.com/citi)
Capgemini (https://www.capgemini.com)
Domino's Pizza (https://www.dominos.com)
Allianz (https://www.allianz.co.uk)
Lenovo (https://www.lenovo.com)
Covanta (https://www.covanta.com)
Company Context
Company Name: Talend; Company Website: https://www.t sources: 1 2 3 | Data Warehousing: Talend can be used to consolidate data from various sources into a centralized data warehouse, enabling businesses to perform analytics and reporting efficiently.
Business Intelligence: Organizations can leverage Talend to prepare and transform data for business intelligence tools, sources: 1 2 | Scalable, cloud-independent data management platform | – | 1001-5000 | 2005 | |
ClickHouse Cloud sources: 1 | ClickHouse is an open-source, real-time data warehouse designed for analytical queries and data processing. Its main features include high performance for complex queries, efficient resource utilization, and the ability to handle large volumes of data. ClickHouse supports SQL for querying, offers co sources: 1 | High performance: ClickHouse can be 2 to 10 times faster than many competitors for analytical queries.
Cost-effective: ClickHouse Cloud is 3-5x more cost-effective than alternatives like Snowflake.
Scalability: Its MPP architecture allows for horizontal scaling, making it suitable for large-scale an sources: 1 2 3 4 | Worse query performance than competitors like TimescaleDB for most queries, except for complex aggregations.
Poor insert performance and higher disk usage compared to some competitors (e.g., 2.7x higher disk usage than TimescaleDB).
Lack of support for certain SQL queries on materialized views, whic sources: 1 2 3 4 | Complex sources: 1 2 | $66.52 sources: 1 2 | $66.52 sources: 1 2 3 | Hybrid sources: 1 2 3 4 | ClickHouse positions itself as a leading player in the real-time data warehousing and analytics market, primarily targeting businesses that require high-speed data processing and analytics capabilities. Its key competitive advantages include:
Performance: ClickHouse is optimized for Online Analytic sources: 1 2 3 4 5 | 4.4 sources: 1 2 3 4 5 6 | 1Flow (https://1flow.ai/)
2gis (https://2gis.ru)
3xpl (https://3xpl.com/)
5CNetwork (https://www.5cnetwork.com/)
ABTasty (https://www.abtasty.com/)
Arkhn (https://www.arkhn.com)
ASO.dev (https://aso.dev/)
AdGreetz (https://www.adgreetz.com/)
AdGuard (https://adguard.com/)
AdScribe (http://www.adscri sources: 1 2 | Real-Time Analytics: ClickHouse is utilized for real-time analytics, allowing businesses to analyze large volumes of data instantly. This is particularly useful for applications that require immediate insights, such as monitoring user behavior on websites or applications.
Business Intelligence Da sources: 1 2 3 | Fast open-source column-oriented database management system | – | 201-500 | 2016 | |
Cloudera Data Platform sources: 1 2 3 | Cloudera offers a hybrid data platform that enables enterprises to manage and analyze data across various environments. Its main features include:
Data Management and Analytics: Provides faster and easier data management and analytics capabilities for data located anywhere, ensuring optimal perfor sources: 1 2 3 | Comprehensive suite of data analytics and management tools for efficient big data processing.
Strong integration capabilities with various cloud environments, suitable for hybrid or multi-cloud scenarios.
Commitment to open-source technology, fostering innovation and collaboration.
Established brand sources: 1 2 3 | Unreliable support and service.
Stability issues, particularly for large-scale solutions.
High pricing compared to alternatives.
Security concerns, particularly regarding blockchain capabilities.
Complicated deployment and integration processes.
Limited flexibility due to proprietary elements in the sources: 1 2 3 | Complex sources: 1 2 3 | $0.04 - $0.20 per CCU sources: 1 2 | $833 sources: 1 2 | Hybrid sources: 1 2 3 | Cloudera is positioned as a leader in the hybrid data platform market, recognized for its innovative approach to data management and analytics. It has been identified as a "Visionary" in the 2023 Gartner Magic Quadrant for Cloud Database Management Systems and a leader in the GigaOm Radar for Data L sources: 1 2 3 | 4.3 - 4.6 sources: 1 2 3 4 | ExxonMobil (https://www.cloudera.com/customers/exxonmobil.html)
Mercy Corps (https://www.cloudera.com/about/news-and-blogs/press-releases/2024-11-25-clouderas-advanced-ai-solutions-accelerate-global-humanitarian-aid-for-mercy-corps.html)
Geodis (https://www.cloudera.com/customers/geodis.html)
sources: 1 2 3 | Remote Monitoring & Control: Cloudera enables manufacturers to implement data-driven remote monitoring and control systems, allowing for real-time data collection and processing to identify issues quickly, improving yield and capacity utilization.
Quality Analytics: The platform facilitates the c sources: 1 2 3 | Hybrid data platform for secure data management | – | 1001-5000 | 2008 | |
Yellowbrick sources: 1 | Yellowbrick Data offers a high-performance SQL data platform designed for enterprise data warehousing, ad-hoc and streaming analytics, and business intelligence workloads. Key features include fully elastic clusters that separate storage and compute, enabling complex queries at multi-petabyte scale sources: 1 2 3 | Predictable and consistent query performance
Significantly reduced total cost of ownership (TCO)
Faster customer onboarding without the need for additional instances
Superior affordability and performance compared to competitors like Snowflake
Seamless migration with PostgreSQL compatibility sources: 1 2 3 | Cloud Dependency: Reliance on cloud infrastructure can be a disadvantage.
Compatibility Issues: Problems with certain ETL tools have been reported.
Difficult Learning Curve: Users find the learning process challenging.
Complex Setup: Initial setup can be complicated.
Expertise Required: Specialized sources: 1 | Complex sources: 1 2 3 4 | $613 per vCPU per year (one-year subscription), $482 per vCPU per year (three-year subscription) sources: 1 | $613 per vCPU per year (one-year subscription), $482 per vCPU per year (three-year subscription) sources: 1 2 | Hybrid sources: 1 2 3 4 | Yellowbrick Data positions itself as a high-performance SQL data platform specifically designed for enterprise data warehousing, ad-hoc and streaming analytics, and business intelligence workloads. Its unique selling proposition lies in its ability to deliver fast, scalable, and flexible data soluti sources: 1 2 3 | 4.8 sources: 1 2 3 4 5 | American Express (https://www.appsruntheworld.com/customers-database/customers/view/american-express-company-united-states)
Nielsen (https://www.appsruntheworld.com/customers-database/customers/view/nielsen-united-kingdom)
Catalina (https://www.appsruntheworld.com/customers-database/customers/vi sources: 1 2 | Application Analytics: Yellowbrick can be utilized to analyze application performance and user behavior, providing insights that help improve application efficiency and user experience.
Enterprise Data Warehouse: The product serves as a robust data warehouse solution, enabling organizations to co sources: 1 2 3 4 5 6 | SQL data platform built on Kubernetes | – | 201-500 | 2014 | |
Alteryx Designer sources: 1 | Alteryx provides a powerful analytics platform designed for enterprise-level data analysis. Its main features include:
Data Preparation: Automates the process of data cleansing and transformation, allowing users to prepare data for analysis quickly.
Predictive Analytics: Offers tools for predict sources: 1 2 3 4 | Advanced Data Preparation: Alteryx excels in data cleaning, blending, and preparation, making complex data tasks easier.
User-Friendly Interface: The platform is designed for accessibility, allowing non-technical users to perform advanced analytics.
Robust Analytics Capabilities: Offers advanced ana sources: 1 2 3 | Expensive, especially for smaller teams.
Steeper learning curve for new users.
Limited data visualization capabilities.
Struggles with very large, complex datasets.
Less flexible for advanced coding tasks.
Vendor lock-in due to proprietary workflows.
Primarily focuses on batch processing, not real-t sources: 1 2 | Complex sources: 1 2 | $4,950, $5,195 sources: 1 2 | $30,000 - $50,000 sources: 1 2 | Hybrid sources: 1 2 3 4 5 6 | Alteryx is strategically positioned as a significant player in the enterprise analytics market, holding a market share of approximately 6.07%, making it the fourth largest in the data analysis category. Its primary competitors include Tableau Software, which dominates the market with a 64.64% share, sources: 1 2 3 | 4.3 to 4.6 sources: 1 2 3 4 5 | Insight Global (https://www.insightglobal.com/)
Amazon Web Services (https://aws.amazon.com/)
The Home Depot (https://www.homedepot.com/)
University Of Wisconsin (https://www.wisc.edu/)
Marriott Vacations Worldwide (https://www.marriottvacationsworldwide.com/)
Pentair (https://www.pentair. sources: 1 2 | Data Analytics Automation: Alteryx automates the data preparation process, allowing users to focus on analysis rather than manual data handling. This increases productivity and ensures consistent insights.
Data Preparation: Users can blend and prepare data from various sources, making it ready for a sources: 1 2 3 | AI-powered analytics platform for data automation | – | 1001-5000 | 1997 | |
Amazon Redshift sources: 1 | Amazon Redshift is a fully managed, petabyte-scale cloud data warehouse service provided by Amazon Web Services (AWS). It enables users to efficiently analyze large datasets with exceptional price performance, scalability, and security. Key features include:
RA3 Instances: These instances allow for sources: 1 2 3 | Cost-effective pricing, significantly lower than many competitors.
High performance with up to 3 times better price-performance compared to other cloud data warehouses.
Fully managed service that reduces administrative overhead.
Scalability with the ability to add nodes easily as data needs grow.
Us sources: 1 2 3 4 5 | Limited support for parallel uploads from non-AWS sources.
Not a multi-cloud solution, restricting flexibility for users on other platforms.
Performance can degrade with multiple simultaneous users executing queries.
Poorly designed distribution and sort keys can lead to increased costs and degraded sources: 1 2 3 | Complex sources: 1 2 | $0.25 sources: 1 | $3 per hour sources: 1 2 | Cloud sources: 1 2 3 | Amazon Redshift is positioned as a leading data warehousing solution within the cloud computing industry, particularly in the data analytics sector. It holds a market share of approximately 15.84%, making it one of the top players in the data warehousing market, competing against notable alternative sources: 1 2 3 | 4.4 sources: 1 2 3 | Level Set Consulting (https://levelsetconsulting.com)
uShip (https://uship.com)
Druva (https://druva.com)
Pure Storage Inc (https://purestorage.com)
Company Context
Company Name: Amazon Web Services; Company Website: https://aws.amazon.com/redshift/ sources: 1 2 | Data Analytics as a Service (DaaS): Organizations can package and offer access to their collected data along with analytical capabilities as a paid service. For example, a marketing agency can provide demographic data to retailers and healthcare providers, ensuring ease of management, security, cost sources: 1 2 3 | Amazon Web Services (AWS) is a subsidiary of Amazon providing on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered pay-as-you-go basis. Within the energy sector, AWS focuses on offering cloud solutions that enable energy companies to optimize operatio | – | – | July 2002 | |
VantageCloud sources: 1 2 | Teradata Vantage is a comprehensive data and analytics platform that enables organizations to conduct advanced analytics using various programming languages, including SQL, Python, and R. It integrates multiple analytic capabilities such as descriptive, predictive, and prescriptive analytics, along sources: 1 2 3 | Massively Parallel Processing (MPP): Teradata's architecture allows for faster and more efficient query execution by distributing data and workloads across multiple nodes.
Unlimited Scalability: Easily add nodes to accommodate growing data requirements without compromising performance.
Advanced Anal sources: 1 2 3 4 | High pricing compared to competitors, making it less accessible for smaller organizations.
Limited compatibility with some Big Data platforms.
Performance issues with large datasets, particularly in data ingestion times.
Decreased performance due to limited memory availability. sources: 1 2 3 | Complex sources: 1 2 | $4.80/hour, $4,800/month, $9,000/month sources: 1 2 | $4.80 per hour, $9,000 per month sources: 1 2 | Hybrid sources: 1 2 | Teradata is positioned as a leader in the data analytics and hybrid cloud industry, particularly recognized for its comprehensive cloud analytics platform, VantageCloud. The company emphasizes its capabilities in integrating artificial intelligence (AI) and machine learning (ML) at scale, which enha sources: 1 2 3 | 3.9 to 4.3 sources: 1 2 3 | Brinker International (https://www.brinker.com/)
American Airlines (https://www.aa.com/)
Unilever (https://www.unilever.com/)
Netflix (https://www.netflix.com/)
Verizon Communications (https://www.verizon.com/)
Airgas (https://www.airgas.com/)
Bunnings (https://www.bunnings.com.au/)
Ci sources: 1 | Understand the customer journey: Analyze customer interactions across various marketing touchpoints (websites, in-branch, emails, call centers) using attribution modeling and channel analysis to enhance customer experiences and increase acquisition rates.
Segment customers based on past purchases: sources: 1 | Trusted AI and data platform for enterprises | – | 5001-10000 | 1979 | |
Qubole Data Service (QDS) sources: 1 2 | Qubole offers an Open Data Lake Platform that provides end-to-end data lake services, enabling organizations to manage their data efficiently in the cloud. Key features include:
Scalability: Users can analyze, process, and store unlimited amounts of data, scaling resources up or down as needed.
High sources: 1 2 3 | Achieve 50% cost savings with built-in TCO optimizations.
Accelerate continuous data engineering on a single platform.
Offers a choice of cloud and data processing engines (Apache Spark, Presto, Hive).
Provides 10 times higher administrative efficiency and 50% lower cloud costs.
Supports end-to-end sources: 1 2 | Fewer ETL tools compared to competitors.
Cluster management issues reported by users. sources: 1 2 | Complex sources: 1 2 3 | $0.168 (Enterprise Edition), $0.24 (On-Demand) sources: 1 | $0.168 per QCU per hour (Enterprise Edition), $0.24 per QCU per hour (On-Demand), $108 per user per month (On-Demand) sources: 1 2 | Cloud sources: 1 2 3 4 | Qubole positions itself as a leader in the data lake and analytics industry by offering a cost-efficient, open, and secure platform that integrates various data engines for machine learning, streaming, and ad-hoc analytics. The company emphasizes its ability to reduce cloud data lake costs by over 5 sources: 1 2 3 | 4.0 sources: 1 2 3 4 5 | Expedia (https://www.expedia.com/)
Merkle (https://www.merkleinc.com/)
Acxiom (https://www.acxiom.com/) sources: 1 | Sentiment Analysis: Enhances customer experience and brand revitalization by mining multi-structured data from various sources into a single database.
360-Degree Customer View: Provides a comprehensive understanding of customer behavior by analyzing data from social media, sensors, and mobile dev sources: 1 | Open data lake platform for analytics and machine learning | – | 201-500 | 2011 | |
Azure Synapse Analytics sources: 1 | Azure Synapse Analytics is a comprehensive analytics service provided by Microsoft that combines enterprise data warehousing and big data analytics. It allows users to analyze data at scale using both serverless and dedicated resources. Key features include integrated data management, powerful query sources: 1 | Scalability: Azure Synapse Analytics can handle large volumes of data and allows businesses to scale up or down as needed.
Integrated platform: It combines big data, data warehousing, and integrates seamlessly with other Microsoft products.
Security: The platform features advanced encryption and str sources: 1 2 | Limited support for certain SQL functions (e.g., no support for the case() function).
Spark clusters are not accessible outside of Synapse, which can hinder integration with other tools like Tableau.
Some users report performance issues with larger datasets compared to competitors.
The user interfac sources: 1 2 3 | Complex sources: 1 2 3 | $883.081 per 100 DWUs/month, $23 per TB per month, $5 per TB processed sources: 1 2 | $100 per month (Standard support) sources: 1 2 | Cloud sources: 1 2 | Microsoft Azure Synapse Analytics is positioned as a significant player in the Big Data Analytics market, holding an estimated market share of 8.83%, ranking it #6 among its competitors. Its primary competitors include Databricks, Azure Databricks, and Apache Hadoop. The product is utilized by over sources: 1 2 | 4.0 sources: 1 2 3 4 5 6 | Walgreens (https://www.appsruntheworld.com/customers-database/customers/view/walgreens-company-united-states)
Bank of America (https://www.appsruntheworld.com/customers-database/customers/view/bank-of-america-corporation-united-states)
Vinci (https://www.appsruntheworld.com/customers-database/cu sources: 1 2 | Manufacturing: Optimize operations with real-time insights at scale to predict equipment failures, reduce maintenance costs, and enhance supply chain visibility.
Retail: Unify siloed data to generate real-time insights, enhance customer service, and enable personalized recommendations through data a sources: 1 2 3 4 | Azure Synapse Analytics is a comprehensive analytics service provided by Microsoft that combines enterprise data warehousing and big data analytics. It allows users to analyze data at scale using both serverless and dedicated resources. Key features include integrated data management, powerful query | – | – | – | |
Apache Spark sources: 1 | Apache Spark™ is an open-source, multi-language engine designed for large-scale data processing. It provides a unified analytics engine for big data processing, with built-in modules for SQL, streaming, machine learning, and graph processing. Key features include:
Speed: Spark is known for its high sources: 1 | High speed and performance, processing data up to 100 times faster in memory and 10 times faster on disk compared to competitors.
Supports real-time data processing and analytics, making it suitable for streaming data applications.
In-memory computing capabilities reduce the need for disk I/O, enhan sources: 1 2 3 4 5 6 | High memory consumption and increased hardware costs
Not a stand-alone solution; requires integration with other tools
Limited support for real-time data processing
Complexity in setup and management
No built-in file management system sources: 1 2 3 | Complex sources: 1 2 3 | 0 sources: 1 2 3 | – | Hybrid sources: 1 2 3 | Apache Spark is positioned as a leading platform in the data engineering, data science, and machine learning industries due to its high performance and versatility. It is known for its speed, often cited as being up to 100 times faster than Hadoop for in-memory processing, which makes it particularl sources: 1 2 3 4 5 | 4.4 sources: 1 2 3 4 5 | Apple (https://apple.com)
JPMorgan Chase (https://jpmorganchase.com)
Visa (https://visa.com)
TikTok (https://tiktok.com)
Uber (https://uber.com)
Netflix (https://netflix.com)
Shopify (https://shopify.com)
Slack (https://slack.com)
Agoda (https://agoda.com)
CRED (https://cred.club) sources: 1 2 3 4 5 | Data Processing and ETL: Apache Spark is widely used for data processing and ETL (Extract, Transform, Load) tasks. It can handle large volumes of data efficiently, allowing organizations to clean, transform, and load data into data warehouses or databases.
Stream Processing: Spark Streaming enables sources: 1 2 3 4 5 6 | Apache Spark™ is an open-source, multi-language engine designed for large-scale data processing. It provides a unified analytics engine for big data processing, with built-in modules for SQL, streaming, machine learning, and graph processing. Key features include:
Speed: Spark is known for its high | – | – | – |
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