Gartner defines the market for cloud database management systems (DBMSs) as the market for software products that store and manipulate data and that are primarily delivered as software as a service (SaaS) in the cloud. Cloud DBMSs may optionally be capable of running on-premises, or in hybrid, multicloud or intercloud configurations. They can be used for transactional work and/or analytical work. They may have features that enable them to participate in a wider data ecosystem. Must-have capabilities for this market include: Availability as SaaS on provider-managed public or private cloud systems; Management of data within cloud storage — that is, cloud DBMSs are not hosted in infrastructure as a service (IaaS), such as in a virtual machine or a container managed by the customer.

Cloud Database Management Systems

Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling that support the independent use by, and collaboration between, data scientists and their business and IT counterparts through all stages of the data science life cycle. These stages include business understanding, data access and preparation, experimentation and model creation, and sharing of insights. They also support machine learning engineering workflows including creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances and/or as a fully managed cloud offering.
Data science and machine learning (DSML) platforms are designed to allow a broad range of users to develop and apply a comprehensive set of predictive and prescriptive analytical techniques. Leveraging data from distributed sources, cutting-edge user experience, and native machine learning and generative AI (GenAI) capabilities, these platforms help to augment and automate decision making across an enterprise. They provide a range of proprietary and open-source tools to enable data scientists and domain experts to find patterns in data that can be used to forecast financial metrics, understand customer behavior, predict supply and demand, and many other use cases. Models can be built on all types of data, including tabular, images, video and text for applications that require computer vision or natural language processing.

Data Science and Machine Learning Platforms

Analytics and business intelligence platforms — enabled by IT and augmented by AI — empower users to model, analyze and share data.
Analytics and business intelligence (ABI) platforms enable organizations to understand their data. For example, what are the dimensions of their data — such as product, customer, time, and geography? People need to be able to ask questions about their data (e.g., which customers are likely to churn? Which salespeople are not reaching their quotas?). They need to be able to create measures from their data, such as on-time delivery, accidents in the workplace and customer or employee satisfaction. Organizations need to blend modeled and nonmodeled data to create new data pipelines that can be explored to find anomalies and other insights. ABI platforms make all of this possible.

Analytics and Business Intelligence Platforms

Analytics query accelerators provide SQL or SQL-like query support on a broad range of data sources. They are most frequently used as a means of providing interactive and production-optimized delivery on semantically flexible data stores that do not inherently have the capabilities to provide sufficient performance or ease of use on their own. Commonly used in conjunction with data lakes, they aim to support BI dashboards, interactive query capabilities, data modeling and other analytics use cases.

Analytics Query Accelerators

Generative AI (GenAI) engineering refers to the field of engineering that focuses on the development, implementation and optimization of generative AI models. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. By developing GenAI models, engineers can create new and innovative ways to generate content. The vendors in this segment are made up by incumbent and startup vendors covering full-model life cycle management, specifically adjusted to and catering to development, refinement and deployment of generative models (e.g., LLMs) and other GenAI artifacts in production applications. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.

Generative AI Engineering

Generative AI (GenAI) model providers focus on developing and providing generative AI technologies and make them available to other developers, businesses and general public through APIs or commercial licenses. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. This layer of vendors offers access to commercial or open-source foundation models such as LLMs and other types of generative algorithms (such as GANs, genetic/evolutionary algorithms or simulations). These models can be provided for developers to embed into their applications or be used as base models for fine-tuning customized models for their software offerings or internal enterprise use cases. This helps businesses gain the benefits of advanced generative AI technologies while avoiding the high costs, expertise requirements and time needed to develop these technologies in-house. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.

Generative AI Model Providers

Gartner defines DataOps as the collaborative data management practice focusing on improving communication, continuous integration, automation, observability and operations of data flows between data managers, data consumers, and their teams across the organization. DataOps tools connect and orchestrate data pipelines across heterogeneous systems. Data and analytics leaders are the buyers in this emerging market. The primary audience for DataOps tools is “data manager” personas like, data engineers, data integration developers, operations/incident analysts, database administrators and data architects. The secondary audience is “data consumer” personas like business analysts, business intelligence developers, data scientists and citizen roles (departmental users who are domain experts, but less technical).

DataOps Tools

Databricks Data Intelligence Platform Reviews, Ratings & Features 2024 | Gartner Peer Insights

Evaluation and contract negotiation

Evaluation & Contracting

Timely and complete response to product questions

Pricing and contract flexibility (pricing and terms)

Integration and deployment

Integration & Deployment

Ease of integration using standard APIs and tools

Quality and availability of end-user training

Ease of deployment

Service and support

Service & Support

Timeliness of vendor's response

Quality of technical support

Quality of peer user community

Product capabilities

Product Capabilities

Scalability

Integration

Customization

Ease of deployment, administration, and maintenance

Advanced Analytics (O)

Application Development Support (O)

Automated Performance Tuning and Optimization (O)

Data Science and Machine Learning

Distributed capabilities

Financial Governance (O/A)

Management and Administration Capabilities (O/A)

Multicloud/Intercloud/Hybrid (O/A)

Operational Intelligence

Performance Features (O/A)

Relational Attributes (O)

Resource Usage (O)

Transactional Consistency (O)

Additional Context

SQL Server

Microsoft

MongoDB Atlas

MongoDB

Oracle Database

Oracle

MySQL HeatWave Database Service

Teradata VantageCloud

Teradata

Amazon Redshift

Amazon Web Services (AWS)

SAP HANA Cloud

Amazon Aurora

Google BigQuery

Google

Snowflake AI Data Cloud

Snowflake

<50M USD

10B - 30B USD

30B + USD

1B - 3B USD

3B - 10B USD

50M - 250M USD

250M - 500M USD

500M - 1B USD

Gov't/PS/ED <5,000 Employees

Gov't/PS/ED 5,000 - 50,000 Employees

Databricks Data Intelligence Platform

Databricks

Head Of Data Science And Engineering

Databricks provides a comprehensive set of capabilities that make processes easier in a lot of different areas. From data engineering to data analytics. With that it's a great framework to build your data democratization initiative around.

Data Engineer

Databricks is a one-stop shop for developing and building out your company's data intelligence platform. It does everything that you would need it to do, and more. It combines the best of both worlds for data warehousing and data lakes and it offers it all in one platform. Whether you want to analyze structured data, unstructured data, video, audio, or images, Databricks does it all. Their embrace of open source solutions means your data and code is never locked into one vendor, meaning that if Databricks starts to falter in its development, you can lift and shift to another solution without being locked in.

Cloudera

Other...

Neo4j

Idera (Qubole)

Salesforce

DataStax

Redis

Couchbase

MariaDB

Yellowbrick Data

1010data

Huawei Technologies

SingleStore

Progress

Citus Data

Infoworks

TigerGraph

Cockroach Labs

Kyligence

Agile Lab

Data Engineering Manager

My overall experience with Databricks has been superb. I view our relationship with Databricks as a true partnership. I work with the people at Databricks daily. Even though we are two discrete companies, we essentially function as a single, unified team.

VP OF ENGINEERING

Databricks has been a comprehensive data and analytics platform for our organization.  We are excited that Databricks has chosen to opensource many of their key components in order to prevent vendor lock in.  The customer service and product organization has readily available to us and receptive to our needs and suggestions.

VP, Engineering

Databricks is an amazing technology and platform being something akin to an "uber" database - a database which is not the best at all things, but has capabilities for most things and has "infinite" storage and scalabilities.  Their recent offerings in AI have empowered our own AI roadmap and shown smart investment into AI operations vs. trying to chase being the "best model."

MLOps / DataOps Engineer

Databricks provides valuable features around for data computing and storage layers along with MLOps capabilities. They have a very rapid pace of feature and product development in the areas of Data Analytics & Science, ML/AI (Ops), Data Lakehouse and Data Governance and provide a wide range of tooling. With the rapid development pace it can sometimes be demanding to migrate to newer features and achieve backwards compatibility in our workloads. They provide good support with solution architecture and best-practices. However, at least in Europe they seem to have some internal fluctuation that from time to time leads to inefficiencies in communication with Databricks internal consultants. They have also significantly improved the data warehousing area in the last year by adding ML/AI based automated optimization methods to DeltaLake. There are extensive features to get data securely in and out of Databricks thanks to Unity Catalog.

Active

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50M-1B USD

1B-10B USD

10B+ USD

Gov't/PS/Ed

Sr Manager, Data Enginerring

Databricks has been an exceptional platform and an outstanding vendor. They are one of the most innovative and fast moving companies. At the same time they are reciprocative to the needs of their customers. 

DIRECTOR, DATA ENGINEERING

The vendor has been very responsive to our needs, especially with our new contracting terms.  Before, we did not have a consulting agreement but now that has been implemented, we are getting better responses than before

VP, Data and Analytics

Databricks has been a wonderful platform for our AI applications and data analysis. The company is excellent to work with and the product is outstanding.

DIRECTOR - BIG DATA AND HPC INFRASTRUCTU

Databricks technology is adding a significant advantage to our overall data and analytics strategy and implementation.  They continue to advance their product capabilities with the implementation of LLM and ML model capabilities.  They have been very responsive to our needs for new features and capabilities as we have adopted their platform.

Lead Data Scientist

The Databricks team has been exceptional at communication during our use of their framework.  They have educated and enabled us to use the service, but also incorporated our feedback to make the improvements we've requested.

Director of Engineering

I have been using Databricks for a long time now and it is one of the finest products in the market for data engineering, analysts and scientists. Databricks makes it very easy and simple to build data pipelines and provides a holistic review of the data end to end. It has helped me tremendously to speed up data delivery across different data engineering teams and has always been a reliable product.  

Engineer

Responsive, knowledgeable support team, fastest stable feature shipping developer team in the segment & best overall managed data & AI platform.

Executive Director of Engineering

Our Account team has provided sufficient support to us.  Runtime upgrades are always a pain as we encounter various issues and create tickets to the support team.  However, the issue is not always resolved in a timely manner. 

The Databricks platform offers great capabilities by integrating AI and Lakehouse, providing a unified location for managing, utilizing and governing data.

Director of Data and Analytics

Extremely easy to use product, great support, intuitive UI

Staff Data Engineer

Databricks is a game changer product and I would really love to use it for all the data needs. They are very innovative and fast paced when it comes to launching new useful features.

Databricks is a global company focusing on data and AI. At the core of Databricks is the Databricks Data Intelligence Platform which allows entire organizations to use data and AI to power a wide range of business use cases. It's built on a lakehouse to provide an open, unified foundation for all data and governance and is powered by a Data Intelligence Engine that understands the uniqueness of the organizations’ data. Databricks simplifies and accelerates enterprises' data and AI goals by unifying data, analytics and AI on one platform. Its key mission is to assist data teams in addressing some of the world's most challenging problems.

Databricks is a global company focusing on data and AI. With the use of its Lakehouse Platform, it serves to unify the data, analytics, and AI of numerous organizations worldwide. Located in San Francisco and having offices in various parts of the world, Databricks was founded by the initial creators of Apache Spark, Delta Lake, and MLflow. Its key mission is to assist data teams in addressing some of the most challenging problems.