This market evaluates vendors of data science and machine-learning platforms. These are software products that data scientists use to help them develop and deploy their own data science and machine-learning solutions. More precisely, Gartner defines a data science and machine-learning platform as: A cohesive software application that offers a mixture of basic building blocks essential both for creating many kinds of data science solution and incorporating such solutions into business processes, surrounding infrastructure and products. Machine learning is a popular subset of data science that warrants specific attention when evaluating these platforms.

Data Science and Machine Learning Platforms

Data Science and Machine Learning (ML) Platforms

Gartner defines the cloud database management system (DBMS) market as being that for products from vendors that supply fully provider-managed public or private cloud software systems that manage data in cloud storage. Data is stored in a cloud storage tier (such as a cloud object store, a distributed data store or other proprietary cloud storage infrastructure), and may use multiple data models — relational, nonrelational (document, key-value, wide-column, graph), geospatial, time series and others.
These DBMSs reflect optimization strategies designed to support transactions and/or analytical processing for one or more of the following use cases: Traditional and augmented transaction processing, Traditional and logical data warehouse, Data science exploration/deep learning, Stream/event processing, and Operational intelligence
This market does not include vendors that only provide DBMSs hosted in infrastructure as a service (IaaS), such as in a virtual machine or container, and managed by the customer.

Cloud Database Management Systems

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

Databricks Lakehouse Platform Enterprise IT Software Reviews | Gartner Peer Insights

Starburst Enterprise

Starburst

Databricks Lakehouse Platform

Databricks

Ahana Cloud for Presto

Ahana

AtScale

Dremio

Kyvos

Kyvos Insights

Varada

Jethro

Alluxio Data Orchestration Platform

Alluxio

ChaosSearch Data Lake Platform

ChaosSearch

3B - 10B USD

30B + USD

50M - 250M USD

500M - 1B USD

250M - 500M USD

1B - 3B USD

10B - 30B USD

Finance

Services

Miscellaneous

Retail

Manufacturing

ANALYST

One of the top products in the market which provides data analysis/ wrangling to feature creation, finetuning, training, model testing and validation, and final deployment. A product used by many users over the world and easing the process of integration and it is another great example that eases accessing my desktop applications to access cloud based Databricks Spark clusters. 
A very well featured and first class product with extensive documentation and support. And the detailed workflow helps me run and visualise data at any level. 

Kyligence

Other...

Incorta

Denodo

GridGain

SENIOR SOLUTIONS ENGINEER

The databricks lakehouse was the took chosen as we adopt multi-cloud strategy and continue to modernize our data from on premise to cloud. There are two reasons why this is a very good product for a cloud upgrade : 
1. Efficient management across different cloud platforms: The company had adopted a multi-cloud strategy and hence it was essential to have a data lake platform which helped manage all these cloud platforms and also ensure same scalability and security. The platform was able to satisfy the use-cases and we have been able to bring together the platforms under one data lake with data bricks. 

2. Ease of integration - One of the bigger issues was around how we would manage these multi cloud platforms and integrate it into a single platform. The databricks support team was on-hand to help with our questions and the solutions architect provided a Solution Design Document which made it possible for us to integrate as well as complete the project on time. 

MANAGER

It is a perfect combination of Machine learning and Data lakes software to help govern the inventory structure. We have shortly used it for inventory but deemed not suitable as it was very high architecture. The report and customisation feature is off the charts.
Our development team was able to deploy this for a short while but it was too much work for a small change which was already being handled by another tool we had.

Manager Data Engineering

Databricks lakehouse platform provided a great medium for designing Datawarehouse and data lake. The referential integrity was able to be defined at all the layers. Similar structures could be grouped together and the independent ones could be categorized separately. The ETL pipelines could be built to transform and integrate the datasets from various sources. 

SENIOR SOFTWARE ENGINEER

Easy to process and do data transformation for large quantities streaming data.The Predicitive model is one of the must have feature for AI capacbilities and learning. the machine learning feature also included with the tool, its integration with most of the Microsoft products like azure,etc. very scalable and elastic search

Active

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