Dremio is a provider of analytical processing data lake engines. The features of the product include data security and control, SSO authentication, automatic backups, predictive pipelining, inbound impersonation, workload management, etc. The clients of the company include Henkel, Honeywell, NCR, UBS, Verisk, DIAGEO, etc.

Dremio is a next-generation data lake engine that liberates your data with live, interactive queries directly on cloud data lake storage. On-Demand Demo.

Starburst queries data across any database, making it instantly actionable for data-driven organizations. With Starburst, teams can lower the total cost of their infrastructure and analytics investments, prevent vendor lock-in, and use the existing tools that work for their business. Trusted by companies like Comcast, FINRA, and Condé Nast, Starburst helps companies make better decisions faster on all data.

Starburst is the analytics engine for the data mesh. It help companies quickly unlock the value of their data by making it easy to access.

Databricks provides a data lakehouse that unifies your data warehousing and AI use cases on a single platform. With Databricks, you can implement a common approach to data governance across all data types and assets, and execute all of your workloads across data engineering, data warehousing, data streaming, data science, and machine learning on a single copy of the data. Built on open source and open standards, with hundreds of active partnerships, Databricks easily integrates with your modern data stack. Additionally, Databricks uses an open standards approach to data sharing to eliminate ecosystem restrictions. Finally, Databricks provides a consistent data platform across clouds to reduce the friction of multicloud environments. Today, Databricks has over 7000 customers, including Amgen, Walmart, Disney, HSBC, Shell, Grab, and Instacart.

Databricks combines the best of data warehouses and data lakes into a lakehouse architecture.

Cloud-based data analytics and data lake management platform. The features of the solution include databases, data science, data collection, data storage, data analysis, data monitoring, data migrating, etc. It also provides open-source storage-based data lakes operations platform.

Lakefs helps you transform your object storage into a Git-like repository.

Onehouse delivers a new bedrock for your data, through a cloud-native managed lakehouse service built on Apache Hudi, which was created by the founding team while they were at Uber. Onehouse makes it possible to blend the ease of use of a warehouse with the scale of a data lake, by offering a seamless experience for engineers to get their data lakes up and running. 

Cazena provides managed cloud service for end-to-end big data service. It acts as an extension to the enterprise infrastructure and enables to run workloads in the cloud. It has intelligence built in to deploy infrastructure for Hadoop, MPP SQL, Spark or search based on the workload. Existing BI tools could be used to analyze the data as if it were present in local. Its use cases include overloaded datacenter, simplifying analytic pipelines, as a sandbox environment, as a data lake etc.

Cazena offers the only automated cloud data lakes ready for instant analytics and ML with Zero Ops. 

Hudi is a rich platform to build streaming data lakes with incremental data pipelines
on a self-managing database layer, while being optimized for lake engines and regular batch processing.

Hudi brings transactions, record-level updates/deletes, and change streams to data lakes!

Qubole is a provider of a cloud-based data lake platform. The product offered by the company is a unified interface for connecting various data sources and querying them on the chosen technology and infrastructure. The platform uses machine learning to create and manage automated pipelines, and infrastructure optimization. It can also be used for ad-hoc querying, BI workloads, and application workloads. The clients of the company include GANNETT, KFC, Gaia, etc.

Qubole is a simple, open, and secure Data Lake Platform for machine learning, streaming, and ad-hoc analytics

Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance. This means customers of all sizes and industries can use it to store and protect any amount of data for a range of use cases, such as data lakes, websites, mobile applications, backup and restore, archive, enterprise applications, IoT devices, and big data analytics. Amazon S3 provides easy-to-use management features so you can organize your data and configure finely-tuned access controls to meet your specific business, organizational, and compliance requirements. Amazon S3 is designed for 99.999999999% (11 9's) of durability, and stores data for millions of applications for companies all around the world.

Amazon S3 has a simple web services interface that you can use to store and retrieve any amount of data, at any time, from anywhere on the web.

Cloud Storage is a flexible, scalable, and durable storage option for your virtual machine instances. You can read and write files to Cloud Storage buckets from almost anywhere, so you can use buckets as common storage between your instances, App Engine, your on-premises systems, and other cloud services.
If Cloud Storage buckets do not meet your performance and latency requirements, you can use Cloud Storage in combination with other instance storage options.

Google Cloud Storage is a RESTful online file storage web service for storing and accessing data on Google Cloud Platform infrastructure.

Upsolver is the only no-code data lake engineering platform. It empowers any SQL-savvy data practitioner to design and deliver big data projects on an AWS or Azure in days, not the months required by hand-coding and configuring Spark implementations. Upsolver is used by the world's most data-intensive organizations, including Sisense, ironSource, and AppsFlyer, for preparing petabyte-scale streaming datasets.

Upsolver is a no-code data lake engineering platform that combines data lake economics with data warehouse performance.

The Azure Storage platform is Microsoft's cloud storage solution for modern data storage scenarios. Core storage services offer a massively scalable object store for data objects, disk storage for Azure virtual machines (VMs), a file system service for the cloud, a messaging store for reliable messaging, and a NoSQL store.

Microsoft Azure, commonly referred to as Azure, is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers.

Delta Lake is a storage layer that brings reliability to your data lakes built on HDFS and cloud storage by providing ACID transactions through optimistic concurrency control between writes and snapshot isolation for consistent reads during writes. Delta Lake also provides built-in data versioning for easy rollbacks and reproducing reports.

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.

Minio is an open source object storage server with Amazon S3 compatible API. It provides the facility to build cloud-native applications portable across all major public and private clouds. It provides protection to the data against hardware failures using erasure code and bitrot detection. To provide confidentiality, integrity, and authenticity of data both server side and client side uses encryption technology such as AES-256-GCM, ChaCha20-Poly1305, and AES-CBC (Cipher Block Chaining). It is available for Linux, Windows, and Mac.

Enterprises use MinIO to deliver against ML/AI, analytics, application, backup and archival workloads - all from a single platform. 

Kylo is an open source data lake management software platform. It supports self-service data ingest with data cleansing, validation, and automatic profiling. Kylo's ingest workflow moves data from source to Hive tables with advanced configuration options around field-level validation, data protection, data profiling, security, and overall governance. It can wrangle data with visual SQL and an interactive transform through an user interface by using capabilities of Apache Spark to create interactive data transformation. Also, it helps search, explore & monitor data and metadata, view lineage, and profile statistics. Kylo is aimed at sectors such as banking, airline, telecommunications, insurance and more.

Kylo is an enterprise-ready modern data lake management software platform for big data engines such as Teradata, Apache Spark, or Hadoop.

Set up an instant data lakehouse to unify, transform and share all of your siloed data in less time than it takes to make a coffee. No code or configuration required.

Phiona makes it simple to set up an analytics lakehouse, automate manual data tasks, and share data with others- no code required.

Companies

Dremio

Starburst

Databricks

Lakefs

Onehouse

Cazena

Apache Hudi

Qubole

AWS S3

Google Cloud Storage

Upsolver

Azure Cloud Storage

Delta.io

Min.io

Kylo

Phiona

Did we miss any company?