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What is Hydrolix?

Hydrolix acts as a sophisticated streaming data lake, combining separated storage, indexed search, and stream processing to facilitate swift query performance at a scale of terabytes while significantly reducing costs. Financial officers are particularly pleased with a substantial 4x reduction in data retention costs, while product teams enjoy having quadruple the data available for their needs. It’s simple to activate resources when required and scale down to nothing when they are not in use, ensuring flexibility. Moreover, you can fine-tune resource usage and performance to match each specific workload, leading to improved cost management. Envision the advantages for your initiatives when financial limitations no longer restrict your access to data. You can intake, enhance, and convert log data from various sources like Kafka, Kinesis, and HTTP, guaranteeing that you extract only essential information, irrespective of the data size. This strategy not only reduces latency and expenses but also eradicates timeouts and ineffective queries. With storage functioning independently from the processes of ingestion and querying, each component can scale independently to meet both performance and budgetary objectives. Additionally, Hydrolix's high-density compression (HDX) often compresses 1TB of data down to an impressive 55GB, optimizing storage usage. By utilizing these advanced features, organizations can fully unlock their data's potential without being hindered by financial limitations, paving the way for innovative solutions and insights that drive success.

What is Dremio?

Dremio offers rapid query capabilities along with a self-service semantic layer that interacts directly with your data lake storage, eliminating the need to transfer data into exclusive data warehouses, and avoiding the use of cubes, aggregation tables, or extracts. This empowers data architects with both flexibility and control while providing data consumers with a self-service experience. By leveraging technologies such as Apache Arrow, Data Reflections, Columnar Cloud Cache (C3), and Predictive Pipelining, Dremio simplifies the process of querying data stored in your lake. An abstraction layer facilitates the application of security and business context by IT, enabling analysts and data scientists to access and explore data freely, thus allowing for the creation of new virtual datasets. Additionally, Dremio's semantic layer acts as an integrated, searchable catalog that indexes all metadata, making it easier for business users to interpret their data effectively. This semantic layer comprises virtual datasets and spaces that are both indexed and searchable, ensuring a seamless experience for users looking to derive insights from their data. Overall, Dremio not only streamlines data access but also enhances collaboration among various stakeholders within an organization.

Media

Media

Integrations Supported

Looker
SQL
Akamai
Amazon Web Services (AWS)
Grafana Cloud
Kubernetes
Microsoft Azure
Redash

Integrations Supported

Looker
SQL
Azure Marketplace
Codd AI
Cyral
DashboardFox
DataClarity Unlimited Analytics
Hex
Microsoft Power BI
Microsoft Power Query
Preset
Privacera
Protegrity
witboost

API Availability

Has API

API Availability

Pricing Information

$2,237 per month
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Not specified

Company Facts

Organization Name

Hydrolix

Company Location

Canada

Company Website

www.hydrolix.io

Company Facts

Organization Name

Dremio

Date Founded

2015

Company Location

United States

Company Website

www.dremio.com

Categories and Features

Big Data

Not specified

Data Lake

Not specified

Categories and Features

Big Data

Not specified

Data Engineering

Not specified

Data Lake

Not specified

Data Lineage

Not specified

Data Virtualization

Not specified

Data Warehouse

Not specified

Query Engines

Not specified

Semantic Layer

Not specified

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