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What is Oracle Big Data Discovery?

Oracle Big Data Discovery stands out as a highly visual and intuitive tool that leverages Hadoop's capabilities, transforming raw data into actionable insights for businesses in mere minutes, thus negating the need for extensive tool mastery or reliance on specialized experts. This innovative solution allows users to easily pinpoint relevant data sets within Hadoop, quickly explore the data to understand its significance, improve its quality through enhancement and refinement, analyze it for fresh insights, and disseminate findings while effortlessly reintegrating into Hadoop for organization-wide applications. By establishing BDD as the foundational element of your data lab, your organization can foster a unified environment for examining and navigating diverse data sources within Hadoop, which streamlines the development of projects and applications. Unlike traditional analytics platforms, BDD opens the door for a wider audience to interact with big data, drastically cutting down the duration required for data loading and updates, hence enabling teams to focus on significant data analysis and exploration. This transition not only boosts productivity but also democratizes data access, enabling a greater number of individuals to participate in data-driven decision-making processes, ultimately leading to improved outcomes for the organization. Furthermore, by empowering users across various skill levels, BDD cultivates a culture of collaboration and innovation in data utilization, fostering an environment where insights can be rapidly derived and acted upon.

What is Apache Parquet?

Parquet was created to offer the advantages of efficient and compressed columnar data formats across all initiatives within the Hadoop ecosystem. It takes into account complex nested data structures and utilizes the record shredding and assembly method described in the Dremel paper, which we consider to be a superior approach compared to just flattening nested namespaces. This format is specifically designed for maximum compression and encoding efficiency, with numerous projects demonstrating the substantial performance gains that can result from the effective use of these strategies. Parquet allows users to specify compression methods at the individual column level and is built to accommodate new encoding technologies as they arise and become accessible. Additionally, Parquet is crafted for widespread applicability, welcoming a broad spectrum of data processing frameworks within the Hadoop ecosystem without showing bias toward any particular one. By fostering interoperability and versatility, Parquet seeks to enable all users to fully harness its capabilities, enhancing their data processing tasks in various contexts. Ultimately, this commitment to inclusivity ensures that Parquet remains a valuable asset for a multitude of data-centric applications.

Media

Media

Integrations Supported

Hadoop
Fortinet SD-WAN
Grafana Cloud
Microsoft Azure
Siemens Opcenter APS
VMware Cloud
VMware Tanzu CloudHealth

Integrations Supported

Hadoop
Amazon SageMaker Data Wrangler
Apache DataFusion
Astera Dataprep
Blotout
Ficstar
Indexima Data Hub
Mage Platform
Mage Sensitive Data Discovery
Meltano
PI.EXCHANGE
PuppyGraph
SSIS Integration Toolkit
Tonic Ephemeral

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

Oracle

Date Founded

1977

Company Location

United States

Company Website

www.oracle.com/middleware/technologies/big-data-discovery.html

Company Facts

Organization Name

The Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

parquet.apache.org

Categories and Features

Data Discovery

Self Service Data Preparation
Visual Analytics

Categories and Features

Columnar Databases

Not specified

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