Druid Features

Collects, stores, and organizes massive, unstructured data in real time

Facilitates the disseminating of collected big data throughout parallel computing clusters

Creates a repository to collect and store raw data from sensors, devices, machines, files, etc.

Aligns processing and distribution workflows on top of Apache Hadoop

Aligns processing and distribution workflows on top of Apache Hadoop

Facilitates solution to run on and scale to a large number of machines and systems

Curates collected data for big data analytics solutions to analyze, manipulate, and model

Aligns processing and distribution workflows on top of Apache Hadoop

Moves big data collection and processing to the cloud

Processes batch, real-time, and streaming data workloads in singular, multi-tenant, or cloud systems

Consolidates, Cleanses and Normalizes data from multiple disparate sources.

Helps save storage capacity and improves query performance.

Eliminates data inconsistency and duplications ensuring data integrity.

SQL based analytics functions like Time series, pattern matching, geospatial analytics etc.

Provides built in capabilities like machine learning algorithms, data preparation functions, model evaluation and management etc.

Allows data querying across data formats like parquet, ORC, JSON etc and analyze complex data types on HDFS

Integrates with data science workflows, Machine Learning and artificial intelligence (AI) capabilities.

Integrates with BI Tools to transform data into Actionable Insights.

Provides speed in data processing and capturing unstructured, semi-structured and streaming data.

Provides On-Premise deployment options.

Provides Cloud deployment options (private or public cloud, hybrid cloud).

Manages huge volumes of data, upscale or downscale as per demand.

Provides data recovery functionality to protect and restore data in a database.

Allows multi-version concurrency control.

Handles workloads, from single machines to data warehouses or web services with many concurrent users.

Allows users to quickly retrieve data through various types of indexing like B-tree, hash table etc.

Helps interpret SQL queries and determine the fastest method of execution.

Provides support for international character sets and full text search.

Supports multiple data types like primitive, structured, document etc.

Supports multiple procedural programming languages like PL/PGSQL, Perl, Python etc.

Available on multiple operating systems like Linux, Windows, MacOS etc.

Prevents other users and applications from accessing data while it is being updated to avoid data loss or update.

Allows permissions to be granted or revoked in the database, schema or table levels.

Built-in native encryption with enterprise key management.

Provides multi-factor authentication with certificates.

Policies, procedures and standards to manage and access data.

Restricts data access at a cell level, mask or hide parts of cells, and encrypt data at rest and in motion

Provides predefined system roles, privileges, and user-defined roles to users.

Allows integration with external security mechanisms like Kerberos, LDAP authentication etc.

Provides an audit log to track access and operations performed on databases for regulatory compliance.

Provides encryption capability for all the data at rest using encryption keys.

Data is organized as a set of tables with columns and rows like a table structure.

Allows users to create, update and retrieve data in a database.

Adheres to ACID (atomicity, consistency, isolation, durability), a set of database transaction properties.

Provides log-based or/and trigger-based replication.

Stores data tables as columns.

Supports multiple data types like lists, sets, hashes (similar to map), sorted sets etc.

Implements auto horizontal data partitioning that allows storing data on more than one node to scale out.

Restores a database to a correct (consistent) state in the event of a failure.

Copy data across multiple servers through master-slave, peer-to-peer replication architecture etc.

Stores frequently-used data in system memory quickly.

Provides support to store, index and query data in more than one format.

Available on multiple operating systems like Linux, Windows, MacOS etc.

Aids in faster processing of query to obtain results.

Enables faster queries and search engine-like workloads.

Enables the user to track the time taken for data to be generated and available for processing

Aids in monitoring and reporting data pipeline performance.

Manages in building pipelines between two protocols.

Allows continuous data ingestion directly from the data source.

Aids the database in scaling horizontally and vertically in petabytes.

Helps store the data in fixed size segments called vectors for better management.

Enables ingesting data efficiently and securely.

Allows concurrent access to data without the use of any table locks.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.