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What is PipelineDB?

PipelineDB acts as an enhancement to PostgreSQL, enabling the effective aggregation of time-series data specifically designed for real-time analytics and reporting needs. It allows users to create continuous SQL queries that consistently compile time-series data while only saving the summarized results in conventional, searchable tables. This method resembles extremely efficient, self-updating materialized views that do not require manual intervention for refreshing. Importantly, PipelineDB does not write raw time-series data to disk, which significantly boosts the performance of aggregation operations. The continuous queries produce their own output streams, facilitating the easy integration of various continuous SQL processes into intricate networks. This capability guarantees that users can develop sophisticated analytics solutions that adapt in real time to incoming data, making it a powerful tool for data-driven decision-making. Moreover, the ability to link multiple data streams enhances the potential for comprehensive insights from diverse datasets.

What is Apache Doris?

Apache Doris is a sophisticated data warehouse specifically designed for real-time analytics, allowing for remarkably quick access to large-scale real-time datasets. This system supports both push-based micro-batch and pull-based streaming data ingestion, processing information within seconds, while its storage engine facilitates real-time updates, appends, and pre-aggregations. Doris excels in managing high-concurrency and high-throughput queries, leveraging its columnar storage engine, MPP architecture, cost-based query optimizer, and vectorized execution engine for optimal performance. Additionally, it enables federated querying across various data lakes such as Hive, Iceberg, and Hudi, in addition to traditional databases like MySQL and PostgreSQL. The platform also supports intricate data types, including Array, Map, and JSON, and includes a variant data type that allows for the automatic inference of JSON data structures. Moreover, advanced indexing methods like NGram bloomfilter and inverted index are utilized to enhance its text search functionalities. With a distributed architecture, Doris provides linear scalability, incorporates workload isolation, and implements tiered storage for effective resource management. Beyond these features, it is engineered to accommodate both shared-nothing clusters and the separation of storage and compute resources, thereby offering a flexible solution for a wide range of analytical requirements. In conclusion, Apache Doris not only meets the demands of modern data analytics but also adapts to various environments, making it an invaluable asset for businesses striving for data-driven insights.

Media

Media

Integrations Supported

PostgreSQL
Luzmo
Vertica

Integrations Supported

PostgreSQL
Apache Flink
Apache Hive
Apache Hudi
Apache Spark
Baidu Palo
Dosi
MySQL
OpenMetadata
SelectDB
TapData
VeloDB

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

PipelineDB

Company Location

United States

Company Website

github.com/pipelinedb/pipelinedb

Company Facts

Organization Name

The Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

doris.apache.org

Categories and Features

Database

Data Security
Mobile Access
NOSQL
Performance Analysis
Queries
Relational Interface

Categories and Features

Data Warehouse

Not specified

OLAP Databases

Not specified

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