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What is IBM Db2 Event Store?

IBM Db2 Event Store is a cloud-native database solution meticulously crafted to handle extensive amounts of structured data stored in Apache Parquet format. The architecture of this system is tailored to enhance event-driven data processing and analytics, allowing it to gather, assess, and store more than 250 billion events every single day. This robust data repository is both flexible and scalable, enabling it to adjust promptly to shifting business requirements. By utilizing the Db2 Event Store service, users can create these data repositories within their Cloud Pak for Data environments, which promotes effective data governance while supporting detailed analytics. Notably, the system can quickly ingest large quantities of streaming data, achieving processing rates of up to one million inserts per second per node, which is crucial for real-time analytics that integrate machine learning functionalities. It also enables immediate analysis of data from numerous medical devices, which can enhance patient health outcomes, while providing a cost-effective approach to data storage management. With such capabilities, IBM Db2 Event Store stands out as an indispensable asset for organizations aiming to effectively harness data-driven insights for improved decision-making and operational efficiency. Ultimately, its multifaceted features empower businesses to stay ahead in a rapidly evolving data landscape.

What is HStreamDB?

A streaming database is purpose-built to efficiently process, store, ingest, and analyze substantial volumes of incoming data streams. This sophisticated data architecture combines messaging, stream processing, and storage capabilities to facilitate real-time data value extraction. It adeptly manages the continuous influx of vast data generated from various sources, including IoT device sensors. Dedicated distributed storage clusters securely retain data streams, capable of handling millions of individual streams effortlessly. By subscribing to specific topics in HStreamDB, users can engage with data streams in real-time at speeds that rival Kafka's performance. Additionally, the system supports the long-term storage of data streams, allowing users to revisit and analyze them at any time as needed. Utilizing a familiar SQL syntax, users can process these streams based on event-time, much like querying data in a conventional relational database. This powerful functionality allows for seamless filtering, transformation, aggregation, and even joining of multiple streams, significantly enhancing the overall data analysis process. With these integrated features, organizations can effectively harness their data, leading to informed decision-making and timely responses to emerging situations. By leveraging such robust tools, businesses can stay competitive in an increasingly data-driven landscape.

Media

Media

Integrations Supported

Apache Parquet
IBM Cloud Pak for Data

Integrations Supported

Apache Spark
Azure Databricks
Elastic Cloud
MongoDB
Oracle Fusion Cloud ERP
Presto
PyTorch
SAP ERP
Snowflake
TensorFlow

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

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
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/docs/en/cloud-paks/cp-data/3.5.0

Company Facts

Organization Name

EMQ

Date Founded

2013

Company Location

United States

Company Website

hstream.io

Categories and Features

Database

Not specified

Categories and Features

Database

Not specified

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