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What is Hitachi Streaming Data Platform?

The Hitachi Streaming Data Platform (SDP) is specifically designed for the real-time processing of large volumes of time-series data as it is generated. By leveraging in-memory and incremental computation methods, SDP facilitates swift analyses that avoid the usual lags associated with traditional data processing approaches. Users can define summary analysis scenarios using Continuous Query Language (CQL), which has a syntax similar to SQL, thereby allowing flexible and programmable data analysis without the need for custom-built applications. The platform's architecture comprises various elements such as development servers, data-transfer servers, data-analysis servers, and dashboard servers, which collectively form a scalable and efficient ecosystem for data processing. Moreover, SDP’s modular architecture supports numerous data input and output formats, including text files and HTTP packets, and integrates effortlessly with visualization tools like RTView for real-time performance tracking. This thoughtful design guarantees that users can proficiently manage and analyze their data streams as they happen, ultimately empowering them with actionable insights. As a result, organizations can respond more quickly to changing data conditions, significantly enhancing their operational agility.

What is Google Cloud Dataflow?

A data processing solution that combines both streaming and batch functionalities in a serverless, cost-effective manner is now available. This service provides comprehensive management for data operations, facilitating smooth automation in the setup and management of necessary resources. With the ability to scale horizontally, the system can adapt worker resources in real time, boosting overall efficiency. The advancement of this technology is largely supported by the contributions of the open-source community, especially through the Apache Beam SDK, which ensures reliable processing with exactly-once guarantees. Dataflow significantly speeds up the creation of streaming data pipelines, greatly decreasing latency associated with data handling. By embracing a serverless architecture, development teams can concentrate more on coding rather than navigating the complexities involved in server cluster management, which alleviates the typical operational challenges faced in data engineering. This automatic resource management not only helps in reducing latency but also enhances resource utilization, allowing teams to maximize their operational effectiveness. In addition, the framework fosters an environment conducive to collaboration, empowering developers to create powerful applications while remaining free from the distractions of managing the underlying infrastructure. As a result, teams can achieve higher productivity and innovation in their data processing initiatives.

Media

Media

Integrations Supported

Virtana Platform

Integrations Supported

CData Connect
DataBuck
Google Cloud Bigtable
Google Cloud Confidential VMs
Google Cloud Datastream
Google Cloud IoT Core
Google Cloud Knowledge Catalog
Google Cloud Managed Service for Apache Airflow
Google Cloud Platform
Google Cloud Profiler
New Relic
Orchestra
Pantomath
Protegrity
Sedai
Telmai
Ternary

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

Not specified

Supported Platforms

SaaS

Customer Service / Support

Standard Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Hitachi

Company Location

Japan

Company Website

www.hitachi.com

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

cloud.google.com/dataflow

Categories and Features

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards

Categories and Features

Data Engineering

Not specified

Data Pipeline

Not specified

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards

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