DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports
Events Video Library
Refcards
Trend Reports

Events

View Events Video Library

Zones

Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks

The software you build is only as secure as the code that powers it. Learn how malicious code creeps into your software supply chain.

Apache Cassandra combines the benefits of major NoSQL databases to support data management needs not covered by traditional RDBMS vendors.

Generative AI has transformed nearly every industry. How can you leverage GenAI to improve your productivity and efficiency?

Modernize your data layer. Learn how to design cloud-native database architectures to meet the evolving demands of AI and GenAI workloads.

Related

  • Utilizing AI and Database Technologies to Stimulate Innovation
  • Leveraging Time Series Databases for Cutting-Edge Analytics: Specialized Software for Providing Timely Insights at Scale
  • Exploring the Dynamics of Streaming Databases
  • Navigating the Divide: Distinctions Between Time Series Data and Relational Data

Trending

  • MySQL to PostgreSQL Database Migration: A Practical Case Study
  • AWS to Azure Migration: A Cloudy Journey of Challenges and Triumphs
  • Data Quality: A Novel Perspective for 2025
  • Unlocking AI Coding Assistants: Generate Unit Tests
  1. DZone
  2. Data Engineering
  3. Databases
  4. Which Flow Is Best for Your Data Needs: Time Series vs. Streaming Databases

Which Flow Is Best for Your Data Needs: Time Series vs. Streaming Databases

While both are used to handle time-related data, their underlying technologies and main purpose are built to serve different purposes.

By 
Gautam Goswami user avatar
Gautam Goswami
DZone Core CORE ·
Dec. 13, 24 · Tutorial
Likes (3)
Comment
Save
Tweet
Share
5.1K Views

Join the DZone community and get the full member experience.

Join For Free

Data is being generated from various sources, including electronic devices, machines, and social media, across all industries. However, unless it is processed and stored effectively, it holds little value.

A significant evolution is taking place in the way data is organized for further analysis. Some databases prioritize organizing data based on its time of generation, while others focus on different functionalities.

Although time series and streaming databases perform different functions, they complement each other well in data management and analytics. While both are used to handle time-related data, their underlying technologies and main purpose are built to serve different purposes.

Time Series Database

A time series database (TSDB) is designed to store, manage, and analyze data points indexed by time. Each data point typically consists of a timestamp and associated values often collected from sensors, logs, or financial markets. However, a streaming database is designed to manage and analyze constant streams of data in real time. It focuses on consuming, processing, and querying data on arrival instead of waiting for data to be stored.

Time series vs. streaming databases

TSDB follows a time-centric architecture where the data is primarily organized around timestamps and supports managing data lifecycles, automatically archiving or deleting older data. Besides, it is designed to handle high-frequency inserts, often from IoT devices or real-time monitoring systems. Also proficient at performing aggregations like averages, minimum, maximum, and trends over time periods.

Streaming Databases

Streaming databases are primarily focused on real-time processing that allows querying and analysis on the fly as data streams in. They are suited for event-driven architecture where computations or alerts based on specific conditions can be triggered by connecting with event-driven systems. They are also majorly connected or integrated with data streaming platforms like Apache Kafka or AWS Kinesis.

Finding the Perfect Match

A TSDB can be used if you want to store everything from continuous monitoring and metrics collection, such as server uptime, CPU usage, memory utilization, network bandwidth, etc. It is well-suited for handling large volumes of high-frequency writes and query metrics over defined time ranges, allowing for real-time monitoring and long-term trend analysis. 

For IoT applications, the TSDB is the best fit as IoT devices generate data like smart thermostats, industry-based equipment devices, or wearables, and the data will have associated timestamps. For financial market analysis, TSDBs are perfect, as financial data is often time-sensitive and needs to be indexed correctly and TSDB can be leveraged to store the historical data and access it quickly for analytics, forecasting, and modeling at scale.

Streaming databases are the right choice in event-driven architectures where timely decision-making is critical, such as tracking user behavior on websites, processing financial transactions, or managing supply chain logistics. When you need to process and evaluate data as it enters your system in real time, a streaming database is perfect. Applications like fraud detection, live dashboards, recommendation engines, and IoT device anomaly monitoring that require instant insights are best served by streaming databases. 

Conclusion

Because both time series and streaming databases are time-centric, they may appear to be comparable, although they serve essentially different needs. Streaming databases are excellent for real-time data processing and analytics, whereas time series databases are best for storing and analyzing historical data. The key to choosing the best technology for your application is knowing your unique use case.

Database Time series Stream processing

Published at DZone with permission of Gautam Goswami, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

Related

  • Utilizing AI and Database Technologies to Stimulate Innovation
  • Leveraging Time Series Databases for Cutting-Edge Analytics: Specialized Software for Providing Timely Insights at Scale
  • Exploring the Dynamics of Streaming Databases
  • Navigating the Divide: Distinctions Between Time Series Data and Relational Data

Partner Resources

×

Comments

The likes didn't load as expected. Please refresh the page and try again.

ABOUT US

  • About DZone
  • Support and feedback
  • Community research
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Core Program
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 100
  • Nashville, TN 37211
  • support@dzone.com

Let's be friends: