In the realm of modern data management, two pivotal database paradigms have emerged: streaming databases and real-time Online Analytical Processing (OLAP) databases. These technologies are frequently mentioned and sometimes intertwined with each other when it comes to real-time data analytics. This article explores the distinctions between streaming and real-time OLAP databases, offering insights to aid in your decision-making process and determine which solution suits your data processing and analytics requirements.

What is a streaming database

A streaming database, also known as a real-time or event streaming database, is a type of database that is designed to handle and process high volumes of data in real time as it is generated or ingested. Unlike traditional databases, which are typically used for storing and querying static data, streaming databases are optimized for managing and analyzing data that is continuously changing and arriving at a rapid pace.

Key characteristics of streaming databases include:

  1. Real-time Data Processing: Streaming databases are capable of processing and analyzing data as it is generated, allowing organizations to make immediate decisions based on real-time insights. This is crucial in applications like financial trading, monitoring IoT devices, or analyzing social media trends.
  2. Event-Driven Architecture: Data in streaming databases is often organized as a series of events or messages. These events can be anything from sensor readings, log entries, user interactions, or any other type of data change. The database can react to these events and trigger actions or notifications in response.
  3. Low Latency: Streaming databases are designed for low-latency data processing, ensuring that data is processed and made available for analysis or action as quickly as possible. This is essential for applications where delay can have a significant impact, such as fraud detection or real-time analytics.
  4. Integration with Streaming Platforms: Streaming databases are often used in conjunction with streaming data platforms, such as Apache Kafka or Apache Pulsar, to efficiently ingest and distribute data streams. These platforms help manage the flow of data to and from the database.
  5. Complex Event Processing (CEP): Many streaming databases include capabilities for complex event processing, which allows users to define custom rules and queries to identify patterns, correlations, or anomalies in the streaming data.
  6. Durability and Fault Tolerance: Streaming databases often provide mechanisms for ensuring data durability, replication, and fault tolerance to prevent data loss and ensure high availability.

Streaming databases are widely used in applications such as real-time analytics, fraud detection, monitoring, and alerting systems, recommendation engines, and more, where timely insights from continuously changing data are critical.

Popular streaming databases include RisingWave, Materialize, ksqlDB, Timeplus, DeltaStream, etc. Also, there are various cloud offerings designed for managing the service directly in the cloud.

What is a real-time OLAP database

A Real-time OLAP (Online Analytical Processing) database is a type of database system that combines the features of OLAP and real-time data processing. OLAP databases are designed for complex data analysis and reporting, allowing users to query and analyze data from multiple perspectives and dimensions. Real-time OLAP databases, as the name suggests, provide this analytical capability with a focus on real-time or near-real-time data updates and queries.

Here are some key characteristics and features of a Real-time OLAP database:

  1. Real-time Data Updates: Real-time OLAP databases are designed to handle data that is constantly changing or streaming in real-time. This could include data from sensors, IoT devices, transactional systems, or other sources that generate data continuously.
  2. Low Latency: These databases aim to minimize latency between data ingestion and availability for analysis. Data is often processed and made available for querying in near real-time or with minimal delay.
  3. Analytical Capabilities: Like traditional OLAP databases, Real-time OLAP databases support complex analytical queries. Users can perform multidimensional analysis, drill down into data, and create various reports and dashboards.
  4. Multidimensional Data Model: Real-time OLAP databases typically use a multidimensional data model, where data is organized into cubes or multi-dimensional structures that enable efficient slicing and dicing of data.
  5. Aggregation and Summarization: They allow for data aggregation and summarization to provide users with high-level insights and detailed analysis of data.
  6. Integration with Streaming Data Sources: Real-time OLAP databases are often integrated with streaming data platforms or event processing systems to capture and process data as it arrives.

It's important to note that building and maintaining a Real-time OLAP database can be a complex task due to the need for low-latency data processing and analytical capabilities. Various technologies and database management systems (DBMS) can be used to implement Real-time OLAP solutions, including in-memory databases, columnar databases, and specialized OLAP platforms with real-time data processing features.

Common use cases for real-time OLAP databases include real-time analytics in areas such as finance, e-commerce, monitoring and alerting, supply chain management, and more.

There are numerous popular real-time OLAP databases in the market, including Apache Druid, Apache Pinot, Clickhouse, Snowflake, and many more.

What is the difference

A streaming database and a real-time OLAP (Online Analytical Processing) database serve different purposes and have distinct characteristics, though they both deal with data in real-time or near-real-time scenarios. Here are the key differences between the two:

  1. Data Processing and Purpose:
    • Streaming Database: A streaming database is designed primarily for ingesting, processing, and analyzing high-velocity, time-sensitive data streams. It is optimized for handling continuous, real-time data streams from various sources, such as IoT devices, sensors, social media, or log data. Streaming databases are used for applications like real-time monitoring, fraud detection, and recommendation systems.
    • Real-time OLAP Database: A real-time OLAP database is tailored for complex analytical queries and reporting on large datasets in real time. It's used to support ad-hoc querying, data exploration, and decision-making by providing low-latency access to aggregated, multidimensional data. Real-time OLAP databases are used in scenarios like business intelligence, financial analysis, and operational reporting.
  2. Data Structure:
    • Streaming Database: Typically, streaming databases store and process data in a time-series fashion. They often use event-driven models and maintain a sliding window of data, allowing for quick access to recent data points.
    • Real-time OLAP Database: These databases use multidimensional data models, with a focus on pre-aggregating and indexing data to support complex analytical queries efficiently. Data is organized hierarchically, often with dimensions and measures.
  3. Query Complexity:
    • Streaming Database: Queries on streaming databases tend to be simpler and focused on real-time filtering, transformation, and basic aggregation of incoming data streams. The emphasis is on speed and responsiveness.
    • Real-time OLAP Database: Real-time OLAP databases are built to handle complex analytical queries that involve multiple dimensions, filtering, grouping, and aggregations. Users can perform ad-hoc queries to gain insights into historical and real-time data.
  4. Latency Requirements:
    • Streaming Database: Low-latency processing is critical for streaming databases to provide real-time insights and actions. They typically aim for sub-second to milliseconds response times.
    • Real-time OLAP Database: While real-time OLAP databases are expected to provide fast query responses, their latency requirements may be slightly more forgiving than streaming databases, often in the range of seconds to sub-seconds.
  5. Use Cases:
    • Streaming Database: Common use cases include real-time monitoring, anomaly detection, recommendation engines, and event-driven applications.
    • Real-time OLAP Database: Typical use cases involve business intelligence, interactive reporting, dashboards, and data exploration for decision support.


In summary, streaming databases are designed for handling high-velocity data streams in real time, while real-time OLAP databases are geared towards complex analytical queries on large datasets with a focus on multidimensional analysis. The choice between them depends on the specific requirements and goals of your data processing and analysis needs. In some cases, organizations may use both types of databases in tandem to cover a wide range of real-time data use cases.

sign up successfully_

Welcome to RisingWave community

Get ready to embark on an exciting journey of growth and inspiration. Stay tuned for updates, exclusive content, and opportunities to connect with like-minded individuals.

message sent successfully_

Thank you for reaching out to us

We appreciate your interest in RisingWave and will respond to your inquiry as soon as possible. In the meantime, feel free to explore our website for more information about our services and offerings.

subscribe successfully_

Welcome to RisingWave community

Get ready to embark on an exciting journey of growth and inspiration. Stay tuned for updates, exclusive content, and opportunities to connect with like-minded individuals.