The logo for kata drudid and superset, incorporating Real time analyitcs and Apache.

Real Time Analytics with Apache Kafka, Druid and Superset

Key takeaways

  • Real-time analytics involves processing data as it is generated to gain immediate insights and take action.
  • Apache Kafka, Apache Druid, and Apache Superset are three open-source tools that can be used together to create a real-time analytics stack.
  • By integrating and orchestrating these tools, organizations can build a powerful data processing pipeline that can handle large volumes of data and provide near-instantaneous insights.

Real-time analytics is becoming increasingly important in today’s data-driven world. With the ability to process data as it is generated, organizations can make more informed decisions and take immediate action.

Apache Kafka, Apache Druid, and Apache Superset are three powerful open-source tools that can be used together to create a real-time analytics stack. In short:

  • Apache Kafka is a distributed streaming platform that is used for building real-time data pipelines and streaming applications
  • Apache Druid is a high-performance, column-oriented, real-time analytics database that is optimized for OLAP queries
  • Apache Superset is a modern, enterprise-ready business intelligence web application that provides data visualization and exploration capabilities.

In this article, you will learn how to use Apache Kafka, Apache Druid, and Apache Superset to build a real-time analytics stack. You will gain an understanding of how each of these tools works and how they can be integrated and orchestrated to create a powerful data processing pipeline.

Understanding Real-Time Analytics

Real-time analytics is the process of gathering and analyzing data as it is generated, with the goal of providing insights and actionable information to businesses in real-time.

Real-time analytics is important for businesses that want to make quick decisions, respond to changing market conditions, and gain a competitive edge.

Real-time analytics is the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real time simply means the analytics is completed within a few seconds or minutes after the arrival of new data

The Importance of Real-Time Data

Real-time data is data that is generated and processed in real-time, as it is produced. This data is often used to monitor and analyze business operations, track customer behavior, and identify trends and patterns.

Real-time data is important because it allows businesses to respond quickly to changes in the market and make data-driven decisions in real-time.

Real-time data is particularly important for businesses that operate in fast-paced environments, such as e-commerce and financial services. In these industries, data freshness is critical to success. Real-time data can help businesses detect fraud, prevent downtime, and optimize customer experiences.

Key Concepts in Real-Time Analytics

Real-time analytics applications are designed to process data in real-time, using a variety of techniques and technologies. Some of the key concepts in real-time analytics include:

  • Stream processing: Stream processing is the process of analyzing data as it is generated, in real-time. Stream processing techniques can be used to detect and respond to events in real-time, such as fraud detection or predictive maintenance.
  • Data ingestion: Data ingestion is the process of collecting and processing data from a variety of sources, such as sensors, social media, or customer interactions. Data ingestion techniques can be used to collect and process data in real-time, allowing businesses to respond quickly to changes in the market.
  • Real-time dashboards: Real-time dashboards are visual representations of data that are updated in real-time. Real-time dashboards can be used to monitor business operations, track customer behavior, and identify trends and patterns.

Real-time analytics is a powerful tool for businesses that want to stay competitive in today’s fast-paced market. By leveraging real-time data and analytics, businesses can gain valuable insights and make data-driven decisions in real-time.

A Superset dashboard showcasing a world map visualization using Kafka for data processing.

Apache Kafka for Data Streaming

If you’re looking for an event streaming platform that can handle high-throughput, Apache Kafka is the perfect solution.

Kafka is a distributed, open-source platform that is used by thousands of companies for real-time data streaming, data integration, and mission-critical applications.

Apache Kafka Logo

Kafka Architecture and Components

Kafka’s architecture is based on a distributed commit log, which is a highly scalable and fault-tolerant data structure. The platform consists of several components, including producers, consumers, brokers, and ZooKeeper.

Producers are responsible for publishing data to Kafka topics, while consumers read data from those topics. Brokers are the servers that store and manage the data, while ZooKeeper is used for coordination and synchronization.

A diagram of the Apache Kafka cluster for real-time analytics.

Image inspiration: Kafka documentation

Kafka Topics and Stream Processing

Kafka topics are the channels through which data is transmitted. Each topic is divided into one or more partitions, which are distributed across the brokers in the cluster. Partitioning allows for parallel processing of the data, which improves throughput and scalability.

Kafka also supports stream processing, which allows you to perform real-time analytics on the data as it flows through the system. Stream processing can be done using Kafka Streams or other stream processing frameworks.

Read more about topics in the kafka introduction

Kafka Connect and Integrations

Kafka Connect is a framework that allows you to easily integrate Kafka with other systems. Connectors are used to move data between Kafka and external systems, such as databases, message queues, and Hadoop.

Kafka Connect supports both source connectors, which pull data from external systems into Kafka, and sink connectors, which push data from Kafka to external systems. This makes it easy to build data pipelines that can stream data in and out of Kafka.

Read more about connect in the kafka documentation


Apache Druid for Real-Time Analytics Databases

Apache Druid is a high performance, real-time analytics database that delivers sub-second queries on streaming and batch data at scale and under load.

Druid is designed for workflows where fast ad-hoc analytics, instant data visibility, or supporting high concurrency is important. As such, Druid is often used to power UIs where an interactive, consistent user experience is desired.

Apache Druid Logo

Understanding Druid’s Architecture

Druid is a column-oriented database that is optimized for OLAP queries. Druid’s architecture is designed to allow for high ingest rates of both real-time and batch data, efficient querying of that data, and horizontal scalability. Druid’s architecture consists of a cluster of nodes, each of which performs a specific function. The nodes in a Druid cluster are:

  • Coordinator Nodes: These nodes are responsible for managing the cluster state and coordinating ingestion and querying tasks.
  • Historical Nodes: These nodes store historical data in segments and serve queries against that data.
  • Real-time Nodes: These nodes receive real-time data and perform indexing tasks to create new segments of data.
  • Broker Nodes: These nodes act as a query router, forwarding queries to the appropriate Historical or Real-time nodes.
A diagram of an Apache driud cloud storage system.

Image source: Apache Druid

Real-Time Data Ingestion in Druid

Druid supports both batch and stream ingestion. Batch data is ingested using indexing tasks, which are responsible for creating segments of data that can be queried by Historical nodes.

Real-time data is ingested using ingestion tasks, which are responsible for creating real-time segments of data that can be queried by Real-time nodes. Druid’s stream ingestion is based on Apache Kafka, which allows for low latency and high availability.

A diagram illustrating the real-time analytics capabilities of Druid using Kafka as a data source and Superset as a visualization tool.

For more information, see the druid docs page.

Querying Data with Druid

Druid supports sub-second queries on both real-time and historical data. Druid’s query engine is optimized for OLAP queries, and supports a SQL-like query language called Druid Query Language (DSL).

Druid supports a variety of aggregation functions, including count, sum, min, max, and average. Druid also supports filtering data based on specific criteria, such as time ranges, dimensions, and metrics. Druid’s query engine is highly parallelized, allowing for efficient querying of large datasets.


Apache Superset for Data Visualization

Apache Superset is an open-source data visualization tool that allows you to explore and visualize your data in real-time. It is fast, lightweight, and intuitive, making it easy for users of all skill levels to create interactive dashboards and visualizations.

Apache Superset logo

Creating Real-Time Dashboards

With Apache Superset, you can create real-time dashboards that allow you to monitor your data as it changes. You can set up automatic refresh intervals to ensure that your dashboards are always up-to-date, and you can use a variety of visualization types to display your data in the way that makes the most sense to you.

A Superset dashboard showcasing a world map visualization using Kafka for data processing.

One of the key features of Apache Superset is its ability to pivot and groupby your data.

This means that you can quickly and easily create tables, charts, and other visualizations that allow you to see how your data is changing over time. You can also use filters and slicers to drill down into your data and explore it in more detail.

Advanced Visualization Features

In addition to its real-time dashboarding capabilities, Apache Superset also offers a range of advanced visualization features.

You can create heatmaps, scatter plots, and other advanced visualizations that allow you to see patterns and trends in your data that might not be immediately apparent.

Visual representation of Apache Superset dashboard

Apache Superset also supports a wide range of data sources, including Apache Druid, which is a high-performance real-time analytics database.

This means that you can use Apache Superset to explore and visualize data that is being generated in real-time, allowing you to make informed decisions based on the latest information.

A screen shot of a dashboard featuring various charts and graphs using Superset.

Overall, Apache Superset is a powerful and flexible data visualization tool that makes it easy to explore and understand your data. Whether you are a data analyst, a business user, or a developer, Apache Superset has the features and capabilities you need to create compelling visualizations that help you make sense of your data.

Integration and Orchestration

Real-time analytics requires a seamless integration of different tools and technologies. Apache Kafka, Druid, and Superset are three such tools that work together to provide a comprehensive real-time analytics solution.

Combining Kafka, Druid, and Superset

Apache Kafka is a distributed streaming platform that can handle high volume, high throughput data streams. Kafka’s integration with Apache Druid, a high-performance, real-time analytics database, allows for sub-second queries on streaming and batch data at scale and under load. Druid is highly optimized for OLAP queries and can handle complex aggregations and filtering.

Apache Superset, on the other hand, is an open-source business intelligence platform that allows users to create and share interactive dashboards. Superset’s integration with Kafka and Druid enables users to create real-time dashboards that update automatically as data streams in.

A diagram illustrating the Apache Kafka Druid Superset connected to API

Data Pipelines and ETL Processes

Data pipelines and ETL (Extract, Transform, Load) processes are critical components of real-time analytics. Kafka’s ability to handle high volume data streams makes it an ideal tool for data ingestion. Kafka Connect, a framework for building and running reusable data pipelines between Kafka and other data systems, enables users to move data between Kafka and other data sources such as databases, file systems, and data warehouses.

Apache Druid’s integration with Apache Beam, a unified programming model for batch and streaming data processing, allows for easy ETL processes. Beam’s integration with Druid enables users to write code that transforms data in real-time and loads it into Druid for fast querying and analysis.

Performance and Scalability

Real-time analytics requires a high-performance and scalable infrastructure that can handle high volumes of data with low latency.

Apache Kafka, Apache Druid, and Apache Superset are designed to provide high throughput, low latency, and scalability. In this section, we will discuss how these technologies ensure high performance and scalability.

Ensuring High Throughput and Low Latency

Apache Kafka is a distributed streaming platform that can handle millions of events per second. It provides high throughput and low latency by partitioning data across multiple servers and allowing consumers to read data in parallel.

A group of people standing around a server discussing Data Strategy and Audit Data.

Kafka also supports multiple subscribers, which means that multiple applications can consume the same data stream concurrently. This feature allows you to build real-time applications that can handle high volumes of data with low latency.

Apache Druid is a column-based distributed database that is designed to ingest high volumes of data and provide low-latency queries. Druid can handle millions of events per second and can provide sub-second query response times.

Druid’s column-based architecture allows for efficient compression and faster query performance. Druid also provides real-time ingestion, which means that data can be queried as soon as it is ingested.

Scalability and Elastic Architecture

Apache Kafka, Apache Druid, and Apache Superset are designed to be scalable and fault-tolerant. Kafka can scale horizontally by adding more brokers to the cluster. Druid can scale horizontally by adding more nodes to the cluster. Superset can scale horizontally by adding more worker nodes to the cluster.

A set of colorful cubes on a black background, representing a data Architecture

Kafka, Druid, and Superset are also designed to be elastic. They can automatically adjust the resources allocated to them based on the workload.

For example, Kafka can dynamically allocate partitions to brokers based on the workload. Druid can dynamically allocate segments to nodes based on the workload. Superset can dynamically allocate workers to handle queries based on the workload.

Security and Reliability

Apache Kafka, Apache Druid, and Apache Superset provide a robust and secure platform for real-time analytics. In this section, we will discuss some best practices for ensuring security and reliability when using these tools.

Data Security Best Practices

Data security is a critical concern when implementing real-time analytics solutions. Apache Kafka, Apache Druid, and Apache Superset provide several security features that can help ensure the confidentiality, integrity, and availability of your data.

To ensure data security, you should follow these best practices:

  • Use secure communication channels: Apache Kafka, Apache Druid, and Apache Superset support secure communication channels such as SSL/TLS. You should configure these tools to use secure communication channels to protect your data from unauthorized access.
  • Implement access control: Apache Kafka, Apache Druid, and Apache Superset provide access control mechanisms that allow you to restrict access to your data. You should implement access control to ensure that only authorized users can access your data.
  • Encrypt your data: Apache Kafka, Apache Druid, and Apache Superset support data encryption. You should encrypt your data to protect it from unauthorized access.

Ensuring Fault Tolerance and High Availability

Fault tolerance and high availability are critical requirements for mission-critical applications. Apache Kafka, Apache Druid, and Apache Superset provide several features that can help ensure fault tolerance and high availability.

To ensure fault tolerance and high availability, you should follow these best practices:

  • Use replication: Apache Kafka and Apache Druid support data replication. You should configure replication to ensure that your data is available even if one or more nodes fail.
  • Use load balancing: Apache Kafka, Apache Druid, and Apache Superset support load balancing. You should use load balancing to distribute the load across multiple nodes and ensure high availability.
  • Monitor your system: You should monitor your system to detect and resolve issues before they cause downtime. Apache Kafka, Apache Druid, and Apache Superset provide several monitoring tools that can help you monitor your system and ensure high availability.
A man is standing in front of a computer screen with colorful data graphs on it.

Advanced Topics and Techniques

Let’s touch upon some more advanced topics for real time analytics

Real-Time Analytics Patterns

One of the most common patterns is the “lambda architecture,” which involves using both batch and real-time processing to handle large volumes of data. This approach can be particularly useful when dealing with data that is constantly changing, such as social media streams or IoT sensor data.

Another pattern that can be useful is the “Kappa architecture,” which involves using only real-time processing to handle data. This approach can be more efficient than the lambda architecture in some cases, especially when dealing with data that is not as volatile.

Optimizing for Fast Query Performance

To optimize query performance with Apache Kafka, Apache Druid, and Apache Superset, there are several techniques that you can use. One of the most important is to ensure that your SQL queries are as efficient as possible. This means using indexes and avoiding unnecessary joins whenever possible.

Another important technique is to ensure that your system is optimized for concurrency. This means using techniques such as sharding to distribute load across multiple machines, and using caching to reduce the number of queries that need to be executed.

Finally, it’s important to ensure that your system is designed to provide real-time insights. This means ensuring that your queries can be executed in sub-second timeframes, and that your system is capable of handling large volumes of data in real-time.

A woman is standing in front of a colorful graph illustrating AI-powered root cause analysis.

Apache Superset, Kafka and Druid: The Essentials

The combination of Apache Kafka, Druid, and Superset presents a formidable solution for real-time analytics, offering organizations the ability to harness the power of streaming data for actionable insights.

  • Apache Kafka is a distributed streaming platform that is used for building real-time data pipelines and streaming applications
  • Apache Druid is a high-performance, column-oriented, real-time analytics database that is optimized for OLAP queries
  • Apache Superset is a modern, enterprise-ready business intelligence web application that provides data visualization and exploration capabilities.

This dynamic trio empowers businesses to process, analyze, and visualize data in real time, fostering informed decision-making and driving innovation in today’s fast-paced digital landscape.

Key Takeaways: Apache Druid, Apache Superset, and Apache Kafka

  • Real-Time Data Processing: The integration of Apache Kafka, Druid, and Superset enables real-time data processing, allowing organizations to derive immediate insights from streaming data sources.
  • Scalable Analytics: Leveraging this combination provides scalable analytics capabilities, accommodating the processing and analysis of large volumes of real-time data.
  • Actionable Insights: Businesses can derive actionable insights from real-time data streams, empowering proactive decision-making and swift responses to dynamic market conditions.
  • Streamlined Visualization: Apache Kafka, Druid, and Superset offer streamlined visualization of real-time data, facilitating the creation of interactive and insightful dashboards for rapid analysis.
  • Continuous Innovation: The synergy between these tools fosters a culture of continuous innovation, allowing organizations to stay ahead in the era of real-time analytics and data-driven strategies.

FAQ: Apache Superset, Apache Kafka and Apache Druid for Real Time Analysis

How can Apache Kafka be utilized for real-time data processing?

Apache Kafka is a distributed streaming platform that can be used for real-time data processing. Kafka enables you to collect and process large amounts of data in real-time, making it an ideal choice for streaming analytics. Kafka’s architecture is designed to handle high volumes of data, and it can be used to build real-time data pipelines, process streams of data, and store data for later analysis.

What are the benefits of using Apache Superset for real-time dashboards?

Apache Superset is a modern, enterprise-ready business intelligence web application that provides real-time dashboards and visualizations. Superset is designed to be easy to use and flexible, making it an ideal choice for real-time analytics. Some of the benefits of using Superset for real-time dashboards include its ability to handle large volumes of data, its support for multiple data sources, and its customizable dashboards.

What are the key differences between Apache Kafka and Apache Druid in real-time analytics?

Apache Kafka and Apache Druid are both powerful tools for real-time analytics, but they have different strengths and use cases. Kafka is primarily a messaging system that is designed to handle high volumes of data and provide real-time data processing capabilities. Druid, on the other hand, is a high-performance, column-oriented data store that is optimized for OLAP queries and real-time analytics.

How does Apache Druid integrate with Kafka for real-time analytics?

Apache Druid can be integrated with Apache Kafka to provide real-time analytics capabilities. Druid can consume streaming data from Kafka to enable analytical queries, and it can ingest data at a rate of millions of events per second. Kafka provides high throughput event delivery, making it an ideal choice for real-time data processing.

What are the latest updates or versions in Apache Druid that enhance real-time analytics capabilities?

Apache Druid has several new updates and versions that enhance its real-time analytics capabilities. Some of the latest updates include improved query performance, support for SQL, and enhanced security features. Additionally, Druid has added support for streaming data ingestion, making it an even more powerful tool for real-time analytics.

Share
Eric J.
Eric J.

Meet Eric, the data "guru" behind Datarundown. When he's not crunching numbers, you can find him running marathons, playing video games, and trying to win the Fantasy Premier League using his predictions model (not going so well).

Eric passionate about helping businesses make sense of their data and turning it into actionable insights. Follow along on Datarundown for all the latest insights and analysis from the data world.