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Apache Druid
Fast column-oriented distributed data store.
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Real-Time Data Ingestion
Apache Druid supports real-time data ingestion, which allows users to immediately query and analyze freshly ingested data, making it ideal for applications that require up-to-the-minute insights.
High Performance
Druid is designed to provide fast query performance, especially for OLAP (Online Analytical Processing) queries. Its architecture leverages techniques like indexing, compression, and shard-based parallel processing to deliver quick results, even on large data sets.
Scalability
Druid's architecture allows it to scale horizontally, supporting both large amounts of data and numerous concurrent queries. This makes it suitable for systems that need to handle high scalability requirements.
Flexible Data Exploration
It supports complex queries, including group-bys, filters, and aggregations, which are essential for exploratory data analysis. Users can perform a wide range of data slicing and dicing operations.
Rich Multi-Tenancy Support
Druid supports multi-tenancy, enabling different user groups to access and query the database simultaneously without performance degradation, thus accommodating diverse data analytics requirements within the same system.
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Check the traffic stats of Apache Druid on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Apache Druid on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Apache Druid's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Apache Druid on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Apache Druid on Reddit. This can help you find out how popualr the product is and what people think about it.
Regarding the storage aspect of vector databases, it is noteworthy that indexing techniques take precedence over the choice of underlying storage. In fact, many databases have the capability to incorporate indexing modules directly, enabling efficient vector search. Existing OLAP databases that are designed for real-time analytics and utilizing columnar storage, such as ClickHouse, Apache Pinot, and Apache Druid,... - Source: dev.to / over 1 year ago
Apache Druid: Focused on real-time analytics and interactive queries on large datasets. Druid is well-suited for high-performance applications in user-facing analytics, network monitoring, and business intelligence. - Source: dev.to / over 2 years ago
Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in... - Source: dev.to / over 2 years ago
Spencer Kimball (now CEO at CockroachDB) wrote an interesting article on this topic in 2021 where they created spencerkimball/stargazers based on a Python script. So I started thinking: could I create a data pipeline using Nifi and Kafka (two OSS tools often used with Druid) to get the API data into Druid - and then use SQL to do the analytics? The answer was yes! And I have documented the outcome below. Hereโs... - Source: dev.to / over 3 years ago
Apache Druid is part of the modern data architecture. It uses a special data format designed for analytical workloads, using extreme parallelisation to get data in and get data out. A shared-nothing, microservices architecture helps you to build highly-available, extreme scale analytics features into your applications. - Source: dev.to / over 3 years ago
Datadog's product is a bit too close to Apache Druid to have named their design system so similarly. From https://druid.apache.org/ : > Druid unlocks new types of queries and workflows for clickstream, APM, supply chain, network telemetry, digital marketing, risk/fraud, and many other types of data. Druid is purpose built for rapid, ad-hoc queries on both real-time and historical data. - Source: Hacker News / almost 4 years ago
Maybe rare for someone to be seeking their first coding job at that age. But plenty of us are in our 50s or older and still coding up a storm. And not necessarily ancient tech or anything. My current project exposes analytics data from Apache Druid and Cassandra via Go microservices hosted in K8s. Source: almost 4 years ago
The configuration items have 108 pieces of real tables in a database. According to the configuration of maxConnectionsizeperquery=50, ShardingSphere-JDBC uses the connection limit mode, divides the query requests into three groups, and merges the results with in-memory. As a result, 36 database connections are required for one query. But the maxActive configured by the druid thread pool is set to 20, resulting in... - Source: dev.to / almost 4 years ago
Personally I'd use Google Analytics or something else purpose-built for this, and avoid trying to reinvent the wheel. I don't know much about it, but Apache Druid is supposed to be for analytics data: https://druid.apache.org/. Source: almost 4 years ago
SigNoz is an opensource observability platform. SigNoz uses distributed tracing to gain visibility into your systems and powers data using Kafka (to handle high ingestion rate and backpressure) and Apache Druid (Apache Druid is a high performance real-time analytics database), both proven in the industry to handle scale. Source: over 5 years ago
Apache Druid has been capturing attention as a powerful real-time analytics database capable of handling the demanding requirements of modern data environments. Highlighted frequently in discussions comparing it to other prominent databases like Apache Pinot, ClickHouse, and Rockset, Druid is lauded for its efficiency in high-concurrency environments and its ability to deliver high-performance analytics with a minimal hardware footprint.
Real-time Analytics and OLAP Capabilities
Druid's design marries the functionalities of traditional OLAP systems with features typical of time-series databases and search systems. This hybrid architecture facilitates swift aggregation and time-based partitioning, enhancing its suitability for event-driven data architectures. Its reputation as an OLAP engine with a real-time focus makes it particularly attractive for applications requiring interactive query performance on large datasets, such as user-facing analytics, network monitoring, and business intelligence.
Industry Use Cases and Synergies
The database is widely recognized for its adaptability in modern data pipelines. It seamlessly integrates with tools like Apache NiFi and Kafka, which are often employed for data ingestion into Druid where SQL can then be used for comprehensive analytics. This functionality is especially beneficial in analyzing continuous data streams, such as clickstream data, thereby solidifying its role in monitoring network telemetry, digital marketing metrics, and APM (Application Performance Management).
Moreover, Druid's adoption is seen among enterprises seeking to enhance their data architectures with extreme parallelization and scalability. Its microservices-based, shared-nothing architecture aligns with modern best practices in developing robust, scalable analytics features.
Comparative Advantages and Considerations
Apache Druid distinguishes itself from its competitors by its ability to handle both real-time and historical data efficiently. This versatility is complemented by the advantages of columnar storage, which leads to impressive data compression and heightened query performance, especially relevant when dealing with vector data.
However, Druid's proficiency is not all-encompassing. Discussions around its capabilities highlight potential challenges when comparing it to streaming databases for incremental computation. While it excels in OLAP scenarios, maintaining the freshness of results in high-frequency streaming applications can pose some limitations.
Conclusion
Public opinion on Apache Druid situates it as a formidable player in the domain of real-time analytics databases. Its design philosophy, grounded in efficiency and minimalism, resonates well with users seeking to enhance performance without a corresponding increase in hardware demand. For enterprises and developers focused on constructing high-performance analytics applications, especially those at scale, Druid offers compelling advantages. Its integration with other open-source tools, adaptability for complex analytics workloads, and capability to handle diverse data types firmly entrench it among the preferred choices for modern data solutions, despite a few challenges in specific streaming data scenarios.
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