Name: Demonstrating Apache Druid Rollup
Uploaded: 2019-10-19T15:23:29.000Z
Duration: 18 min 51 s
Description: Demonstrating Apache Druid Rollup

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### Data Ingestion and Integration
Users generally praise Apache Druid's data ingestion and integration capabilities. They appreciate the ease of integration with various databases like MySQL and the out-of-the-box connectors for popular data sources such as Apache Kafka, HDFS, and AWS S3. The ability to ingest data in real-time from streaming sources like Kafka and the support for schema-less data sources are highlighted as key strengths. However, some users express concerns about inefficiencies in bulk data extraction and the complexity of creating and debugging ingestion specs. Despite these challenges, Apache Druid's data ingestion and integration features are seen as valuable for powering analytical dashboards and handling real-time streaming data effectively.

### Integration with Kafka and Other Connectors
Users widely praise Apache Druid's robust integration with Kafka and its array of built-in connectors, citing them as key strengths of the platform. The ease of use in both data insertion through Kafka and querying processes has been highlighted as intuitive and user-friendly. This positive sentiment towards the integration capabilities with Kafka and other connectors underscores Apache Druid's appeal for real-time streaming applications and data processing needs.

### Storage and Data Management
Users consistently highlight Apache Druid's robust storage and data management capabilities, emphasizing its ability to handle large volumes of data efficiently. The platform's column-oriented storage and time series database functionality are particularly praised for enabling low-latency queries and real-time analytics. Additionally, the ease of integration with various cloud infrastructures and the support for horizontal scalability are noted as significant advantages in enhancing data resilience and facilitating rapid data ingestion. While some caution against heavy dependence on joins due to potential performance impacts, overall, Apache Druid's storage and data management features are viewed favorably by users for powering event-driven analytic workloads effectively.

### Query Performance and Execution
Users consistently highlight Apache Druid's query performance and execution as standout features, praising its ability to deliver sub-second query responses and support for complex queries. While some users mention limitations such as issues with high cardinality dimensions and lack of intuitive error messages, overall, the community appreciates Druid's fast and efficient query processing. The open-source nature of Druid, coupled with its strong community support and impressive scalability for ingesting and querying data, further solidify its reputation as a reliable platform for query performance and execution.

### SQL Support and Complex Query Handling
Reviewers have expressed mixed opinions regarding Apache Druid's SQL support and complex query handling capabilities. While some users find the SQL query support convenient and efficient for querying Druid, others have highlighted limitations when dealing with multiple joins in complex queries. The ability to pre-aggregate data and the REST interface for communication have been praised, but concerns have been raised about the difficulty of picking up Druid's native JSON query format for SQL users. Additionally, the lack of dynamic rollover queries and the limitations in handling joins between data sources have been noted as areas for improvement. Despite these challenges, Apache Druid remains a valuable tool for real-time analytic workloads, particularly for event-driven data analysis.

### Data Aggregation and Reporting
Reviewers generally agree that Apache Druid excels in data aggregation and reporting capabilities. Users appreciate the pre-aggregate capability that reduces compute and storage costs, as well as the ease of creating ingestion_specs via the Druid UI. The REST interface for druid_broker simplifies integration with microservices, and the support for SQL queries makes it user-friendly for business analysts. Additionally, the data security options provided by Druid are seen as valuable features. Despite some limitations in compatibility with certain data analytics platforms, Apache Druid's data aggregation and reporting functionalities are widely praised for their efficiency and effectiveness.

According to the Apache Software Foundation, Apache Druid is a high-performance, real-time analytics database designed to deliver sub-second queries on streaming and batch data at scale and under load. It is positioned as a solution for powering real-time analytics applications that require fast queries and high uptime. Apache Druid is said to be suitable for companies of all sizes, from small businesses to large enterprises. It caters to a wide range of professions and industries, including data analysts, data engineers, business intelligence professionals, e-commerce companies, and digital advertising agencies.

## Key Features

According to the vendor, Apache Druid offers an interactive query engine that utilizes scatter/gather for high-speed queries. Queries can be processed in parallel, enabling sub-second performance for most queries, even with very large data sets. The tiering and quality of service feature allows for configurable tiering, guaranteeing priority and avoiding resource contention. This feature enables fine-tuning of cluster resources for optimal performance.

Apache Druid automatically optimizes the data format by columnarizing, time-indexing, dictionary-encoding, bitmap-indexing, and type-aware compressing the ingested data. This optimization provides fast filtering and searching across multiple columns with compressed bitmap indexes. It also optimizes storage by compressing string columns using dictionary encoding and numeric columns using compressed raw values.

The elastic architecture of Apache Druid consists of loosely coupled components for ingestion, queries, and orchestration. This architecture enables easy scale-up and scale-out with a deep storage layer, providing flexibility and quick scalability to handle large aggregations and high-performance applications.

According to the vendor, Apache Druid offers true stream ingestion with connector-free integration with streaming platforms such as Apache Kafka and Amazon Kinesis. This feature enables query-on-arrival, high scalability, low latency, and guaranteed consistency. Apache Druid supports the ingestion of millions of events per second and continuous backup into deep storage.

The vendor claims that Apache Druid ensures non-stop reliability through automatic data services, including continuous backup, automated recovery, and multi-node replication. These services are designed to ensure high availability and durability of data, providing a reliable and fault-tolerant system for critical applications.

Apache Druid features schema auto-discovery, which allows for automatic detection, definition, and updating of column names and data types upon ingestion. This feature provides the ease of schemaless data ingestion with the performance of strongly typed schemas, reducing the need for manual schema management and improving data ingestion efficiency.

The flexible joins support in Apache Druid enables join operations during data ingestion and at query-time execution. This feature provides the fastest query performance when tables are pre-joined during ingestion, enabling efficient data analysis across multiple dimensions and tables.

Developers and analysts can leverage the familiar SQL API for end-to-end data operations in Apache Druid. The vendor states that Apache Druid supports SQL-based queries for ingestion, transformation, and querying of data, simplifying the adoption and integration of Druid into existing data workflows.

Apache Druid solves the key business problem of real-time ingestion and analytical queries on high volume data. According to users, it allows them to ingest data in real-time from streaming sources and aggregate it for serving analytical dashboards. It provides fast querying capabilities, slice and dice, and stable datastore setup with minimal maintenance. Users benefit from OLAP data analytics, reporting business metrics, and powering different UIs with real-time aggregation and grouping problems solved for high volume data processing.

Druid is a good alternative for visualization and BI tools with low latency query results in the UI. It enables users to retire several existing third-party systems, power front ends, and reporting for users, providing quick real-time insights into business data. One reviewer mentioned that they used Druid to store syslog data from network devices at a huge rate and perform analytics on streaming data, allowing them to communicate to customers how their marketing campaigns are performing in real-time. Overall, Apache Druid is a versatile solution that addresses the growing analytical demands of businesses while offering stable performance and scalability.

Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture.

Apache Druid

Time Series Databases (TSDB) are designed to store and analyze event data, time series, or time-stamped data, often streamed from IoT devices, and enables graphing, monitoring and analyzing changes over time.

Time Series Databases

Interactive Realtime Dashboards On Data Streams Using Apache Kafka ,Druid And Superset

Apache Druid 24 multi-stage query showcase

Bangalore Apache Druid Meetup on April 13, 2021

Talk #1: Druid at Scale: Anatomy and Industry Use Cases

Abstract: Apache Druid is an analytics engine powering real-time, lightning fast analytics – and is probably the only engine which provides sub second query latency over huge data volume. In this talk, we will discuss Druid architecture and Druid-related projects, as well as case studies from well-known industry use cases.

Bio: Tijo Thomas is a Senior Solutions Architect at Imply with over 18 year of experience as a programmer/architect. Prior to Imply, Tijo was a big data Solutions Architect at Cloudera, where he dealt with big data challenges using tools like Spark, Druid, Kafka, and others. He has worked with customers across APAC by providing solutions to their big data challenges, and is keen on sharing his experiences.

Talk #2: Using Apache Druid as a Real-time Analytical Datastore for Observability Data in SigNoz

Abstract: SigNoz (https://signoz.io/) is an open-source observability platform. For some time the industry has been using Elastic and Cassandra as datastores for distributed tracing data. In the talk, we shall discuss the current state of features in the latest distributed tracing tool called Jaeger, and what analytical capabilities Druid provides in SigNoz.

We shall also discuss the real-time data ingestion architecture in SigNoz powered by Druid's Kafka ingestion supervisor. From the reasons of design choice, the installation steps, the problems faced, the help from the community, to the roadmap of upcoming adventures in SigNoz, this talk will be a starter and exploratory journey towards using Druid. We shall end with a demo of SigNoz to see Druid in action.

Bio: Ankit Nayan is a co-founder and maintainer at SigNoz which is a part of the recent Y Combinator W21 batch. Ankit loves to discuss new technologies and the problems they solve. He believes technology will be more profoundly used in the future to scale business needs. He is now passionate about the application performance monitoring space and loves talking to developers about how they do it today. When not working he loves to play badminton and go on adventurous trips. He has done many Himalayan trips earlier and is a philanthropist.

Apache Druid Meetup featuring SigNoz.io

Project Shapeshift demo: SQL ingest for Druid

Likelihood to Recommend

The Apache Software Foundation provides support for the Apache community of open-source software projects. The Apache projects are characterized by a collaborative, consensus based development process, an open and pragmatic software license, and a desire to create high quality software that leads the way in its field.

We consider ourselves not simply a group of projects sharing a server, but rather a community of developers and users.

Apache Druid 0.21.0 Quickstart, Lookups, and JOINs

A quick walk-through of the Apache Druid quickstart followed by loading some lookups and using them in queries.

Demonstrating Apache Druid Rollup

A brief look at Apache Druid's rollup feature that greatly speeds queries and reduces storage requirements. 
For more info check out https://druid.apache.org/docs/latest/tutorials/tutorial-rollup.html

Imply transforms how businesses run by integrating real-time intelligence into their operations. Founded by the authors of the Apache Druid database, Imply provides a cloud-native solution that delivers real-time ingestion, interactive ad-hoc queries, and intuitive visualizations for many types of event-driven and streaming data flows. Imply has operations in North America, Europe, and Asia Pacific and is backed by Andreesen Horowitz, Khosla Ventures, and Geodesic Capital.

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Website: https://imply.io/
Linkedin: https://www.linkedin.com/company/imply/
Twitter: https://twitter.com/implydata
Github: https://github.com/implydata
Slideshare: https://www.slideshare.net/implydata

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Apache Druid 2024-01-02 09:38:16

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We use Druid for rapid ingest of a variety of data sources, including traditional databases, Kafka topics, and data stored in Hadoop. Our users enjoy the easy creation of ingest specs, and the ability to ingest only the relevant columns/fields required for their programs and queries. Being able to translate and enrich data during ingest is a huge plus.

Rapid ingest
Limiting ingest to only the relevant fields/columns
Easy ingest spec creation

Security configuration is problematic
Cluster management could have more features
Troubleshooting incomplete tasks/jobs is a chore

Integration with S3 storage has saved about 35% on our storage, over HDFS
The rapid ingest has saved user's time in the query aspects of their applications.
The ability to ingest from a variety of data sources has made overall user application queries much simpler

Apache Kafka, Cloudera Distribution Hadoop (CDH), Apache Spark

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What is Druid?