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.NET for Apache Spark VS Apache Druid

Compare .NET for Apache Spark VS Apache Druid and see what are their differences

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.NET for Apache Spark logo .NET for Apache Spark

.NET for Apache Spark™ provides C# and F# language bindings for the Apache Spark distributed data analytics engine. Supported on Linux, macOS, and Windows.

Apache Druid logo Apache Druid

Fast column-oriented distributed data store
  • .NET for Apache Spark Landing page
    Landing page //
    2023-05-23
  • Apache Druid Landing page
    Landing page //
    2023-10-07

.NET for Apache Spark features and specs

No features have been listed yet.

Apache Druid features and specs

  • 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.

Possible disadvantages of Apache Druid

  • Complex Setup and Configuration
    Setting up and configuring Apache Druid can be complex and resource-intensive. It requires a good understanding of its architecture and components, which may pose a steep learning curve for beginners.
  • Resource Heavy
    Druid can be resource-intensive, often requiring significant CPU, memory, and disk resources, especially when handling large scale data and high query loads. This can result in increased infrastructure costs.
  • Limited Transactional Support
    Druid is not designed for transactional workloads and lacks full ACID compliance. It is optimized for read-heavy analytical queries rather than write-heavy transactional operations.
  • Complexity in Handling Updates
    Updating or deleting existing records in Druid is not straightforward and often involves re-indexing data. This can complicate use cases where mutable data is a common requirement.
  • Limited Tooling and Ecosystem
    Compared to more established databases and analytical engines, Druid's ecosystem and available tooling for development, monitoring, and management might be less extensive, potentially requiring custom solutions.

.NET for Apache Spark videos

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Apache Druid videos

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An introduction to Apache Druid

More videos:

  • Review - Building a Real-Time Analytics Stack with Apache Kafka and Apache Druid

Category Popularity

0-100% (relative to .NET for Apache Spark and Apache Druid)

.NET for Apache Spark

Apache Druid

PHP Web Framework
100 100%
PHP Web Framework
0% 0
Databases
0 0%
Databases
100% 100
Data Integration
100 100%
Data Integration
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare .NET for Apache Spark and Apache Druid

.NET for Apache Spark Reviews

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Apache Druid Reviews

Rockset, ClickHouse, Apache Druid, or Apache Pinot? Which is the best database for customer-facing analytics?
“When you're dealing with highly concurrent environments, you really need an architecture that’s designed for that CPU efficiency to get the most performance out of the smallest hardware footprint—which is another reason why folks like to use Apache Druid,” says David Wang, VP of Product and Corporate Marketing at Imply. (Imply offers Druid as a service.)
Source: embeddable.com
Apache Druid vs. Time-Series Databases
Druid is a real-time analytics database that not only incorporates architecture designs from TSDBs such as time-based partitioning and fast aggregation, but also includes ideas from search systems and data warehouses, making it a great fit for all types of event-driven data. Druid is fundamentally an OLAP engine at heart, albeit one designed for more modern, event-driven...
Source: imply.io

Social recommendations and mentions

Based on our record, Apache Druid should be more popular than .NET for Apache Spark. It has been mentiond 10 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

.NET for Apache Spark mentions (3)

  • Debug dotnet Spark using Databricks-connect
    I assume you are talking about this https://dotnet.microsoft.com/en-us/apps/data/spark. Source: over 2 years ago
  • Microsoft Announces new Scalable Machine Learning Library for .NET
    Good question! The API and the authoring experience is .NET, but the backend is Apache Spark which is built on the JVM. We use the .NET for Apache Spark to do the parallization. Source: almost 3 years ago
  • Microsoft Announces new Scalable Machine Learning Library for .NET
    Yes that's correct. SynapseML builds on top of the Apache Spark for .NET project which provides .NET support for the Apache Spark distributed computing framework. Apache Spark is written in Scala (a language on the JVM) but has language bindings in Python, R, .NET and other languages. This release adds full .NET language support for all of the models and learners in the SynapseML library so you can author... Source: almost 3 years ago

Apache Druid mentions (10)

  • Why You Shouldn’t Invest In Vector Databases?
    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 / 26 days ago
  • How to choose the right type of database
    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 / about 1 year ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    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 1 year ago
  • Analysing Github Stars - Extracting and analyzing data from Github using Apache NiFi®, Apache Kafka® and Apache Druid®
    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. Here’s... - Source: dev.to / over 2 years ago
  • Apache Druid® - an enterprise architect's overview
    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 2 years ago
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What are some alternatives?

When comparing .NET for Apache Spark and Apache Druid, you can also consider the following products

Apache Flume - Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Vertica - Vertica is a grid-based, column-oriented database designed to manage large, fast-growing volumes of...

ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Printopia - Printopia is a wireless printing application that allows users to print anything directly from their iPhone or iPad.

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