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Apache Druid VS Databricks

Compare Apache Druid VS Databricks and see what are their differences

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Apache Druid logo Apache Druid

Fast column-oriented distributed data store

Databricks logo Databricks

Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?
  • Apache Druid Landing page
    Landing page //
    2023-10-07
  • Databricks Landing page
    Landing page //
    2023-09-14

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.

Databricks features and specs

  • Unified Data Analytics Platform
    Databricks integrates various data processing and analytics tools, offering a unified environment for data engineering, machine learning, and business analytics. This integration can streamline workflows and reduce the complexity of data management.
  • Scalability
    Databricks leverages Apache Spark and other scalable technologies to handle large datasets and high computational workloads efficiently. This makes it suitable for enterprises with significant data processing needs.
  • Collaborative Environment
    The platform offers collaborative notebooks that allow data scientists, engineers, and analysts to work together in real-time. This enhances productivity and fosters better communication within teams.
  • Performance Optimization
    Databricks includes various performance optimization features such as caching, indexing, and query optimization, which can significantly speed up data processing tasks.
  • Support for Various Data Formats
    The platform supports a wide range of data formats and sources, including structured, semi-structured, and unstructured data, making it versatile and adaptable to different use cases.
  • Integration with Cloud Providers
    Databricks is designed to work seamlessly with major cloud providers like AWS, Azure, and Google Cloud, allowing users to easily integrate it into their existing cloud infrastructure.

Possible disadvantages of Databricks

  • Cost
    Databricks can be expensive, especially for large-scale deployments or high-frequency usage. It may not be the most cost-effective solution for smaller organizations or projects with limited budgets.
  • Complexity
    While powerful, Databricks can be complex to set up and manage, requiring specialized knowledge in Apache Spark and cloud infrastructure. This might lead to a steeper learning curve for new users.
  • Dependency on Cloud Providers
    Being heavily integrated with cloud providers, Databricks might face issues like vendor lock-in, where switching providers becomes difficult or costly.
  • Limited Offline Capabilities
    Databricks is primarily designed for cloud environments, which means offline or on-premise capabilities are limited, posing challenges for organizations with strict data governance policies.
  • Resource Management
    Efficiently managing and allocating resources can be challenging in Databricks, especially in large multi-user environments. Mismanagement of resources could lead to increased costs and reduced performance.

Apache Druid videos

+ Add

An introduction to Apache Druid

More videos:

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

Databricks videos

+ Add

Introduction to Databricks

More videos:

  • Tutorial - Azure Databricks Tutorial | Data transformations at scale
  • Review - Databricks - Data Movement and Query

Category Popularity

0-100% (relative to Apache Druid and Databricks)

Apache Druid

Databricks

Databases
100 100%
Databases
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
Big Data
29 29%
Big Data
71% 71
Big Data Analytics
0 0%
Big Data Analytics
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 Apache Druid and Databricks

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

Databricks Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Databricks notebooks are a popular tool for developing code and presenting findings in data science and machine learning. Databricks Notebooks support real-time multilingual coauthoring, automatic versioning, and built-in data visualizations.
Source: lakefs.io
7 best Colab alternatives in 2023
Databricks is a platform built around Apache Spark, an open-source, distributed computing system. The Databricks Community Edition offers a collaborative workspace where users can create Jupyter notebooks. Although it doesn't offer free GPU resources, it's an excellent tool for distributed data processing and big data analytics.
Source: deepnote.com
Top 5 Cloud Data Warehouses in 2023
Jan 11, 2023 The 5 best cloud data warehouse solutions in 2023Google BigQuerySource: https://cloud.google.com/bigqueryBest for:Top features:Pros:Cons:Pricing:SnowflakeBest for:Top features:Pros:Cons:Pricing:Amazon RedshiftSource: https://aws.amazon.com/redshift/Best for:Top features:Pros:Cons:Pricing:FireboltSource: https://www.firebolt.io/Best for:Top...
Source: www.shipyardapp.com
Top 10 AWS ETL Tools and How to Choose the Best One | Visual Flow
Databricks is a simple, fast, and collaborative analytics platform based on Apache Spark with ETL capabilities. It accelerates innovation by bringing together data science and data science businesses. It is a fully managed open-source version of Apache Spark analytics with optimized connectors to storage platforms for the fastest data access.
Source: visual-flow.com
Top Big Data Tools For 2021
Now Azure Databricks achieves 50 times better performance thanks to a highly optimized version of Spark. Databricks also enables real-time co-authoring and automates versioning. Besides, it features runtimes optimized for machine learning that include many popular libraries, such as PyTorch, TensorFlow, Keras, etc.
Source: blog.bismart.com

Social recommendations and mentions

Based on our record, Databricks should be more popular than Apache Druid. It has been mentiond 18 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.

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 / 5 months 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 / over 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 / almost 3 years ago
View more

Databricks mentions (18)

  • Platform Engineering Abstraction: How to Scale IaC for Enterprise
    Vendors like Confluent, Snowflake, Databricks, and dbt are improving the developer experience with more automation and integrations, but they often operate independently. This fragmentation makes standardizing multi-directional integrations across identity and access management, data governance, security, and cost control even more challenging. Developing a standardized, secure, and scalable solution for... - Source: dev.to / about 1 year ago
  • dolly-v2-12b
    Dolly-v2-12bis a 12 billion parameter causal language model created by Databricks that is derived from EleutherAI’s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA). Source: over 2 years ago
  • Clickstream data analysis with Databricks and Redpanda
    Global organizations need a way to process the massive amounts of data they produce for real-time decision making. They often utilize event-streaming tools like Redpanda with stream-processing tools like Databricks for this purpose. - Source: dev.to / about 3 years ago
  • DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
    Databricks, a data lakehouse company founded by the creators of Apache Spark, published a blog post claiming that it set a new data warehousing performance record in 100 TB TPC-DS benchmark. It was also mentioned that Databricks was 2.7x faster and 12x better in terms of price performance compared to Snowflake. - Source: dev.to / over 3 years ago
  • A Quick Start to Databricks on AWS
    Go to Databricks and click the Try Databricks button. Fill in the form and Select AWS as your desired platform afterward. - Source: dev.to / over 3 years ago
View more

What are some alternatives?

When comparing Apache Druid and Databricks, you can also consider the following products

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

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

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

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

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