Apache Cassandra vs. Apache Druid

Mar 06, 2019
Rachel Pedreschi

I am a field engineer and evangelist for Imply (the company behind Druid), and I was a field engineer and evangelist for Datastax (the company behind Cassandra).As a result, I’ve seen things.A lot of them are cool: designing elegant, high-performance, scalable data architectures that work flawlessly in production.Some of them are not so pretty:selecting wrong systems for critical use cases and spending countless hours to make them fit.

If you are reading this because you are considering whether to use Apache Cassandra/DSE/ScyllaDB or Apache Druid/Imply, then you can just stop right now.You are already headed towards not pretty.If you, like many people I talk to, are evaluating Cassandra (or HBase), but starting to think that Druid is a better fit, then let me save you from the black hole of white papers known as db-engines.

Everything you think you know about Cassandra and Druid is probably wrong.The two databases fundamentally do not address the same set of use cases.

Cassandra and Druid are both fast and highly scalable.They can both deal with time-related data.They also both have cool mascots, but that’s where the similarity ends.The major differences between Cassandra and Druid is inextricably tied to the query patterns dictated by your use case.

Query Patterns are Key

It’s critical to understand anticipated query patterns when selecting any database for your applications. Unlike the olden days (the early 2000s) RDBMSs are no longer the only answer to the “which database should I use?” question.

The simplest question to first ask yourself is, do you have an OLTP or OLAP use case? Cassandra works great for OLTP use cases and Druid works great for OLAP ones.If you already know your use case, then great, you know what system to use.Not sure? More details await!

If your queries ALWAYS constrain on a single column in the WHERE clause, for example on a field such as deviceID or customerID, and you are looking to quickly (sub-second response time) scoop up any and all data related to that ID field reliably, and you are doing nothing else, then Cassandra is your mythological creature of choice.Seriously.You are welcome to argue with me, I don’t mind.I welcome distributed database discourse.

If your use case is such that you honestly have no idea what your WHERE clause will look like, but you know that multiple ID columns will probably need to be queried reliably in less than a few seconds, then Druid is your best bet.Queries matter, people! Know thy query, know thy database.I’m sure someone famous said that.

Understanding what queries you trying to optimize for is the foundation for our discussion.Of course, there’s a lot more details to cover, which can be broken down to two major topics: architecture and data.

Druid and Cassandra Architectures Compared

Druid and Cassandra are both distributed database systems that were designed from inception to withstand outages, and are therefore well-suited for modern infrastructure and application requirements.Both systems allow you to flexibly configure data replication, and distribute data in tiers to take advantage of faster, more expensive hardware.Both are transparently sharded, so you don’t really need to care what data lives on which machine, and both can be easily scaled up and down, with no downtime, to meet rising capacity demands.

Of course, there more than one way to skin a distributed database, and many of the implementation details between the two systems are very different.I will touch on some highlights, but this is by no means an exhaustive list.First, Druid relies on Apache Zookeeper for coordination, while Cassandra has its own system based on the gossip protocol.Zookeeper was much more battle tested by the time Druid was conceived (2011), and the developers felt that since most of implementations of Druid at the time were being used with Apache Hadoop, people would already have Zookeeper in their stack.On the other hand, Cassandra rarely sits in the same space as Hadoop, so reliance on anything from that stack was quickly removed for simplicity.Also, Cassandra has built-in multi datacenter active/active replication that allows for always-on applications with very little outside management required.In contrast, Druid doesn’t rely on replication to guarantee availability within a DC, and instead always maintains a copy of all data segments from all time in deep storage (a distributed file system).They are not used in the read path, and are available to bring a cluster back to operation from near and/or complete death scenarios.You can totally do multi-datacenter with Druid, but it is up to you to manage and maintain it.

Druid was designed in the post-cloud-eating-the-world era, and therefore has the ability to separate processes from each other to be run on the most appropriate hardware.For example, master servers do not require the same CPU, memory, or storage horsepower that data servers do.With Cassandra, you run all processes on a single machine using a single hardware profile.You need to be a lot more careful when you choose your hardware for Cassandra, and this can end up being pretty costly in clouds like AWS.Of course, this is totally ironic since Cassandra gets its inspiration from the Dynamo white paper.Alternatively, Druid gets its inspiration from search systems, timeseries DBs, and traditional analytics DBs.

Druid and Cassandra Data Structures Compared

In terms of how data is stored, the 2 systems are much more different than they are alike, which sheds some light on why query patterns are the bottom line in choosing between the two.They are both well suited to time-based data, and Druid has many special optimizations if a timestamp is in the data.They are also both fairly flexible in terms of schema.Neither supports JOINs or FOREIGN KEY constraints.>They both support evolving schemas and nested data sets, so they are much more forgiving than the relational systems of old.Most importantly, both systems share the requirement that to get the most out of the system, you should understand what your query patterns are going to look like BEFORE designing your schema.This is the price you pay for flexibility and speed over RDBMS, and most modern application design philosophies support this newer way of thinking.

I won’t go into exhaustive detail about how data is laid out on disk for each database, but in summary, Cassandra is fundamentally a key-value store and distributes data around the cluster by a PARTITION KEY, then sorts the data on that partition (or row) by the CLUSTERING key.Adding new data to that row is almost free, and updates are handled by marking the previous cell value with a tombstone and adding the new value to the row.Eventually, you will need to compact these partitions as data becomes fragmented over multiple files, but remember that you are amortizing your INSERTs and UPDATEs over time with almost instantaneous commits.This makes scanning a single partition or row very fast as the disk head only performs a single seek operation.However, if you want more than a single Cassandra partition, performance goes south fairly quickly as scatter/gather queries are an anti-pattern, and secondary indexes are only useful in extremely rare and specific occasions.Therefore, when you know what partition you want to scan, and you don’t want to do any aggregations, GROUPBYs, or any other more analytical operations, then you are in good shape.The result is that Cassandra is great for small, tightly constrained, well-known queries and high-volume inserts and updates.

In contrast, Druid is fundamentally a column store, and is designed for analytical queries (GROUPBYs with complex WHERE clauses) that need to scan across multiple partitions.Druid stores its index in segment files, which are partitioned by time.Segment files are columnar, with the data for each column laid out in separate data structures.By storing each column separately, Druid can decrease query latency by scanning only those columns that are required for a query.There are different column types for different data types (string, numbers, etc.).Different columns can have different encoding and compression algorithms applied.For example, string columns will be dictionary encoded, LZF compressed, and have search indexes created for faster filtering.Numeric columns will have completely different compression and encoding algorithms applied.Druid segments are immutable once finalized, so updates in Druid have limitations.Although more recent versions of Druid have added “lookups”, or the ability to join a mutable table external to Druid with an immutable one in Druid, I would not recommend Druid for any workflows where the same underlying data is frequently updated and those updates need to complete in less than a second (say, powering a social media profile page).Druid supports bulk updates, which are more commonly seen with analytic workloads.

TK Conclusion Header

If you have made it this far, congratulations! Hopefully, by now you understand the way I laid out my argument and my statement that the choice between Cassandra and Druid is all about the use case and how it relates to the way queries run.Cassandra is best for use cases that are write heavy with small, highly constrained queries (OLTP).Druid is best for use cases that are read heavy, and require full analytical query capacity (OLAP).Both are great systems and can be awesome tools when applied correctly, but choose wisely, gentle reader, for the consequences of building your application on the wrong one could bring your business to a grinding halt.No system is a unicorn that can do everything you need.The key is to understand your query patterns and your workload.

A great way to get hands-on with Druid is through a Free Imply Download or Imply Cloud Trial.

Photo taken on my iPhone by me @ Candytopia SF

Other blogs you might find interesting

No records found...
Sep 21, 2023

Migrate Analytics Data from MongoDB to Apache Druid

This blog presents a concise guide on migrating data from MongoDB to Druid. It includes Python scripts to extract data from MongoDB, save it as CSV, and then ingest it into Druid. It also touches on maintaining...

Learn More
Sep 21, 2023

How Druid Facilitates Real-Time Analytics for Mass Transit

Mass transit plays a key role in reimagining life in a warmer, more densely populated world. Learn how Apache Druid helps power data and analytics for mass transit.

Learn More
Sep 19, 2023

Migrate Analytics Data from Snowflake to Apache Druid

This blog outlines the steps needed to migrate data from Snowflake to Apache Druid, a platform designed for high-performance analytical queries. The article covers the migration process, including Python scripts...

Learn More
Sep 15, 2023

Apache Kafka, Flink, and Druid: Open Source Essentials for Real-Time Applications

Apache Kafka, Flink, and Druid, when used together, create a real-time data architecture that eliminates all these wait states. In this blog post, we’ll explore how the combination of these tools enables...

Learn More
Sep 11, 2023

Visualizing Data in Apache Druid with the Plotly Python Library

In today's data-driven world, making sense of vast datasets can be a daunting task. Visualizing this data can transform complicated patterns into actionable insights. This blog delves into the utilization of...

Learn More
Sep 05, 2023

Bringing Real-Time Data to Solar Power with Apache Druid

In a rapidly warming world, solar power is critical for decarbonization. Learn how Apache Druid empowers a solar equipment manufacturer to provide real-time data to users, from utility plant operators to homeowners

Learn More
Sep 05, 2023

When to Build (Versus Buy) an Observability Application

Observability is the key to software reliability. Here’s how to decide whether to build or buy your own solution—and why Apache Druid is a popular database for real-time observability

Learn More
Aug 29, 2023

How Innowatts Simplifies Utility Management with Apache Druid

Data is a key driver of progress and innovation in all aspects of our society and economy. By bringing digital data to physical hardware, the Internet of Things (IoT) bridges the gap between the online and...

Learn More
Aug 14, 2023

Three Ways to Use Apache Druid for Machine Learning Workflows

An excellent addition to any machine learning environment, Apache Druid® can facilitate analytics, streamline monitoring, and add real-time data to operations and training

Learn More
Aug 11, 2023

Introducing Apache Druid 27.0.0

Apache Druid® is an open-source distributed database designed for real-time analytics at scale. Apache Druid 27.0 contains over 350 commits & 46 contributors. This release's focus is on stability and scaling...

Learn More
Aug 10, 2023

Unleashing Real-Time Analytics in APJ: Introducing Imply Polaris on AWS AP-South-1

Imply, the company founded by the original creators of Apache Druid, has exciting news for developers in India seeking to build real-time analytics applications. Introducing Imply Polaris, a powerful database-as-a-Service...

Learn More
Aug 03, 2023

Embedding Visualizations using React and Express

In this guide, we will walk you through creating a very simple web app that shows a different embedded chart for each user selected from a drop-down. While this example is simple it highlights the possibilities...

Learn More
Jul 25, 2023

Apache Druid: Making 1000+ QPS for Analytics Look Easy

This 2-part blog post explores key technical considerations to support high QPS for analytics and the strengths of Apache Druid

Learn More
Jul 25, 2023

Things to Consider When Scaling Analytics for High QPS

This 2-part blog post explores key technical considerations to support high QPS for analytics and the strengths of Apache Druid

Learn More
Jul 20, 2023

Automate Streaming Data Ingestion with Kafka and Druid

In this blog post, we explore the integration of Kafka and Druid for data stream management and analysis, emphasizing automatic topic detection and ingestion. We delve into the creation of 'Ingestion Spec',...

Learn More
Jul 12, 2023

Schema Auto-Discovery with Apache Druid

This guide explores configuring Apache Druid to receive Kafka streaming messages. To demonstrate Druid's game-changing automatic schema discovery. Using a real-world scenario where data changes are handled...

Learn More
Jul 11, 2023

What’s new in Imply Polaris – Q2 2023

Imply Polaris, our ever-evolving Database-as-a-Service, recently focused on global expansion, enhanced security, and improved data handling and visualization. This fully managed cloud service, based on Apache...

Learn More
Jun 06, 2023

Introducing hands-on developer tutorials for Apache Druid

The objective of this blog is to introduce the new set of interactive tutorials focused on the Druid API fundamentals. These tutorials are available as Jupyter Notebooks and can be downloaded as a Docker container.

Learn More
Jun 01, 2023

Introducing Schema Auto-Discovery in Apache Druid

In this blog article I’ll unpack schema auto-discovery, a new feature now available in Druid 26.0, that enables Druid to automatically discover data fields and data types and update tables to match changing...

Learn More
May 30, 2023

Exploring Unnest in Druid

Druid now has a new function, Unnest. Unnest explodes an array into individual elements. This blog contains design methodology and examples for this new Unnest function both from native and SQL binding perspectives.

Learn More
May 28, 2023

What’s new in Imply Polaris – Our Real-Time Analytics DBaaS

Every week we add new features and capabilities to Imply Polaris. This month, we’ve expanded security capabilities, added new query functionality, and made it easier to monitor your service with your preferred...

Learn More
May 24, 2023

Introducing Apache Druid 26.0

Apache Druid® 26.0, an open-source distributed database for real-time analytics, has seen significant improvements with 411 new commits, a 40% increase from version 25.0. The expanded contributor base of 60...

Learn More
May 22, 2023

ACID and Apache Druid

ACID and Druid, an interesting dive into some of the Druid capabilities in the light of ACID compliance

Learn More
May 21, 2023

How to Build a Sentiment Analysis Application with ChatGPT and Druid

Leveraging ChatGPT for sentiment analysis, when combined with Apache Druid, offers results from large data volumes. This integration is easily achievable, revealing valuable insights and trends for businesses...

Learn More
May 21, 2023

Snowflake and Apache Druid

In this blog, we will compare Snowflake and Druid. It is important to note that reporting data warehouses and real-time analytics databases are different domains. Choosing the right tool for your specific requirements...

Learn More
May 20, 2023

Learn how to achieve sub-second responses with Apache Druid

Learn how to achieve sub-second responses with Apache Druid. This article is an in-depth look at how Druid resolves queries and describes data modeling techniques that improve performance.

Learn More
May 19, 2023

Apache Druid – Recovering Dropped Segments

Apache Druid uses load rules to manage the ageing of segments from one historical tier to another and finally to purge old segments from the cluster. In this article, we’ll show what happens when you make...

Learn More
May 18, 2023

Real-Time Analytics: Building Blocks and Architecture

This blog identifies the key technical considerations for real-time analytics. It answers what is the right data architecture and why. It spotlights the technologies used at Confluent, Reddit, Target and 1000s...

Learn More
May 17, 2023

Transactions Come and Go, but Events are Forever

For decades, analytics has focused on Transactions. While Transactions are still important, the future of analytics is understanding Events.

Learn More
May 16, 2023

What’s new in Imply Polaris – Our Real-Time Analytics DBaaS

This blog explains some of the new features, functionality and connectivity added to Imply Polaris over the last two months. We've expanded ingestion capabilities, simplified operations and increased reliability...

Learn More
May 15, 2023

Elasticsearch and Druid

This blog will help you understand what Elasticsearch and Druid do well and will help you decide whether you need one or both to reach your goals

Learn More
May 14, 2023

Wow, that was easy – Up and running with Apache Druid

The objective of this blog is to provide a step-by-step guide on setting up Druid locally, including the use of SQL ingestion for importing data and executing analytical queries.

Learn More
May 13, 2023

Top 7 Questions about Kafka and Druid

Read on to learn more about common questions and answers about using Kafka with Druid.

Learn More
May 12, 2023

Tales at Scale Podcast Kicks off with the Apache Druid Origin Story

Tales at Scale cracks open the world of analytics projects and shares stories from developers and engineers who are building analytics applications or working within the real-time data space. One of the key...

Learn More
May 11, 2023

Real-time Analytics Database uses partitioning and pruning to achieve its legendary performance

Apache Druid uses partitioning (splitting data) and pruning (selecting subset of data) to achieve its legendary performance. Learn how to use the CLUSTERED BY clause during ingestion for performance and high...

Learn More
May 10, 2023

Easily embed analytics into your own apps with Imply’s DBaaS

This blog explains how developers can leverage Imply Polaris to embed robust visualization options directly into their own applications without them having to build a UI. This is super important because consuming...

Learn More
May 09, 2023

Building an Event Analytics Pipeline with Confluent Cloud and Imply’s real time DBaaS, Polaris

Learn how to set up a pipeline that generates a simulated clickstream event stream and sends it to Confluent Cloud, processes the raw clickstream data using managed ksqlDB in Confluent Cloud, delivers the processed...

Learn More
May 08, 2023

Real time DBaaS comes to Europe

We are excited to announce the availability of Imply Polaris in Europe, specifically in AWS eu-central-1 region based in Frankfurt. Since its launch in March 2022, Imply Polaris, the fully managed Database-as-a-Service...

Learn More
May 07, 2023

Stream big, think bigger—Analyze streaming data at scale in 2023

Imply is predicting the next "big thing" in 2023 will be analyzing streaming data in real time (and Druid is built for just that!)

Learn More
May 07, 2023

Should You Build or Buy Security Analytics for SecOps?

When should you build—or buy—a security analytics platform for your environment? Here are some common considerations—and how Apache Druid is the ideal foundation for any in-house security solution.

Learn More
May 05, 2023

Introducing Apache Druid 25.0

Apache Druid 25.0 contains over 293 updates from over 56 contributors.

Learn More
May 03, 2023

Druid and SQL syntax

This is a technical blog, which summarises the process of extending the Druid's SQL grammar for ingestion and delves into the nitty gritty of Calcite.

Learn More
May 02, 2023

Native support for semi-structured data in Apache Druid

Describes a new feature- ingest complex data as is into Druid- massive improvement in developer productivity

Learn More
May 01, 2023

Real-Time Analytics with Imply Polaris: From Setup to Visualization

Imply Polaris offers reduced operational overhead and elastic scaling for efficient real-time analytics that helps you unlock your data's potential.

Learn More
May 01, 2023

Datanami Award

Apache Druid won Datanami's 2022 Readers’ and Editors’ Choice Awards for Reader's Choice "Best Data and AI Product or Technology: Analytics Database".

Learn More
Apr 30, 2023

Alerting and Security Features in Polaris

Describes new features - alerts and some security features- and how Imply customers can leverage it

Learn More
Apr 29, 2023

Ingestion from Amazon Kinesis and S3 into Imply Polaris

Imply Polaris now supports data ingestion from Amazon Kinesis and Amazon S3

Learn More
Apr 27, 2023

Getting the Most Out of your Data

Ingesting data from one table to another is easy and fast in Imply Polaris!

Learn More
Apr 26, 2023

Combating financial fraud and money laundering at scale with Apache Druid

Learn how Apache Druid enables financial services firms and FinTech companies to get immediate insights from petabytes-plus data volumes for anti-fraud and anti-money laundering compliance.

Learn More
Apr 26, 2023

What’s new in Imply – December 2022

This is a what's new to Imply in Dec 2022. We’ve added two new features to Imply Polaris to make it easier for your end users to take advantage of real-time insights.

Learn More
Apr 25, 2023

What’s New in Imply Polaris – November 2022

This blog provides an overview for the new features, functionality, and connectivity to Imply Polaris for November 2022.

Learn More
Apr 24, 2023

Imply Pivot delivers the final mile for modern analytics applications

This blog is focused on how Imply Pivot delivers the final mile for building an anlaytics app. It showcases two customer examples - Twitch and ironsource.

Learn More
Apr 23, 2023

Why Analytics Need More than a Data Warehouse

For decades, analytics has been defined by the standard reporting and BI workflow, supported by the data warehouse. Now, 1000s of companies are realizing an expansion of analytics beyond reporting, which requires...

Learn More
Apr 21, 2023

Why Open Source Matters for Databases

Apache Druid is at the heart of Imply. We’re an open source business, and that’s why we’re committed to making Druid the best open source database for modern analytics applications

Learn More
Apr 20, 2023

Ingestion from Confluent Cloud and Kafka in Polaris

How to ingest data into Imply Polaris from Confluent Cloud and from Apache Kafka

Learn More
Apr 18, 2023

What Makes a Database Built for Streaming Data?

For an analytics app to handle real-time, streaming sources, it must be built for streaming data. Druid has 3 essential features for stream data.

Learn More
Oct 12, 2022

SQL-based Transformations and JSON Columns in Imply Polaris

You can easily do data transformations and manage JSON data with Imply Polaris, both using SQL.

Learn More
Oct 06, 2022

Approximate Distinct Counts in Imply Polaris

When it comes to modern data analytics applications, speed is of the utmost importance. In this blog we discuss two approximation algorithms which can be used to greatly enhance speed with only a slight reduction...

Learn More
Sep 20, 2022

The next chapter for Imply Polaris: celebrating 250+ accounts, continued innovation

Today we announced the next iteration of Imply Polaris, the fully managed Database-as-a-Service that helps you build modern analytics applications faster, cheaper, and with less effort. Since its launch in...

Learn More
Sep 20, 2022

Introducing Imply’s Total Value Guarantee for Apache Druid

Apache Druid 24.0 contains 450 updates and new features, major performance enhancements, bug fixes, and major documentation improvements

Learn More
Sep 16, 2022

Introducing Apache Druid 24.0

Apache Druid 24.0 contains 450 updates and new features, major performance enhancements, bug fixes, and major documentation improvements

Learn More
Aug 16, 2022

Using Imply Pivot with Druid to Deduplicate Timeseries Data

Imply Pivot offers multi step aggregations, which is valuable for timeseries data where measures are not evenly distributed in time.

Learn More
Jul 21, 2022

A Look Under the Surface at Polaris Security

We have taken a security-first approach in building the easiest real-time database for modern analytics applications.

Learn More
Jul 14, 2022

Upserts and Data Deduplication with Druid

A look at what can be done with Druid for upserts and data deduplication.

Learn More
Jul 01, 2022

What Developers Can Build with Apache Druid

We obviously talk a lot about #ApacheDruid on here. But what are folks actually building with Druid? What is a modern analytics application, exactly? Let's find out

Learn More
Jun 29, 2022

When Streaming Analytics… Isn’t

Nearly all databases are designed for batch processing, which leaves three options for stream analytics.

Learn More
Jun 29, 2022

Apache Druid vs. Snowflake

Elasticity is important, but beware the database that can only save you money when your application is not in use. The best solution will have excellent price-performance under all conditions.

Learn More
Jun 22, 2022

Druid 0.23 – Features And Capabilities For Advanced Scenarios

Many of Druid’s improvements focus on building a solid foundation, including making the system more stable, easier to use, faster to scale, and better integrated with the rest of the data ecosystem. But for...

Learn More
Jun 22, 2022

Introducing Apache Druid 0.23

Apache Druid 0.23.0 contains over 450 updates, including new features, major performance enhancements, bug fixes, and major documentation improvements.

Learn More
Jun 20, 2022

An Opinionated Guide to Component APIs

We have collected a number of guidelines for React component APIs that make components more predictable in terms of behavior and performance.

Learn More
Jun 10, 2022

Druid Architecture & Concepts

In a world full of databases, learn how Apache Druid makes real-time analytics apps a reality in this Whitepaper from Imply

Learn More
May 25, 2022

3 decisions that shaped the Polaris UI

Imply Polaris is a fully managed database-as-a-service for building realtime analytics applications. John is the tech lead for the Polaris UI, known internally as the Unified App. It began with a profound question:...

Learn More
May 19, 2022

How Imply Polaris takes a security-first approach

A primer for developers on security tools and controls available in Imply Polaris

Learn More
May 17, 2022

Imply Raises $100MM in Series D funding

There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...

Learn More
May 11, 2022

Imply Named “Cool Database Vendor” by CRN

There can’t be one database good at everything. When it comes to real-time analytics, you need a database built for it.

Learn More
May 11, 2022

Living the Stream

We are in the early stages of a stream revolution, as developers build modern transactional and analytic applications that use real-time data continuously delivered.

Learn More
May 02, 2022

Migrating Data from ClickHouse to Imply Polaris

In this blog, we’ll review the simple steps to export data from ClickHouse in a format that is easy to ingest into Polaris.

Learn More
Apr 18, 2022

Apache Druid vs. ClickHouse

Developers and architects must look beyond query performance to understand the operational realities of growing and managing a high performance database and if it will consume their valuable time.

Learn More
Apr 06, 2022

Java Keytool, TLS, and Zookeeper Security

Lean the basics of Public Key Infrastructure (PKI) as it relates to Druid and Zookeeper security.

Learn More
Apr 01, 2022

Building high performance logging analytics with Polaris and Logstash

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

Learn More
Apr 01, 2022

For April 1st: a New Description of Apache Druid from Our Youngest Technical Architect

A simple set of instructions to deploy Apache Druid on minikube using minio for local deep storage on your laptop.

Learn More
Mar 24, 2022

Distributed by Nature: Druid at Scale

Horizontal scaling is the key to performance at scale, which is why every database claims this. You should investigate, though, to see how much effort it takes, especially compared to Apache Druid.

Learn More
Mar 23, 2022

Atomic Replace in Polaris

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

Learn More
Mar 22, 2022

Announcing Imply Polaris

Today, we're excited to announce a major leap forward in ease-of-use with the introduction of Imply Polaris, our fully-managed, database-as-a-service.

Learn More
Mar 22, 2022

Building Analytics for External Users is a Whole Different Animal

Analytics aren’t just for internal stakeholders anymore. If you’re building an analytics application for customers, then you’re probably wondering…what’s the right database backend? 

Learn More
Mar 07, 2022

Clustered Apache Druid® on your Laptop – Easy!

A simple set of instructions to deploy Apache Druid on minikube using minio for local deep storage on your laptop.

Learn More
Mar 01, 2022

Why Data Needs More than CRUD

After over 30 years of working with data analytics, we’ve been witness (and sometimes participant) to three major shifts in how we find insights from data - and now we’re looking at the fourth.

Learn More
Mar 01, 2022

The Rise of a New Analytics Hero in 2022

Every year industry pundits predict data and analytics becoming more valuable the following year. But this doesn’t take a crystal ball to predict. There’s instead something much more interesting happening...

Learn More
Mar 01, 2022

A new shape for Apache Druid

Today, I'm prepared to share our progress on this effort and some of our plans for the future. But before diving further into that, let's take a closer look at how Druid's core query engine executes queries,...

Learn More
Feb 11, 2022

Product Update: SSO, Cluster level authorization, OAuth 2.0 and more security features

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

Learn More
Feb 04, 2022

Multi-dimensional range partitioning

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

Learn More
Dec 12, 2021

Log4Shell Vulnerability and Mitigation

A critical vulnerability has recently been discovered in Apache Log4j, a popular logging library for Java projects.

Learn More
Nov 22, 2021

Druid Nails Cost Efficiency Challenge Against ClickHouse & Rockset

To make a long story short, we were pleased to confirm that Druid is 2 times faster than ClickHouse and 8 times faster than Rockset with fewer hardware resources!.

Learn More
Nov 09, 2021

Unveiling Project Shapeshift Nov. 9th at Druid Summit 2021

There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...

Learn More
Oct 25, 2021

How we made long-running queries work in Apache Druid

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

Learn More
Oct 20, 2021

Auto Scaling real-time Kafka Ingestion FTW!

Uneven traffic flow in streaming pipelines is a common problem. Providing the right level of resources to keep up with spikes in demand is a requirement in order to deliver timely analytics.

Learn More
Oct 04, 2021

Community Discoveries: multi-value dimensions in Apache Druid

Hellmar Becker is an Imply solutions engineer based in Germany, where he has been delving into the nooks-and-crannies of multi-valued dimension support in Druid. In this interview, Hellmar explains why...

Learn More
Sep 28, 2021

Community Spotlight: Apache Pulsar and Apache Druid get close…

The community team at Imply spoke with an Apache Pulsar community member, Giannis Polyzos, about how collaboration between open source communities generates great things, and more specifically, about how...

Learn More
Sep 27, 2021

Meet the team: Abhishek Agarwal, engineering lead in India

Abhishek is Imply’s first engineer in India. We spoke to him about setting up our operations in Bangalore and asked what kind of local talent the company is looking for.

Learn More

Let us help with your analytics apps

Request a Demo