Apache Druid vs. Snowflake

Jun 29, 2022
Carl Dubler

Which one is best for my analytics application?

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.

Many application architects and developers are under pressure to standardize on a database. For you, perhaps this is Snowflake. It would be reasonable to assume that your analytics applications should also run on it. So why consider a different database, one that is purpose-built for analytics applications? The question answers itself: purpose-built. Snowflake’s architecture is not built for a modern analytics application and in fact works against it being a sustainable choice.

I’m not saying that Snowflake can’t do any analytics. Snowflake is a good choice for traditional business intelligence, such as reports and dashboards. These typically have low interactivity, few concurrent users, and are based on batch data that is refreshed only periodically. If you need high interactivity, concurrency, or real-time data, Snowflake just isn’t designed for this.

What is Snowflake Selling?

In general, analytics applications need two things:

  1. The elasticity to make developers and system admins happy. As needs change and the system grows, you adapt quickly.
  2. The query performance to make end users happy. When they click on something in your app, they get an answer in less than a second. Even better if the data freshness is real-time.

Snowflake’s main value proposition is simple: elasticity. Save money when your application is not in use, scale up to meet a spike in demand, and then scale back down. This requires an architecture that separates storage and compute.

By storing data in a different layer than the computing power that processes queries, you can add or remove compute power when you need it–usually within a few minutes. However, this comes at the cost of query processing, since round-trips to the storage layer for data is very slow.

Fig 1: a 4-node cluster with separated storage and compute (query processing). Data movement between layers causes major query performance degradation, but adding/removing compute nodes is easy.

This is in contrast to Snowflake competitors Amazon Redshift and ClickHouse, who are focused on performance. So, they use a shared-nothing design where storage and compute are located on the same nodes of a cluster. The query performance is much better with this approach, but scaling to meet demand can be a multi-day effort (I detail this in an article about ClickHouse).

Fig 2: a 4-node cluster with storage and compute (query processing) on each node. This eliminates the data movement that slows queries, but adding/removing nodes requires downtime to redistribute data.

With elasticity as their value proposition, it is clear why Snowflake chose separation of storage and compute. But they had to make an unavoidable trade-off with query performance. If you are on Snowflake, it is because you value this elasticity. Application not in use? Shut it down! Sudden spike in demand? Ramp it up!

This pay-as-you-go approach is great for something infrequently used. For example, your weekly dashboard for executives. What if, though, you build an app for external use and now need to support lots of concurrent users, sub-second response on high volumes of data, or real time data? Frankly, don’t you want this to happen? Who wants to build an analytics app that doesn’t get used? Every developer wants to build an application that is so important, it is in use constantly by lots of people. What is essential is to support constant use economically.

With this as the context, let’s compare Snowflake and Druid across 4 areas vital to an important, rapidly growing, and constantly used analytics app: caching, indexing, concurrency, and real-time data. Along the way, I’ll explain how Druid’s architecture addresses both needs: elasticity and performance.

Caching

Snowflake’s architecture works against it when you need rapid query response. Because the computing power that processes queries is physically separated from the data, many round trips must happen, killing efficiency. Snowflake tries to solve this with caching, temporarily storing recently-used data at the compute layer. This will help for queries that are repeated or happen to be using the data already cached. The issue becomes optimizing the cache constantly. If you don’t know what your users will query next, or you are constantly adding data, caching is difficult to optimize.

Druid solves this with a unique architecture that combines the flexibility of separate storage and compute (like Snowflake) with shared-nothing performance (like Redshift and ClickHouse). Instead of limited caching that must be constantly redone and optimized, Druid pre-fetches all data, enabling sub-second response for the first query and anything that comes next. Yet you can add and remove nodes as needed and Druid will automatically rebalance the cluster.

Fig 3: An overview of Druid’s architecture with separate storage and compute (query processing) for flexibility and prefetching for shared-nothing performance.

Indexing

Snowflake does not use secondary indexes. As with any database, you still order your data by a primary index (key), and they are betting that most of what you want can be done by scanning and then filtering by this key. Scans are slow in any case, and if you want high cardinality data (specific records, not ranges of records), it is a lot of wasted effort. Snowflake tries to hide this inefficiency by adding computing power (scaling up) and getting the results by brute force. This might work when only a few users are hammering away at the data, but scaling up will definitely cost you more.

Druid automatically creates indexes that not only reduce scanning, but also ensure high cardinality queries are sub-second or very close to it. Druid data segments are columnar and compressed, making them highly efficient. Data is automatically indexed on data nodes during ingestion, making it essentially “pre-fetched” for queries from deep storage. It’s a combination that beats old-fashioned caching and brute force in price-performance. Most Druid use cases involve massive amounts of read-only data organized by time–perfect for this efficient storage design.

Fig 4: a Druid data segment showing compressed columnar storage with data dictionary and automatic indexing. In this very small example, there are 3 records for LAX, 3 for SEA, and 2 for SFO.

Concurrency

Snowflake claims you can “…support a near-unlimited number of concurrent users and workloads without degrading performance.” So it may come as a surprise to learn that the maximum concurrency of a Snowflake warehouse is only 8. Snowflake offers some advice on how to get the most out of this limit, which basically comes down to carefully monitoring what is going on and taking care not to overwhelm the system. How then can they make this “near- unlimited” claim?

I should show you the entire sentence: “Spin-up dedicated compute resources instantly to support a near-unlimited number of concurrent users and workloads without degrading performance” (emphasis added). I suppose this means that you can have near-unlimited users if you also have a near-unlimited budget to keep spinning up more computing power. Scaling out, not up, is how you solve concurrency, which is why Enterprise Edition customers of Snowflake can add up to 10 clusters per warehouse. But consider how burdensome and costly this may be with concurrent user growth. Again, with pay-as-you-go, you must hope that you don’t need to go anywhere with more than a few users.

Druid’s unique architecture handles high concurrency with ease, and it is not unusual for systems to support hundreds and even thousands of concurrent users (the Druid system at Target, for example, handles over 70,000 daily average users). Quite the opposite of Snowflake, Druid is designed to accommodate constant use at a low price point, with an efficient scatter-gather query engine that is highly distributed: scale out instead of scale up. With Druid, scaling out is always built-in, not a special feature of a more expensive enterprise version and not limited in how far you can grow.

Fig 5: illustration of Druid’s scatter-gather query engine that maximizes scale-out performance and enables high concurrency.

A Druid cluster has 3 major node types, each of them independently scalable, to allow a custom fit if you need it:

  • Data nodes for ingestion and storage
  • Query nodes for processing and distribution
  • Master nodes for cluster health and load balancing 

This gives administrators fine-grained control and enables cost-saving data tiering by putting less important or older data on cheaper systems. Further, there is no limit to how many nodes you can have, with some Druid applications using thousands of nodes.

Fig 6: Druid’s independently-scalable and flexible node types. Data is also automatically replicated to deep storage for automatic backup and recovery from node failures.

Real-Time Data

Snowflake has connectors for streaming data such as Kafka and Kinesis. This isn’t a big deal–everyone has these (except for Druid, as you’ll see in a moment). But connecting to streaming data is not the same thing as being real-time. Snowflake has only one way to load the data: buffered in batches. Queries must wait for data to be batch-loaded and persisted in storage, and further delays happen if you check to make sure there are no duplicates (exactly once ingestion), a difficult proposition when thousands or even millions of events are generated each second.

Druid has native support for both Kafka and Kinesis–you do not need a connector to install and maintain. Druid can query streaming data the moment it arrives at the cluster, even millions of events per second (the Druid system at Netflix tracks 2 million events per second–115 billion each day). There’s no need to wait as it makes its way to storage. Further, because Druid ingests streaming data in an event-by-event manner, it automatically ensures exactly-once ingestion.

Fig 7: Druid can ingest millions of streaming events each second and query them immediately on arrival.

Conclusion

Snowflake’s elasticity makes it a good choice for infrequently used reporting and dashboards. But when developers from Netflix, Twitter, Confluent, Salesforce, and many others needed interactivity at scale and real-time data, they chose Druid. If you are looking for alternatives to Snowflake for such applications, you should definitely put Druid on your list and try it out for free with Imply Polaris, the database as a service built from Druid. There’s no need to replace Snowflake where it makes sense. It is simply a matter of the right tool for the job.

© 2022 Imply. All rights reserved. Imply and the Imply logo, are trademarks of Imply Data, Inc. in the U.S. and/or other countries. Apache Druid, Druid and the Druid logo are either registered trademarks or trademarks of the Apache Software Foundation in the USA and/or other countries. All other marks are the property of their respective owners.

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 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
Sep 27, 2021

Meet the team: Jihoon Son, Software Engineer

Jihoon Son is a software engineer at Imply who works on Apache Druid®. He explains what drew him to Imply five years ago and why he’s even more inspired by the company today.

Learn More

Let us help with your analytics apps

Request a Demo