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:
The elasticity to make developers and system admins happy. As needs change and the system grows, you adapt quickly.
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.
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...
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.
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...
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...
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...
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
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
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...
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
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...
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...
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...
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',...
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...
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...
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.
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...
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.
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...
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...
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...
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 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.
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...
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...
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...
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.
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...
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...
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...
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...
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...
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.
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.
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.
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.
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...
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
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...
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...
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
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...
Apache Druid 0.23.0 contains over 450 updates, including new features, major performance enhancements, bug fixes, and major documentation improvements.
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:...
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...
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.
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.
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...
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.
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...
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?
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.
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...
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,...
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...
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...
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!.
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...
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...
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.
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...
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...
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.
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.