Upserts and Data Deduplication with Druid

Jul 14, 2022
Jad Naous

Our customers often come to us with use cases that require upserts or data deduplication. After digging into the use cases and the problems the customer is attempting to resolve, we find better approaches that are well-supported by Druid. Sometimes, we find that the use case really does require these capabilities, and the use of an external function is required. That said, in this blog, I’d like to tackle what can be done with Druid for upserts and data deduplication.

“Upsert” means “update if found, insert otherwise”. On the other hand, “deduplication” is the process of ensuring that only a single row exists for a particular key. These two functions are similar, in that they both rely on a key to identify a row. If users are generally upserting data into a table, that would also ensure deduplicated data. The main idea being that upserting means the user wants the latest version of the data whereas deduplication means the user wants any version of the data since they are all duplicates. So from here on, this blog will only be concerned with upserts, and there are several options for doing them with Druid, depending on the use case.

1. Upserts Using Batch Ingestion

One possibility is to rewrite Druid time chunks (ie Druid time partition) with the latest data for that time chunk. This is relatively expensive at upsert time since you would need to reindex all the data in that time chunk, even if a single row changes. It also means that you would need to have the updated version of all the data in that time chunk handy for reingestion in a single job, which is often not the case. That said, this does provide the best query performance since the data will be already in its optimal form for querying.

Time partitions with one time partition being completely replaced by another

This is useful in situations where the upsert volume is not high (meaning the data is not changing often), and it’s not that important to make the upsert visible quickly. A useful strategy for a production deployment would be to batch upserts until there’s a sufficient number of them to be worth an ingestion job. Most use cases involving this solution would be situations where Druid is used as a materialized view over data that resides in another system such as in S3 or in a data warehouse and kept up to date. Then the latest data for any particular time chunk is always readily available for reloading into Druid.

2. Upserts Using Query-time Aggregation

Sometimes users just want to read the latest value of some field y for some key field x. If you append all updates (instead of upserting them) during ingestion, then you can do a query like SELECT x, LATEST_BY(y, update_timestamp) from tbl GROUP BY x to get the latest values. Note that you’ll need an update timestamp to be added so Druid can track the latest version. However, if you use this as a subquery and do further processing on top of it (for example SELECT w, SUM(latest_y) from (SELECT w, x, LATEST_BY(y, update_timestamp) as latest_y GROUP BY x) GROUP by w), and there are a lot of x in the subquery, this query could be expensive – though enhancements to Druid that make this sort of query faster are on the way (see this blog article).  This pattern is useful in situations where there aren’t that many distinct x or where the performance requirement for these kinds of queries is not very demanding.

The table below is an example, where x is user_id and y is points. To get the latest points at each hour for each user_id, execute SELECT FLOOR(__time to HOUR) as hour_time, user_id, LATEST_BY(update_timestamp, points) from tbl GROUP BY 1, 2.

__timeupdate_timestampuser_idpoints
1:00pm1:00pmfunny_bunny110
1:00pm1:05pmfunny_bunny120
2:00pm2:00pmfunny_bunny15
2:00pm2:00pmcreepy_monkey130
2:00pm2:05pmcreepy_monkey125
3:00pm3:00pmfunny_bunny110

The result of that SELECT statement looks like:

hour_timeuser_idpoints
1:00pmfunny_bunny120
2:00pmfunny_bunny15
2:00pmcreepy_monkey125
3:00pmfunny_bunny110

A mitigation for the cost of these kinds of queries is to set up a periodic batch ingestion job that reindexes modified data into a new datasource for direct querying without grouping. Another mitigation is to do ingestion-time aggregation, and to use that LATEST_BY aggregation at ingestion time, appending the upserts through streaming ingestion into a rolled up datasource. Because appending into a time chunk adds new segments and does not perfectly roll up data, so rows may be partial rather than complete rollups, and you may have multiple of these partially rolled up rows, you would still need to use the GROUP BY query for correct querying of that data source. But if you set up autocompaction right, you will be able to significantly reduce the number of stale rows and improve your performance.

3. Upserts Using Deltas

This is similar to the previous method, but rather than appending the new value and then using LATEST as the aggregator, you would use the aggregator you usually use. This allows you to possibly avoid a level of aggregation and grouping in your queries since you would issue the same queries as usual. The example is a datasource with a measure column y that’s usually aggregated by SUM. Say that the old value of y for some x was 3 and you want it to be 2. Then you’d insert a y=-1 for that x, and that would allow you to have the correct value of y for any queries that sum y grouped by x. This could offer a significant performance advantage but may be less flexible since now the aggregation has to always be a SUM. In other cases, the updates to the data may already be deltas to the original, and so the data engineering required to append the updates would be simple. Another simplification here is that the update timestamp is no longer needed since all the data is used. Just as before, the same mitigations as the previous case apply to improve performance with autocompaction and rollup at ingestion time.

Below is an example table that would provide the same results as before with a different query: SELECT FLOOR(__time to HOUR) as hour_time, user_id, SUM(points) from tbl GROUP BY 1, 2.

__timeuser_idpoints
1:00pmfunny_bunny110
1:00pmfunny_bunny110
2:00pmfunny_bunny15
2:00pmcreepy_monkey130
2:00pmcreepy_monkey1-5
3:00pmfunny_bunny110

A Note on Deduplication

In the last two upsert processes described above, we alway needed some way to identify a time of update. For deduplication, the time of update is not necessary since all versions of a row are the same. We also would likely not want to use the LATEST aggregator for grouping, since ANY is good enough.
Do you have a use case for upserts or deduplication? Do one of these three models work for you, or are you doing something else? Please visit the Druid Forum and let us know!

Other blogs you might find interesting

No records found...
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 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 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 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
Apr 27, 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
Apr 27, 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
Apr 27, 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
Apr 27, 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
Apr 27, 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
Apr 27, 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
Apr 27, 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
Apr 27, 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
Apr 27, 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
Apr 27, 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
Apr 27, 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
Apr 26, 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
Apr 26, 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
Apr 26, 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
Apr 26, 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
Apr 26, 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
Apr 26, 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
Apr 26, 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
Apr 26, 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
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 26, 2023

Introducing Apache Druid 25.0

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

Learn More
Apr 26, 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
Apr 26, 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
Apr 26, 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 24, 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 24, 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 24, 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 24, 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 24, 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 24, 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 24, 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 24, 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 24, 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
Jan 31, 2023

Tales at Scale Podcast: Who Really Needs Real-Time Data?

Gwen Shapira, co-founder and CPO of Nile joins us to help define real-time data, discuss who needs it (and who probably doesn't) and how to not build yourself into a corner with your architecture. When you're...

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 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
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
Sep 06, 2021

Community Spotlight: Sparking that connection with Apache Druid

It’s been nearly 10 years now since Druid was open sourced “to help other organizations solve their real-time data analysis and processing needs”. This has happened not because of one person or one...

Learn More
Aug 18, 2021

Community Spotlight: Augmented analytics on business metrics by Cuebook with Apache Druid®

Cuebook is putting you, decision-maker, back in the driving seat, powered by Apache Druid®. In this interview with their founder and CEO, we learn their reason for being, their open source Cuelake tooling,...

Learn More
Jul 28, 2021

The Open Source Modern Analytics Stack

Empowering all types of users to analyze data incredibly quickly from wherever it sits provides huge value to organizations. Citizen data scientists and decision scientists are able to make empirically-backed,...

Learn More
Jun 16, 2021

Imply Raises $70MM in Series C funding

Our vision at Imply has always been to create a new category for data analytics, analytics-in-motion, and enable organizations to unlock workflows they’ve never been able to do before. With the most recent...

Learn More
May 25, 2021

Community Spotlight: Avesta powers next-generation applications with Apache Druid

When considering various real-time analytics solutions, Apache Druid quickly became the clear choice: Avesta uses only open-source products and libraries. And today, they’re using Druid as a central component...

Learn More
May 25, 2021

The Future of Analytics

The traditional BI workflow starts with a strategic question. Such a question is not too time-sensitive—days or weeks is okay—and the question is pretty complex to answer.

Learn More
May 18, 2021

How we enabled the “Go Fast” button on TopN queries: Hint: we used vectorized virtual columns (which is new in Apache Druid 0.20.0)

Apache Druid is a fast, modern analytics database designed for workflows where fast, ad-hoc analytics, instant data visibility, or supporting high concurrency is important. Multiple factors contribute to...

Learn More
May 17, 2021

How Sift is accurately identifying anomalies in real time by using Imply Druid

As the leader in Digital Trust & Safety and a pioneer in using machine learning to fight fraud, Sift regularly deploys new machine learning models into production. Sift’s customers use the scores generated...

Learn More
May 14, 2021

Making the impossible, possible: A GameAnalytics case study

We’ve had the pleasure of speaking with Ioana Hreninciuc, CEO of GameAnalytics, to learn just how they use Imply to make their next-generation data stack possible.

Learn More
May 11, 2021

Make your real-time dashboards blazing fast with per-segment caching

Imagine a scenario where Druid is collecting metrics about a huge microservices application —there’s a continuous stream of metrics coming in about the different services from this application.

Learn More
May 11, 2021

Community Spotlight: smart advertising from Sage+Archer + Apache Druid

Out-of-home advertising has changed. Gone are static, uncompromisingly homogenous posters, replaced instead with bright and fluid installations. Installations that make smart decisions about what and when...

Learn More
May 07, 2021

Data deletion in Apache Druid (part 2)

Some time ago, Dana Assa and I wrote a detailed blog post about Data retention and deletion in Apache Druid. Our intention was to help Druid database users and provide guidance on how to control the TTL...

Learn More
Apr 30, 2021

Data Revolution at Hawk powered by Imply

Hawk is the first independent European platform to offer a transparent and technological advertising experience across all screens: Desktop, Mobile, CTV, DOOH & Digital Audio.

Learn More
Apr 29, 2021

If you thought you had perfect rollups before, you might have been wrong!

In Apache Druid, you can roll up duplicate rows into a single row to optimize storage and improve query performance. Rollup pre-aggregates data at ingestion time, which reduces the amount of data the query...

Learn More
Apr 27, 2021

Imply’s real-time analytics maturity model to create better customer experiences

Imply’s real-time Druid database today powers the analytics needs of over 100 customers across industries such as Banking, Retail, Manufacturing, and Technology. We have observed that the majority of prospects...

Learn More
Apr 09, 2021

What I wish I knew about Imply when I was developing in-house analytics

Like a lot of engineers at Imply, I got my start here after having worked on an analytics solution for a previous employer. In my case, it was a large non-tech company going through a digital transformation.

Learn More
Mar 31, 2021

Kueez leverages Imply Cloud to reduce operational overhead & enable real-time analytics

Imply allows Kueez's data analysts, content editors, and growth teams to optimize their campaigns in real-time. With open-source Druid, they struggled to keep their system up and running, their queries were...

Learn More

Let us help with your analytics apps

Talk to an expert

Schedule a demo

Need more information about Druid and Imply? Let us set you up with a demo.