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

Apr 26, 2023
Hellmar Becker

Introduction

A modern streaming analytics pipeline is built around two central components:

  • an event streaming platform
  • an event analytics platform.

This is conveniently achieved using Confluent Cloud as a SaaS event streaming platform, and Imply Polaris as a SaaS realtime analytics database.

In this blogpost, I am going to show you 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 stream using Confluent Cloud
  • ingests these JSON events, using a native connection, into Imply Polaris
  • and visualizes the event data in a dashboard.

All this, only with a few clicks and some SQL!

But first, let’s cover some of the basics.

The Case for Streaming Analytics

Old School Analytics

This is how we used to do analytics, roughly 20 years ago. You would have your operational systems that collected data into transactional, or OLTP, databases.

OLTP databases are built to process single updates or inserts very quickly. In traditional relational modeling this means you have to normalize your data model, ideally to a point where each item exists only once in a database. The downside is when you want to run an analytical query that aggregates data from different parts of your database, these queries require complex joins and can become very expensive, hurting query times and interfering with the transactional performance of your database.

Hence another type of databases was conceived which is optimized for these analytical queries: OLAP databases. These come in different shapes and flavors, but generally a certain amount of denormalization and possibly preaggregation is applied to the data.

The process that ships data from the transactional system to the OLAP database is called ETL – Extract, Transform, Load. It is a batch process that would run on a regular basis, for instance once a night or once every week. The frequency of the batch process determines how “fresh” your analytical data is.

In the old world, where analytical users would be data analysts inside the enterprise itself, that was often good enough. But nowadays, in the age of connectivity, everyone is an analytics user. If you check your bank account balance and the list of transactions in your banking app on your smartphone, you are an analytics user. And if someone transfers funds to your account, you expect to see the result now and not two days later.

A better way of processing data for analytics was needed. And we’ll look at that now.

Big Data and the Lambda Architecture

About ten years ago, the big data craze came up around the Hadoop ecosystem. Hadoop brought with it the ability to handle historical data to a previously unknown scale, but it also already had real time1 capability, with tooling like Kafka, Flume, and HBase.

The first approach to getting analytics more up to date was the so called lambda architecture, where incoming data would be sent across two parallel paths:

  • A realtime layer with low latency and limited analytical capabilities
  • A highly scalable but slower batch layer.

This way, you would be able to retrieve at least some of the analytics results immediately, and get the full results the next day.

A common serving layer would be the single entry point for clients.

This architectural pattern did the job for a while but it has an intrinsic complexity that is somewhat hard to master. Also, when you have two different sources of results, you need to go through an extra effort to make sure that the results always match up.

Kappa Architecture

A better way needed to be found. It was created in the form of the kappa architecture. In the kappa architecture, there is only one data path and only one result for a given query. The same processing path gives (near) real time results and also fills up the storage for historical data.

The kappa architecture handles incoming streaming data and historical data in a common, uniform way and is more robust than a lambda architecture. Ideally you still want to encapsulate the details of such an architecture and not concern the user with it. We will come to that in a moment.

Implementing the Kappa Architecture: Druid

Apache Druid is a high performance, real-time analytics database purpose-built for powering analytics applications at massive scale and concurrency on streaming and batch data.

Druid encapsulates the kappa architecture so you don’t need to bother about all the implementation detail. Here is a quick, high level overview of the comoponents that make up a Druid instance.

Druid is heavily distributed and exceptionally scalable, and here is how that works.

In Druid, there are three type of servers: master, query, and data servers. Also there is deep storage (typically object storage, such as S3), and a relational database for metadata.

Master servers handle data coordination, metadata processing, and service discovery. They know which bit of data lives where in the Druid cluster, and which processes and compute resources are available.

Query servers serve as the entry point for clients. They receive a query, chop it up into partial queries that can be handled by a single machine independently, and assign each partial query to a process on a data server. When the partial results come back, the query server assembles them, applies final processing such as sorting and aggregations, and returns the result to the caller.

The heavy lifting is mostly done by machines called data servers. A data server handles both data ingestion and partial queries.

Let’s look at streaming ingestion. An indexer process consumes data directly from a Kafka stream. These data are stored in memory as a realtime segment. They are already queryable. When a configurable time interval has been passed, the segment is closed off and a new segment is started. The finished segment is transformed into a columnar format. Within the segment, data is ordered by time. All alphanumeric data are dictionary compressed and bitmap indexed. The final result is binary compressed again, and written to deep storage. Deep storage serves as an archive and the source of truth.

From deep storage, segments are then loaded to the local storage of the data servers, typically twice replicated for resiliency and performance. Then they are available for querying by the historical processes.

A query’s result is collected from the realtime segments (via the indexers) and the historical segments. This encapsulates the kappa architecture and hides most of its detail from the database user.

Imply Polaris: Druid as a Service

Imply Polaris is a cloud based database-as-a-service based on Druid. Polaris is completely managed and offers a unified GUI and API to ingest, manage, and analyze your data. It can natively read from message streams with minimal configuration, and offers a built-in frontend for adhoc analytics, dashboarding and alerting.

I will be using Polaris as the analytics database in this tutorial.

Preparing your Data: Streaming ETL

We also need to concern ourselves with getting the data out of the transactional systems into our analytics architecture – the ETL part.

Instead of processing batches of data, streaming ETL has to be event driven. There are two ways of processing event data in a streaming ETL pipeline:

  • Simple event processing looks at one event at a time. Simple event processing is stateless which makes it easy to implement but limite the things you can do with it. This is used for format transformations, filtering, or data cleansing, for instance. An example for simple event processing is Apache NiFi.
  • Complex event processing looks at a collection of events over time, hence it is stateful and has to maintain a state store in the background. With that you can do things like windows aggregations, such as sliding averages or session aggregations. You can also join various event streams (think orders and shipments), or enrich data with lookup data that is itself event based. Complex event processing is possible using frameworks like Spark Streaming, Flink, or Kafka Streams.

In this tutorial, I will use Confluent Cloud for data delivery, and ksqlDB for streaming ETL. ksqlDB is a community licensed SQL framework on top of Kafka Streams, by Confluent. It is also available as a managed offering in Confluent Cloud, and that is what I will be using.

With ksqlDB, you can write a complex event streaming application as simple SQL statements. ksqlDB queries are typically persistent: unlike database queries, they continue running until they are explicitly stopped, and they continue to emit new events as they process new input events in real time. ksqlDB abstracts away for the most part the detail of event and state handling.

High-Level Architecture

So, here’s the plan:

  • Use Confluent Cloud (based on Kafka) for delivery
  • For streaming ETL, use ksqlDB – also in Confluent Cloud
  • ksqlDB will play data back into a Kafka topic
  • Use Imply Polaris’s built in Confluent Cloud connectivity to ingest the preprocessed data into Polaris.

Prerequisites

For this tutorial, you need a Confluent Cloud account. In this account, create an environmenta cluster, and a ksqlDB application.

The smallest size of cluster (Basic) will do.

Furthermore, you need an Imply Polaris environment. You can sign up for a free trial here.

Data Generation

I am using the imply-news data generator. It simulates clickstream data from a news publisher portal. You can find setup instructions in the repository if you want to run this yourself.

Here is a sample of the data:

{"timestamp": 1673512817.517501, "recordType": "click", "url": "https://imply-news.com/home/Sport/Step-left-list-discuss-up", "useragent": "Mozilla/5.0 (Windows; U; Windows NT 10.0) AppleWebKit/531.19.5 (KHTML, like Gecko) Version/5.0.5 Safari/531.19.5", "statuscode": "200", "state": "home", "statesVisited": ["home", "content", "content", "home"], "sid": 14362070, "uid": "86525", "isSubscriber": 0, "campaign": "fb-2 US Election", "channel": "display", "contentId": "Sport", "subContentId": "Step left list discuss up", "gender": "w", "age": "51-60", "latitude": "45.47885", "longitude": "133.42825", "place_name": "Lesozavodsk", "country_code": "RU", "timezone": "Asia/Vladivostok"}
{"timestamp": 1673512817.5246856, "recordType": "click", "url": "https://imply-news.com/affiliateLink/News/Argue-Congress-beautiful-go-usually-which-brother", "useragent": "Opera/9.37.(Windows CE; mni-IN) Presto/2.9.171 Version/11.00", "statuscode": "200", "state": "affiliateLink", "statesVisited": ["home", "affiliateLink"], "sid": 14357239, "uid": "59450", "isSubscriber": 0, "campaign": "fb-2 US Election", "channel": "social media", "contentId": "News", "subContentId": "Argue Congress beautiful go usually which brother", "gender": "w", "age": "26-35", "latitude": "4.96667", "longitude": "10.7", "place_name": "Tonga", "country_code": "CM", "timezone": "Africa/Douala"}
{"timestamp": 1673512730.502167, "recordType": "session", "useragent": "Mozilla/5.0 (compatible; MSIE 7.0; Windows NT 6.2; Trident/5.1)", "statesVisited": ["home", "exitSession"], "sid": 14333040, "uid": "74108", "isSubscriber": 1, "campaign": "fb-2 US Election", "channel": "social media", "gender": "m", "age": "26-35", "latitude": "-33.59217", "longitude": "-70.6996", "place_name": "San Bernardo", "country_code": "CL", "timezone": "America/Santiago", "home": 1, "content": 0, "clickbait": 0, "subscribe": 0, "plusContent": 0, "affiliateLink": 0, "exitSession": 1}
{"timestamp": 1673512817.5646698, "recordType": "click", "url": "https://imply-news.com/home/Sport/Unit-down-perform-religious-add-find-management", "useragent": "Mozilla/5.0 (Linux; Android 2.3.2) AppleWebKit/532.1 (KHTML, like Gecko) Chrome/34.0.876.0 Safari/532.1", "statuscode": "200", "state": "home", "statesVisited": ["home"], "sid": 14370083, "uid": "81655", "isSubscriber": 0, "campaign": "fb-2 US Election", "channel": "paid search", "contentId": "Sport", "subContentId": "Unit down perform religious add find management", "gender": "w", "age": "36-50", "latitude": "41.66394", "longitude": "-83.55521", "place_name": "Toledo", "country_code": "US", "timezone": "America/New_York"}
{"timestamp": 1673512817.5769033, "recordType": "click", "url": "https://imply-news.com/exitSession/Puzzle/Resource-within-author-can", "useragent": "Mozilla/5.0 (Android 5.0.2; Mobile; rv:62.0) Gecko/62.0 Firefox/62.0", "statuscode": "200", "state": "exitSession", "statesVisited": ["home", "content", "clickbait", "plusContent", "plusContent", "home", "exitSession"], "sid": 14345138, "uid": "72517", "isSubscriber": 0, "campaign": "fb-2 US Election", "channel": "social media", "contentId": "Puzzle", "subContentId": "Resource within author can", "gender": "m", "age": "51-60", "latitude": "37.60876", "longitude": "-77.37331", "place_name": "Mechanicsville", "country_code": "US", "timezone": "America/New_York"}

You can see that we have different kinds of objects in this topic, distinguished by the recordType field. These have different type and number of fields, which means we cannot just slurp up everything using off-the-shelf JSON ingestion.

(Why would you have different objects in a topic in a real life scenario? This blog discusses some of the reasons and the decision criteria.)

The ETL Pipeline

Let’s set up a basic ETL pipeline. We have two tasks:

  1. We need to filter the raw data, retaining only the records that have type click.
  2. Once we have a uniform structure, we want to filter out only records that originate from a specific country.

Here’s the overview in a chart:

The chart also suggests that we are going to need a total of three topics:

  • The original topic imply-news
  • A topic that contains all click data
  • A topic that contains only filtered click data.

We are going run some ksqlDB queries to achieve this. You can enter the queries directly into the ksqlDB editor in Confluent Cloud:

Creating a Stream

ksqlDB uses abstractions on top of Kafka topics: Streams and Tables. You can imagine Streams as describing a change log, and Tables as describing the last known state. For this tutorial, we will work with Streams only.

Let’s create a stream on top of the original topic. Because we have different types of objects, we have to treat the incoming events as unstructured data for now. We do this by specifying VALUE_FORMAT='KAFKA' in the statement, which interprets the data as a sequence of bytes without further assumptions:

CREATE OR REPLACE STREAM `imply-news-raw` (
  `sid_key` STRING KEY, 
  `payload` STRING 
) 
WITH ( KAFKA_TOPIC='imply-news', KEY_FORMAT='KAFKA', VALUE_FORMAT='KAFKA' );

This does not create any jobs or new topics yet!

Split by Type

Now let’s implement topic splitter. We will only retain records with type ‘click’. This is one way to splice up a topic that has different types of records. Data is still regarded as a blob and the splicing criteria is extracted with an explicit JSON function:

CREATE OR REPLACE STREAM `imply-news-clicks` WITH (
  KAFKA_TOPIC='imply-news-clicks',
  PARTITIONS=6,
  KEY_FORMAT='KAFKA',
  VALUE_FORMAT='KAFKA' ) AS
SELECT
  `sid_key`,
  `payload` 
FROM `imply-news-raw` 
WHERE EXTRACTJSONFIELD(`payload`, '$.recordType') = 'click';

While this statement looks similar to the first one, it does something very different: The idiom CREATE STREAM ... AS SELECT creates a new push query and a new topic to receive the result. Unlike a regular (pull) query, the push query continues to run and to produce new output for every input event!

Viewed another way, by issuing this statement you have just deployed a realtime streaming application!

From Unstructured to Structured Data

Now let’s reinterpret the cleansed data as structured JSON records. This is another CREATE STREAM statement that, like the first one, does not spawn a new process. But the VALUE_FORMAT is now JSON and we specify the fields for our record type explicitly. (There are more elegant ways to do this using the Schema Registry, but that is another story for another time.)

CREATE OR REPLACE STREAM `imply-news-cooked` (
  `sid_key` STRING KEY,
  `sid` STRING,
  `timestamp` BIGINT,
  `recordType` STRING,
  `url` STRING,
  `useragent` STRING,
  `statuscode` STRING,
  `state` STRING,
  `uid` STRING,
  `isSubscriber` INT,
  `campaign` STRING,
  `channel` STRING,
  `contentId` STRING,
  `subContentId` STRING,
  `gender` STRING,
  `age` STRING,
  `latitude` DOUBLE,
  `longitude` DOUBLE,
  `place_name` STRING,
  `country_code` STRING,
  `timezone` STRING
)
WITH ( KAFKA_TOPIC='imply-news-clicks', KEY_FORMAT='KAFKA', VALUE_FORMAT='JSON' );

Filtering the Data

And finally, let’s do a minimalistic example of further processing by filtering out only click events that originate from Germany, creating another push query and a new topic:

CREATE OR REPLACE STREAM `imply-news-de` WITH (
  KAFKA_TOPIC='imply-news-de',
  KEY_FORMAT='KAFKA',
  VALUE_FORMAT='JSON' ) AS
SELECT *
FROM `imply-news-cooked`
WHERE `country_code` = 'DE';

Here’s where you would apply any other filtering, massaging, joining, or whatever you would do in an ETL process. But let’s leave Confluent Cloud here and move on the the next step.

Ingesting the Data into Polaris

In Polaris, create a new source using Confluent Cloud as the input:

You will need your Confluent Cloud broker URL and credentials. How to acquire these, I have covered this in more detail in this post. There is a Test button to verify that the connection is working.

Once you are done, navigate to the Tables menu and create a new table:

Follow the documentation to set up the table schema based on your newly created connection:

Start the ingestion job and watch the data coming in! Now you can continue with building dashboards like this:

Conclusion

What have we just learnt?

It is easy to build a streaming analytics architecture on top of Kafka and Druid. I haven’t had to write a line of code to put this together.

We have seen two flavors of SQL which complement each other in streaming analytics, although they work in very different ways:

ksqlDB enables streaming SQL for ETL and event processing:

  • ksqlDB queries are mostly push queries – you continue to get new results. You can also view each query as an encapsulated streaming application.
  • Each ksqlDB query generates a new stream. Also, each aggregation that you defined in ksqlDB creates a new stream.

Druid enables streaming analytics:

  • Druid has a scalable and highly performant way to ingest streaming data.
  • Druid contains a (typically) fine grained model of all your data.
  • Druid queries can aggregate at any level and slice and dice every which way.
  • Druid queries are pull queries and give a snapshot of the data store at the time it was queried.

Together, they form a powerful combo!


Image source: Lambda and Kappa architecture diagrams are linked from this Ericsson blog.

  1. Not in the sense of hard real time like in embedded systems – here we speak of latencies in the range of seconds or fraction of a second.

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

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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.

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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

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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:...

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May 19, 2022

How Imply Polaris takes a security-first approach

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

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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...

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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.

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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.

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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.

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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.

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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.

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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...

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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.

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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.

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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...

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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.

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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? 

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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.

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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.

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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...

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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,...

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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...

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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...

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Dec 12, 2021

Log4Shell Vulnerability and Mitigation

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

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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!.

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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...

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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...

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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.

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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...

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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...

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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.

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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.

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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...

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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,...

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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,...

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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...

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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...

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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.

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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...

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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...

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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.

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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.

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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...

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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...

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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.

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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...

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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...

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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.

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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...

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