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

May 09, 2023
Hellmar Becker


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.


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": "", "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": "", "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": "", "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": "", "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 

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 (
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

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:

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:


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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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.

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Apr 25, 2023

What’s New in Imply Polaris – November 2022

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

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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.

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Apr 23, 2023

Why Analytics Need More than a Data Warehouse

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

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Apr 21, 2023

Why Open Source Matters for Databases

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

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Apr 20, 2023

Ingestion from Confluent Cloud and Kafka in Polaris

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

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Apr 18, 2023

What Makes a Database Built for Streaming Data?

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

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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.

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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...

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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...

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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

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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

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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.

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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.

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Jul 14, 2022

Upserts and Data Deduplication with Druid

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

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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

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Jun 29, 2022

When Streaming Analytics… Isn’t

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

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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.

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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...

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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.

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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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