Automate Streaming Data Ingestion with Kafka and Druid

Jul 20, 2023
Rick Jacobs

The technological world is a maze of data. Every interaction, transaction, and process generates data that could potentially be invaluable for businesses. But to truly benefit from data, organizations need effective ways to process and analyze it, as it’s created. Enter Kafka and Druid. Kafka serves as a robust message-broker system, adept at handling real-time data feeds efficiently, while Druid is a high-performance real-time analytics database, designed for workflows where fast queries, low latency, and stream ingest really matter.  However, managing data streams can be a challenging task, especially when you have to deal with large volumes of data from disparate sources. Streamlining and automating the process can greatly increase operational efficiency and make managing dynamic data easier.

What will you learn?

In this blog post, you will:

  • Learn how Kafka and Druid work together to manage and analyze data streams, with Kafka serving as a robust message-broker system and Druid, as a high-performance, real-time analytics database.
  • Gain insights on the process of automatically detecting new topics in Kafka and automating their ingestion into Druid.
  • Become familiar with the use of Python scripts to connect to the Kafka server, fetch the list of topics, and compare it with a previously stored list to detect new topics and ingest the new data into Druid.
  • Discover how an ‘Ingestion Spec’ is created for new topics and how it guides the ingestion and processing of data associated with these topics.
  • Learn about the schema auto-detection feature in Druid that simplifies the ingestion process.
  • Explore how to run SQL queries from within the Druid console.
  • Understand how automating the detection and ingestion of Kafka topics into Druid enhances operational efficiency and scalability.
  • Learn about the potential benefits of this automation, such as reducing manual workload, eliminating time delays, and ensuring immediate access to insights derived from the latest data.
  • Find out how to implement these automation processes through a step-by-step guide and the provided code samples.

Improve Efficiency by Automating Data Streams

Managing large-scale, real-time data requires substantial resources, and the risk of error is high. Automation offers a compelling solution to these challenges. It’s not only about doing things faster; it’s about scalability, reducing the risk of error, and freeing up valuable personnel for more complex tasks. By automating data ingestion from Kafka to Druid, organizations can streamline their data pipeline, enhancing efficiency and scalability.

In a dynamic data environment, new Kafka topics can appear at any time. Keeping track of these new topics and setting up ingestion processes for them manually is both time-consuming and prone to errors. This article demonstrates an automated discovery process that periodically checks for new topics and kickstarts the ingestion process whenever a new topic appears. Python scripts connect to the Kafka server, fetch the list of topics, and compare it with a previously stored list. Any new topics detected are then flagged for ingestion. This automatic discovery of topics ensures that no new data is missed due to a delay in setting up ingestion. It also reduces manual workload and the chance of errors that could occur during the setup process.

Once a new topic has been identified, the next step is ingesting the data associated with that topic into  Druid. This is accomplished by creating an ingestion spec, which is a JSON object describing where to find the data and how to ingest and process it, and executing that spec on a Druid cluster.  Druid’s schema auto-detection greatly simplifies the ingestion spec because a data schema is not required.  When schema auto-detection is set to “true”, Druid is capable of ingesting data without a user-defined data structure.  

How The Automatic Topic Ingestion Works

Kafka topics and corresponding messages can be automatically ingested into Druid using the following process.


To begin, you will need to install and configure  Druid on your local machine or server. Then install Kafka locally, write code to monitor the topics, and automatically set up the ingestion configuration in Druid with enable auto-schema detection enabled.

Install Druid Locally

1. Download Druid from and extract the file.

2. From the terminal, change directories to the distribution directory.

cd druid_26/distribution/target/apache-druid-27.0.0-SNAPSHOT

3. From the apache-druid-26.0.0 package root, run the following command to start the micro-quickstart configuration:


4. This starts up instances of ZooKeeper and the Druid services

5. After the Druid services finish startup, open the web UI at http://localhost:8888.

Note: To stop Druid at any time, use CTRL+C in the terminal. This exits the script and terminates all Druid processes.

Install Kafka

Kafka is a high-throughput message bus that works well with Druid. 

1.  Download the latest version of Kafka, using commands similar to the ones below (based on the Kafka version) in your terminal:

         curl -O

2.  Unzip the .tgz file:

         tar -xzf kafka_2.13-2.7.0.tgz

3.  Go to the Kafka directory

         cd to the location of the kafka_2.13-2.7.0 folder

4.  In the Kafka root directory, run this command to start a Kafka broker:

./bin/ config/

Note: To stop Kafka at any time, use CTRL+C in the terminal. This exits the start script and terminates all Kafka processes.

Detect New Topics

Once it is executed, this script continuously scans the Kafka server for new topics. Which is a distinct category where messages of a certain type are stored. The script establishes a connection to the Kafka server and identifies existing topics. It then enters a continuous loop, looking for any new topics that might appear. When a new topic is discovered, the script initiates the ingestion process into Druid, preparing the new data for immediate analysis.  An ingestion task is automatically created in Druid that consumes the messages from the new topic.  This process of checking for new topics, preparing for message delivery, and reading the messages continues until the script is terminated, ensuring your data is always up-to-date and ready for use.

import time
from confluent_kafka import Consumer
from confluent_kafka.admin import AdminClient

# Import Kafka ingest example for downstream processing
from kafka_ingest_example import create_kafka_ingest

# Kafka client setup with bootstrap servers specified
kafka_client = AdminClient({
	'bootstrap.servers': 'localhost:9092'  # Replace with your Kafka server address

# Consumer configuration
conf = {
	'bootstrap.servers': "localhost:9092",
	'': "group_id",
	'auto.offset.reset': 'earliest'

def get_existing_topics():
	Retrieves the list of existing topics on the Kafka broker
	# Request metadata from Kafka server
	metadata = kafka_client.list_topics(timeout=5)
	# Extract topic names from metadata and return them as a list
	topics = [topic.topic for topic in metadata.topics.values()]
	return topics

def msg_process(msg):
	Processes and prints the consumed Kafka message
	print(f'Message on {msg.topic()} [{msg.partition()}] at offset {msg.offset()} with key {msg.key()}: {msg.value()}')

def basic_consume_loop(conf, topic_name):
	Main loop for consuming messages from Kafka
	# Kafka Consumer creation with the specified configuration
	consumer = Consumer(conf)

    	# Subscribe the consumer to the specified topic

    	# Infinite loop for consuming messages
    	while True:
        	# Poll a message from the consumer
        	msg = consumer.poll(timeout=1.0)
        	if msg is None:
            	# Process the message if there's no error
    	# Close the consumer to commit final offsets

def get_new_topics(existing_topics):
	Checks for new topics on the Kafka broker
	# Request metadata from Kafka server
	metadata = kafka_client.list_topics(timeout=5)
	# Extract new topic names from metadata
	new_topics = [topic.topic for topic in metadata.topics.values() if topic.topic not in existing_topics]
	# Add the new topics to the existing ones
	return new_topics

if __name__ == "__main__":
	# Retrieve existing topics
	existing_topics = get_existing_topics()
	print(f"Existing topics: {existing_topics}")

	# Infinite loop for checking and consuming from new topics
	while True:
    	# Check for new topics
    	new_topics = get_new_topics(existing_topics)
    	if new_topics:
        	print(f'New topics: {new_topics}')
        	for topic_name in new_topics:
            	# Start downstream Druid ingestion process
     	       # Pause for a while before consuming messages and sending to new topic
            	# created in Druid
            	# Start consuming from the new topic
            	basic_consume_loop(conf, topic_name)

        	print(f'No new topics')
    	# Pause for a while before the next check

Ingest New Topics Into Druid

Once the monitoring code identifies the new topic, I automatically create an ingestion job for that new topic using the code sample below which creates a detailed  ‘Ingestion Spec’, for how to process messages from the new Kafka topic. This plan includes information about where the messages are coming from, and the specific topic, and sets schema auto-detection to ‘true’. It also includes instructions to start from the earliest message in the topic and not to group the messages. The granularity of the messages is set to ‘hour’, which means the data is divided into one-hour blocks.  When the script is executed, the Druid host will start processing messages according to the plan. This process ensures that the messages from the new Kafka topic are properly processed and organized in the Druid analytics database, keeping your data up-to-date and ready for analysis.

import json
import requests

# Define a function to create a Kafka Ingestion Spec for Druid
def create_kafka_ingest(topic_name, bootstrap_servers='localhost:9092', druid_host='http://localhost:8081/druid/indexer/v1/supervisor'):
	# Create the Kafka Ingestion Spec in JSON format
	kafka_ingestion_spec = json.dumps(
        	"type": "kafka",
        	"spec": {
            	"ioConfig": {
               	 "type": "kafka",
                	"consumerProperties": {
                    	"bootstrap.servers": bootstrap_servers
                	"topic": topic_name,
                	"inputFormat": {
                    	"type": "kafka",
                    	"valueFormat": {
                        	"type": "json"
                	"useEarliestOffset": True
            	"tuningConfig": {
         	       "type": "kafka"
            	"dataSchema": {
                	"dataSource": topic_name,
                	"timestampSpec": {
                    	"column": "kafka.timestamp",
                    	"format": "millis"
                	"dimensionsSpec": {
                    	"dimensions": [],
                    	"dimensionExclusions": [],
                    	"spatialDimensions": [],
                    	"useSchemaDiscovery": True
                	"granularitySpec": {
                    	"queryGranularity": "none",
                    	"rollup": False,
                    	"segmentGranularity": "hour"

	# Set headers for the post request
	headers = {
    	'Content-Type': 'application/json'

	print(f'Creating Kafka ingestion spec for {topic_name}')
	print(f'using this ingestion spec:\n{kafka_ingestion_spec}')

    	# Make a post request to Druid with the Kafka ingestion spec
    	kafka_supervisor_post =, kafka_ingestion_spec, headers=headers)
    	# Raise an exception if the request was unsuccessful
	except Exception as e:
    	print("Something went wrong with the request:", e)

	# Print the response

Note that with the new schema auto detection feature no schema requited in ingestion spec, see the “dimensionsSpec” section in the spec above.

Create Topic

I tested the monitoring and ingestion code using the code sample below.  This script sends a series of messages to a specific Kafka topic. Once the topic is recognized by the monitoring code we initiated earlier, then the topic is ingested into Druid.  If the code is interrupted, it makes sure that all the messages that have been generated are sent. This ensures that no messages are lost.

import json
import os
import subprocess
from datetime import datetime
from confluent_kafka import Producer
from confluent_kafka import KafkaException

def create_topic(topic_name):
    	# Change directory to the location of the Kafka scripts

    	# Run the Kafka script to create a new topic
        	['./bin/', '--create', '--topic', topic_name, '--bootstrap-server', 'localhost:9092'],
	except Exception as e:
    	# Log and raise any exception that occurs while creating the topic
    	print(f"Failed to create topic: {e}")

def simple_message(val):
    	# Create a timestamp and a message
    	now =
    	msg = f'This is message number {val} from Kafka created at {now}'

    	# Bundle the message and the timestamp into a JSON object
    	data = {
        	"message": msg,
        	"timestamp": now,

    	return json.dumps(data)
	except Exception as e:
    	# Log and raise any exception that occurs while creating the message
    	print(f"Failed to create message: {e}")

def send_simple_messages(i, producer, topic):
    	# Create a simple message
    	msg = simple_message(i)
    	# Produce the message to the Kafka topic
    	producer.produce(topic, value=msg)
	except KafkaException as e:
    	# Log and raise any KafkaException that occurs while sending the message
    	print(f"Failed to send message: {e}")

def run_producer(topic_name):
    	# Create the new topic
    	# Todo - Comment out the below line to prevent the topic from being created
    	# print('Line commented out to prevent the topic from being created')

    	producer = Producer({'bootstrap.servers': 'localhost:9092'})
    	print(f'The following messages will be sent to the topic: {topic_name}')

    	for i in range(1, 11):  # Adjusted to send only 10 messages
        	send_simple_messages(i, producer, topic_name)
	except KeyboardInterrupt:
    	# If the user interrupts the process, log the event and stop the producer
    	print("KeyboardInterrupt occurred, stopping producer...")
	except Exception as e:
    	# Log and raise any general exception that occurs while running the producer
    	print(f"Failed to run producer: {e}")
    	# Ensure that all messages are delivered before exiting

if __name__ == "__main__":
    	topic_name = 'my_topic_new'
    	# Run the Kafka producer with the specified topic
	except Exception as e:
    	# Log and raise any exception that occurs during the program's execution
    	print(f"Error occurred: {e}")

Query Data

To verify that the messages were ingested, select the query interface from within the Druid console.  And execute SQL similar to the example below:

The Analytics Query UI within the Druid Console allows for easy querying and analysis of the data stored. The Console provides real-time updates as data is ingested into Druid enabling instantaneous analysis of large data. Users can create custom queries using SQL query language or utilize pre-built templates for common analytical tasks. Advanced features such as filters, aggregations, and rollups enable complex data analysis thereby providing insights to enhance your business performance. Additionally, integration with other tools such as Grafana, Tableau, and Supersets allows for seamless end-to-end analytics workflows that improve your business intelligence processes.


In conclusion, this blog highlights the importance of effectively managing and analyzing data streams in the modern data-centric world. This solution utilizes Kafka as a powerful message-broker system and  Druid as a high-performance, real-time analytics database. Automating the detection and ingestion of Kafka topics into Druid significantly enhances operational efficiency and scalability.

This blog describes some benefits, such as reducing manual workload, eliminating time delays, and ensuring immediate access to insights derived from the latest data. Also included is a step-by-step guide with implementation code for the automation process. So don’t let your valuable data get lost in the shuffle or languish in latency. Start automating your data ingestion today, and take advantage of new opportunities to harness the power of your data.

About the Author

Rick Jacobs is a Senior Technical Product Marketing Manager at Imply. His varied background includes experience at IBM, Cloudera, and Couchbase. He has over 20 years of technology experience garnered from serving in development, consulting, data science, sales engineering, and other roles. He holds several academic degrees including an MS in Computational Science from George Mason University. When not working on technology, Rick is trying to learn Spanish and pursuing his dream of becoming a beach bum. 

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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...

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May 08, 2023

Real time DBaaS comes to Europe

We are excited to announce the availability of Imply Polaris in Europe, specifically in AWS eu-central-1 region based in Frankfurt. Since its launch in March 2022, Imply Polaris, the fully managed Database-as-a-Service...

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May 07, 2023

Stream big, think bigger—Analyze streaming data at scale in 2023

Imply is predicting the next "big thing" in 2023 will be analyzing streaming data in real time (and Druid is built for just that!)

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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.

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May 05, 2023

Introducing Apache Druid 25.0

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

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May 03, 2023

Druid and SQL syntax

This is a technical blog, which summarises the process of extending the Druid's SQL grammar for ingestion and delves into the nitty gritty of Calcite.

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May 02, 2023

Native support for semi-structured data in Apache Druid

Describes a new feature- ingest complex data as is into Druid- massive improvement in developer productivity

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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.

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May 01, 2023

Datanami Award

Apache Druid won Datanami's 2022 Readers’ and Editors’ Choice Awards for Reader's Choice "Best Data and AI Product or Technology: Analytics Database".

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Apr 30, 2023

Alerting and Security Features in Polaris

Describes new features - alerts and some security features- and how Imply customers can leverage it

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Apr 29, 2023

Ingestion from Amazon Kinesis and S3 into Imply Polaris

Imply Polaris now supports data ingestion from Amazon Kinesis and Amazon S3

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Apr 27, 2023

Getting the Most Out of your Data

Ingesting data from one table to another is easy and fast in Imply Polaris!

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Apr 26, 2023

Combating financial fraud and money laundering at scale with Apache Druid

Learn how Apache Druid enables financial services firms and FinTech companies to get immediate insights from petabytes-plus data volumes for anti-fraud and anti-money laundering compliance.

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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.

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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 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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