How to Build a Sentiment Analysis Application with ChatGPT and Druid

May 21, 2023
Rick Jacobs


Like many in the developer community, I have tried ChatGPT from OpenAI.  Over my IT career, I have worked in many positions including as a data scientist/data engineer.  So, as I did my assessment of ChatGPT, I thought of ways to use the technology in practice.  I have done sentiment analysis before using custom algorithms that I wrote, specific NLP (Natural Language Processing) libraries, and low-code platforms like Weka, RapidMiner, and DataRobot.  Why not do something similar using ChatGPT and combine it with a real-time analytics database like Apache Druid? 

There are many benefits to combining a trained, NLP model with Apache Druid for sentiment analysis. Modern models such as GPT-3 and GPT-4 are highly effective in understanding and processing natural language. They can better identify nuances and context, resulting in more accurate results.  Sentiment analysis often requires processing large volumes of data, such as social media posts, reviews, or customer feedback. And then, aggregating and analyzing those sentiments at scale can reveal even more insights and identify patterns and trends – all crucial for businesses that need to react quickly to changes in customer sentiment.  

As this blog will show, the integration is relatively easy using technologies that are publicly accessible.  

Here is A Quick Summary of the Technologies

ChatGPT from OpenAI is a hot, trending topic these days.  It is defined as a deep learning-based language model trained using a large corpus of text data.  It uses unsupervised learning, contextual understanding, and probabilistic modeling techniques to generate human-like responses to natural language inputs.  Developers can integrate ChatGPT into their applications to provide functionality like language translation, summarization, sentiment analysis, conversation generation, etc. to users.

Apache Druid is an open-source, high-performance, analytics database designed for real-time data analysis. Druid is designed to efficiently handle terabytes and petabytes of batch and streaming data while supporting thousands of concurrent users with low latency and high throughput that enable sub-second queries. Druid is designed to scale horizontally by adding more nodes to the cluster as the data size grows. It can store and query both historical and real-time data and offers flexible data ingestion options, allowing users to import data from a variety of sources, including Kafka, Kinesis, and hundreds of databases. It also supports advanced analytics features such as theta sketches (approximate distinct counting based on the Apache Data Sketches library), time series forecasting, and anomaly detection. Developers use Druid to build custom applications that require fast, real-time querying of large data sets.

Developers use Twitter APIs (Application Programming Interface) to access Twitter’s data and functionality programmatically. Twitter APIs provide a range of endpoints for accessing different types of data, including tweets, users, and trends. You can use the APIs to create custom applications that interact with Twitter’s platform, such as social media monitoring tools, sentiment analysis tools, and chatbots that operate in real-time. 

Why Integrate ChatGPT and Druid?

Druid is designed to handle large volumes of data and can scale horizontally, as needed. Although it is purpose-built for streaming data, it can also ingest batch data, as I will describe later.  In production environments, Druid is optimized to handle sub-second queries at scale, with high concurrency, low latency, and high throughput which results in lower cost with higher user satisfaction. 

By using an AI tool like ChatGPT with Druid, you can perform sentiment analysis on massive datasets without compromising on query performance or accuracy. You could run ad-hoc aggregations and filters across different topics, populations, geographies, time ranges or 100s of other dimensions. Want to analyze your brand’s reputation by age group?  Or want to see what percentage of your followers are sending positive or negative sentiments at any period of time?  ChatGPT and Druid could empower businesses to make quick, data-driven decisions and respond to customer feedback or market trends in real-time.  Druid makes visualization really easy too by seamlessly integrating with a variety of data visualization tools, including Apache Superset, Tableau, Power BI, Looker, QlickView, and Grafana. 

Leveraging the strengths of both technologies, you can create robust solutions to tackle a range of AI analytical use cases, including:

  1. Customer Feedback Analysis: the AI model can be used to analyze customer feedback on social media by performing sentiment analysis on tweets related to a particular brand, product, or service. This data can be streamed to Druid to allow data analysts to generate conclusions based on real-time information.  The insights gained from this analysis can help organizations identify areas for improvement and address customer concerns.
  2. Brand Monitoring: the AI model combined with Druid can be used to monitor the sentiment of tweets related to a brand, including mentions of the brand, competitors, or industry trends. This can help organizations stay on top of their brand reputation and respond to any negative sentiment.
  3. Political Analysis: the AI model can be used to analyze tweets related to politics, including sentiment analysis of tweets related to political figures, events, and policies. These tweets can then be stored in Druid as streaming or batch data.  Developers can then build analytical applications, and visualizations that provide insights into public opinion on political issues and help shape political messaging.
  4. Social Media Marketing: the AI model can be used to analyze the sentiment of tweets related to social media marketing campaigns.  By persisting the sentiments and original tweets in Druid, organizations identify which campaigns resonate with their target audience and adjust their marketing strategies accordingly.
  5. Crisis Management: the AI model can be used to monitor the sentiment of tweets related to a crisis, such as a natural disaster or public health emergency.  This data can be saved in Druid and used to create time series dashboards, predict things like which goods will be most needed and analyze problems like misinformation.

For this project, I will capture tweets using the Twitter API, determine the sentiments of the tweets using a ChatGPT model, save the tweets in Druid segments and then produce a chart to summarize the overall sentiments.


The following prerequisites are required to execute this project.

Get Twitter API Credentials

The Twitter API allows developers to programmatically access Twitter data and functionality, such as searching for tweets, posting tweets, or retrieving user data.  Follow similar steps below to obtain Twitter API credentials:

  1. Create a Twitter account: If you don’t already have a Twitter account, create one at
  2. Apply for a developer account: Go to and apply for a developer account. You will need to fill out a form with your name, email, and a description of your intended use case for the Twitter API.
  3. Verify your email address: Once you submit your application, you will receive an email from Twitter with a link to verify your email address. Click the link to confirm your email.
  4. Create a Twitter app: After you verify your email address, log in to the Twitter Developer Dashboard and create a new app. Provide a name and description for your app, and select the use case that best matches your needs.
  5. Set up app permissions: Once you create your app, you will need to set up permissions for your app to access the Twitter API. Depending on your use case, you may need to request additional permissions from Twitter.
  6. Obtain API keys and access tokens: After you set up app permissions, Twitter will provide you with four credentials: API key, API secret key, access token, and access token secret. These credentials are necessary for you to authenticate and make requests to the Twitter API.
  7. Use the Twitter API: Once you have your API keys and access tokens, you can use them to make requests to the Twitter API. Twitter provides documentation and code examples to help you get started.

Get OpenAI ChatGPT Key

OpenAI provides access to its language models, including GPT (Generative Pre-trained Transformer), through the OpenAI API. To use the API, you need to create an account with OpenAI and obtain an API key. This API key is a unique identifier that allows you to access the OpenAI API and use the language models in your applications.  Follow the general steps below to obtain an OpenAI key:

  1. Create an account.
  2. Fill out the registration form with your email address, password, and other required information.
  3. Verify your email address by clicking on the verification link sent to your email inbox.
  4. Once you’ve verified your email address, log in to your OpenAI account.
  5. Click on the “API Keys” tab.
  6. Click the “New API Key” button.
  7. Copy the API key provided.
  8. Store the API key in a secure location.
  9. Note that there may be fees associated with using the OpenAI API, depending on your usage and the specific features you need. You can find more information about the OpenAI API plans and pricing here.

Druid Installed and Running

If Druid is not installed, please refer to my previous blog for local installation instructions. 

Let’s get started

Below is a data flow diagram (DFD) of how data flows through this system:

Pipeline DFD

In a nutshell, the process starts by fetching Twitter data containing text “ChatGPT”. The data is then passed through sentiment analysis using ChatGPT to enhance it. The enhanced data is saved to Apache Druid. The program then connects to Druid and retrieves the enhanced Twitter data. The program counts the number of occurrences of each value in the column, stores the counts in a variable, and creates a pie chart using the aggregation. Finally, the pie chart is shown, and the process ends. 

The data pipeline can be segmented into four specific actions.  Each step is explained in greater detail below.

Step 1: Gather the data

Once you have secured the required Twitter credentials (see above), the next step is to connect to the Twitter API and retrieve tweets.  This can be done in Python by using the tweepy library.  The connection code should be similar to this with your specific authentication secrets and keys:

def get_tweepy_api():
     consumer_key = "xxxxx"
     consumer_secret = "xxxxx"
     access_key = "xxxxx"
     access_secret = "xxxxx"
     auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
     auth.set_access_token(access_key, access_secret)
     api = tweepy.API(auth)
     return api
def get_tweets(api, containing_text, total_number_of_tweets, language):
     tweets = api.search_tweets(q=containing_text, lang=language, count=total_number_of_tweets)
     return tweets

After executing the authentication and search actions you will have the number of tweets specified in a tweets object. From the tweets I pull out the following fields:

  • id
  • created_at
  • full_text
  • retweeted

At this point it’s time for step two.

Step 2: Get the Sentiments

Now it’s time to enhance the Twitter data with the NLP sentiment analysis from ChatGPT. After obtaining the ChatGPT key (see above), it’s possible to access the models via the OpenAI API.  We will be using the text-davinci-003 model.  It is an advanced natural language processing model developed by OpenAI. It is capable of generating human-like text responses and is trained on a large corpus of text data using deep neural networks, enabling it to understand context and generate responses in a natural, conversational way. 

To use the model first import OpenAI.  Then questions can be asked and responses returned using the code sample below:

def question_davinci_model(prompt):
     openai.api_key = API_KEY
     response = openai.Completion.create(


     return response

I use the OpenAI API to ask questions of each tweet and then save the NLP responses.  The table below gives sample questions I use to gather the AI generated sentiment information to make the tweet data more meaningful.

ChaptGPT Questions

Data RequestedSample question
SentimentWhat is the sentiment of this statement?
RankingOn a scale of 1 to 10, how positive or negative is this tweet?
OpinionWhat is your opinion of this statement?
ProfileWhat is the profile of the person who would write this tweet?

Now that we have the tweets and their sentiment information.  It’s time to store that data.

Step 3: Ingest Data

There are several ways to ingest data into Druid.  I used a Python script to execute a command line instruction (see below).

def upload_data():
     execute_cmd = 'bin/post-index-task --file /Users/rick/IdeaProjects/twitter_chatgpt_druid/insert_config.json --url http://localhost:8081'
     return_message = os.system(execute_cmd)

This code uses the Python os library to execute the load instructions to load the data in the specified file and create indexes using a ‘bin/post-index-task’ utility that ships with Druid and the configuration file that I specified.  Below is an example of the insert_config.json that I used.

 "type": "index_parallel",
 "spec": {
   "ioConfig": {
     "type": "index_parallel",
     "inputSource": {
       "type": "local",
       "baseDir": "/Users/rick/IdeaProjects/twitter_chatgpt_druid/data",
       "filter": "*.csv"
     "inputFormat": {
       "type": "csv",
       "findColumnsFromHeader": true
   "tuningConfig": {
     "type": "index_parallel",
     "partitionsSpec": {
       "type": "dynamic"
   "dataSchema": {
     "dataSource": "tweets_sentiments_data",
     "timestampSpec": {
       "column": "tweet_created_at",
       "format": "auto"
     "dimensionsSpec": {
       "dimensions": [
           "type": "long",
           "name": "ai_ranking"
     "granularitySpec": {
       "queryGranularity": "none",
       "rollup": false,
       "segmentGranularity": "day"

In my analysis, I examined the tone of each tweet collected to gauge how users felt about the ChatGPT AI platform. This involved assessing whether the tweets conveyed positive, negative, or neutral opinions as determined by the AI model.  I could have chosen any other topic and replaced the text filter used to retrieve the tweets (see code snippet below).

if __name__ == "__main__":
   containing_text = "chatgpt"
   total_number_of_tweets = 100
   language = "en"
   api = get_tweepy_api()
   get_tweets_containing_text(api, containing_text, total_number_of_tweets, language)

Here is an example of what the data looks like in the Druid UI:

Now it’s time to visualize the results.

Step 4: Plot the Distribution

To plot the data, I first connect to Druid using a library called pydruid with specific connection details for the Druid database, such as the host, port, path, and scheme.  I then execute a SQL SELECT query to get the data.  I load the results into a pandas DataFrame and count the number of occurrences of each value in the DataFrame column and store the results in a variable. Finally, I generate a pie chart using the counts data and displays it using the ‘’ function from the matplotlib library.  The resulting chart shows the proportion of each value in the ‘ai_sentiment’ column.  Here is the sample code:

# Druid connection details

druid_host = "localhost"
druid_port = 8888

druid_path = "/druid/v2/sql"
druid_scheme = "http"

# Query to retrieve data from Druid

druid_query = "SELECT ai_sentiment FROM tweets_sentiments_data WHERE ai_sentiment IS NOT NULL"

# Connect to Druid and execute query

druid_connection = connect(host=druid_host, port=druid_port, path=druid_path, scheme=druid_scheme)

druid_cursor = druid_connection.cursor()

results = druid_cursor.execute(druid_query)

# Convert query results to a Pandas DataFrame

df = pd.DataFrame(druid_cursor.fetchall(), columns=[desc[0] for desc in druid_cursor.description])

# Count the number of occurrences of each value in the column

counts = df['ai_sentiment'].value_counts()

# Plot the counts as a pie chart


# Add a title to the chart

plt.title('Summary of Sentiment Analysis')

# Show the chart

The resulting pie chart shows that the vast majority of the tweets about ‘ChatGPT’ are positive based on the NLP analysis of the OpenAI, ChatGPT text-davinci-003 model. For the tweets I analyzed, the sentiment was overwhelmingly positive. I suspect that will be the general tone of the ChatGPT-themed tweets for the near future.  In that case, the graph will look similar to the one my data produced below:

I also executed a few queries from the Druid UI.  For example, I was curious about sentiments that were highly positive.  To get that information I used the following query:

SELECT ai_sentiment, tweet_text, ai_opinion, ai_profile
FROM tweets_sentiments_data
WHERE ai_sentiment = 'Positive' and ai_ranking = 10

I chose one of the resulting records:

The tweet was:

Musk has reached out to artificial intelligence researchers in recent weeks to set up a new research lab to develop…

The opinion from AI was:

I think this is an exciting opportunity for AI researchers to further their work and potentially revolutionize the industry.

Here is one interesting result.  Take a look at the profile of the person the text-davinci-003 model says would write the tweet:

The profile of a person who would use this statement is likely someone who is interested in technology and artificial intelligence.

It appears that the ChatGPT model is very impressed by the potential of AI.


To simplify this project, I only utilize a set number of tweets.  But for a production sentiment analysis application, the tweets could be streamed to a messaging service like Kafka or Kinesis. The tweets could then be analyzed and the data enhanced using one of many sentiment analysis libraries such as:

In this blog, I showed how to address a use case where sentiment analysis is required for a specific data source.  The same approach can be taken when dealing with other sources.  Get the data, enhance the data with an AI model, save the data, and run analytics.  Using Druid as the data store, these use cases can be addressed at scale using code for batch uploads or in real-time when sub-second analysis is required by thousands of concurrent users analyzing trillions of rows of data. The importance of Druid in this scenario is its ability to support fast analytical queries at scale.   

A real-time environment is where Druid truly shines.  It can connect to Kafka and Kinesis natively, so there is no need for a connector library or specific language-based SDK.  Users can capture and augment data using various AI technologies as it is loaded into Druid then the data can be and then analyzed and visualized.

The fact is, AI systems are becoming a part of everyday life. The key is to ensure that these machines are aligned with human intentions and values. Please feel free to use the sample code included to create your own solutions and stay tuned for my upcoming articles.

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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Apache Druid uses load rules to manage the ageing of segments from one historical tier to another and finally to purge old segments from the cluster. In this article, we’ll show what happens when you make...

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May 18, 2023

Real-Time Analytics: Building Blocks and Architecture

This blog identifies the key technical considerations for real-time analytics. It answers what is the right data architecture and why. It spotlights the technologies used at Confluent, Reddit, Target and 1000s...

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May 17, 2023

Transactions Come and Go, but Events are Forever

For decades, analytics has focused on Transactions. While Transactions are still important, the future of analytics is understanding Events.

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May 16, 2023

What’s new in Imply Polaris – Our Real-Time Analytics DBaaS

This blog explains some of the new features, functionality and connectivity added to Imply Polaris over the last two months. We've expanded ingestion capabilities, simplified operations and increased reliability...

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May 15, 2023

Elasticsearch and Druid

This blog will help you understand what Elasticsearch and Druid do well and will help you decide whether you need one or both to reach your goals

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May 14, 2023

Wow, that was easy – Up and running with Apache Druid

The objective of this blog is to provide a step-by-step guide on setting up Druid locally, including the use of SQL ingestion for importing data and executing analytical queries.

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May 13, 2023

Top 7 Questions about Kafka and Druid

Read on to learn more about common questions and answers about using Kafka with Druid.

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May 12, 2023

Tales at Scale Podcast Kicks off with the Apache Druid Origin Story

Tales at Scale cracks open the world of analytics projects and shares stories from developers and engineers who are building analytics applications or working within the real-time data space. One of the key...

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May 11, 2023

Real-time Analytics Database uses partitioning and pruning to achieve its legendary performance

Apache Druid uses partitioning (splitting data) and pruning (selecting subset of data) to achieve its legendary performance. Learn how to use the CLUSTERED BY clause during ingestion for performance and high...

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May 10, 2023

Easily embed analytics into your own apps with Imply’s DBaaS

This blog explains how developers can leverage Imply Polaris to embed robust visualization options directly into their own applications without them having to build a UI. This is super important because consuming...

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May 09, 2023

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

Learn how to set up a pipeline that generates a simulated clickstream event stream and sends it to Confluent Cloud, processes the raw clickstream data using managed ksqlDB in Confluent Cloud, delivers the processed...

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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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