Migrate Analytics Data from Snowflake to Apache Druid
Sep 19, 2023
Rick Jacobs
In recent years, the world of data analytics has seen a significant shift towards cloud-based data warehousing solutions. Snowflake has emerged as a popular choice among developers for its ease of use and scalability. However, as data volumes continue to grow, many developers are finding that Snowflake’s performance may not be sufficient for their needs. In this blog, we will walk through how to migrate data from Snowflake to Apache Druid. Including the code required to connect to Snowflake, download the data, and ingest that data into Druid.
Druid is designed specifically for high-performance analytical queries. Druid stores data in a columnar format, similar to Snowflake, but it uses an indexing engine to speed up query performance. The through indexing allows Druid to quickly filter and aggregate data at the segment level. This means that Druid can retrieve only the relevant data for a given query, rather than retrieving the entire dataset. This approach significantly reduces the amount of I/O required to retrieve data from disk, resulting in faster query performance. Druid is also purpose-built to handle real-time stream ingestion from sources like Kakfa and Kenisis, making it an excellent choice for applications that require real-time analytics.
The Data Migration Process
To execute the steps outlined in this blog, you will need the following prerequisites:
This Python script below extracts data from a Snowflake database and subsequently saves it in CSV format. The program begins by loading configuration details from a `database_configs.json` file using the `load_config` function. After reading the configuration, it connects to the Snowflake database with the `connect_to_snowflake` function, using the provided credentials and database details. Once connected, the script executes a SQL query to retrieve all records from the `TBL_CUSTOMERS` table using the `execute_query` function. The results are then saved to a CSV file via the `save_results_to_csv` function. If any issues occur during the execution, appropriate error messages are displayed. Once the data retrieval and storage process concludes, the script invokes the `create_ingest_spec` function from the `snowflake_ingest` module to upload the data to Druid. That code is provided later in the blog.
Python
import csvimport snowflake.connectorimport jsonfrom snowflake_ingest import create_ingest_specCONFIG_PATH ='/Users/rick/IdeaProjects/CodeProjects/druid_data_integrations/database_configs.json'defload_config(file_path=CONFIG_PATH):"""Load configuration from a JSON file."""withopen(file_path,'r')as file:return json.load(file)defconnect_to_snowflake(config):"""Connect to Snowflake using provided configuration."""return snowflake.connector.connect(user=config['snowflake'].get('user'),password=config['snowflake'].get('password'),account=config['snowflake'].get('account'),database=config['snowflake'].get('database'))defexecute_query(conn,query):"""Execute a SQL query using an active Snowflake connection."""with conn.cursor()as cur: cur.execute(query)return cur.fetchall(), cur.descriptiondefsave_results_to_csv(data,description,output_file="data/snowflake_data.csv"):"""Save the result set to a CSV file."""withopen(output_file,'w',newline='')as file: writer = csv.writer(file) writer.writerow([desc[0]for desc in description]) writer.writerows(data)defget_data():try: config =load_config()withconnect_to_snowflake(config)as conn: data, description =execute_query(conn,'SELECT * from TBL_CUSTOMERS')save_results_to_csv(data, description)print("Snowflake connection and query successful.")exceptExceptionas e:print(f"An error occurred: {e}")if __name__ =="__main__":get_data()create_ingest_spec()
Note that the load_config function uses a config file to store Snowflake connection data.
Below is an example of the `database_configs.json` that stores the database configuration details that are used in the script above.
Once you’ve extracted the data from Snowflake, the next step is to load it into Druid. For this batch upload process, the script below creates an ingestion specification for Druid. The `construct_ingestion_spec` function had two primary parameters: the data source’s name and the directory where the data resides, in a CSV format. The script sends the specification to a Druid host using the `post_ingest_spec_to_druid` function. This function dispatches the ingestion specification via an HTTP POST request, uisng the `requests` library. During this transmission, any errors, are captured and displayed to the user. For convenience, the script uses default values for the data source, directory, and Druid host which can be overridden with passed in parameters to the `create_ingest_spec` function.
Python
import jsonimport requestsdefconstruct_ingestion_spec(DATA_SOURCE,BASE_DIR):# Construct Ingestion Spec in JSON formatreturn{"type":"index_parallel","spec":{"ioConfig":{"type":"index_parallel","inputSource":{"type":"local","baseDir": BASE_DIR,"filter":f"{DATA_SOURCE}.csv"},"inputFormat":{"type":"csv","findColumnsFromHeader":True}},"tuningConfig":{"type":"index_parallel","partitionsSpec":{"type":"dynamic"}},"dataSchema":{"dataSource": DATA_SOURCE,"timestampSpec":{"column":"CREATED_AT","format":"auto"},"dimensionsSpec":{"useSchemaDiscovery":True,"dimensionExclusions":[]},"granularitySpec":{"queryGranularity":"none","rollup":False,"segmentGranularity":"hour"}}}}defpost_ingest_spec_to_druid(spec,DRUID_HOST):"""Post the ingestion spec to Druid and return the response.""" headers ={'Content-Type':'application/json'} response = requests.post(DRUID_HOST, json.dumps(spec),headers=headers)return responsedefcreate_ingest_spec(DATA_SOURCE='snowflake_data',BASE_DIR='/Users/rick/IdeaProjects/CodeProjects/druid_data_integrations/data',DRUID_HOST='http://localhost:8081/druid/indexer/v1/task'): spec =construct_ingestion_spec(DATA_SOURCE, BASE_DIR)print(f'Creating .csv ingestion spec for {DATA_SOURCE}')try: response =post_ingest_spec_to_druid(spec, DRUID_HOST) response.raise_for_status()print(response.text)except requests.HTTPError:print(f"HTTP error occurred: {response.text}")exceptExceptionas e:print(f"An error occurred: {e}")if __name__ =="__main__":create_ingest_spec()
After the initial batch data ingest, updates from Snowflake can be handled by a simple change data capture (CDC) process using a LAST_UPDATED date time field to determine the updated or new records, along with data load code to add the new/updated fields or by using various ELT tools. For example:
Snowflake Streams: Snowflake has a feature called “Streams” that allows you to capture changes made to a table. Using Snowflake Streams, you can identify changed data and then use an ETL tool or custom script to send those changes to Druid.
ETL Tools: Tools like Apache NiFi, Talend, or StreamSets can be used to create workflows that capture, transform, and load changes from Snowflake to Druid.
Custom Scripts: Depending on the complexity and volume of your changes, you might opt to write custom scripts that regularly poll Snowflake for changes and then push those changes to Druid.
Third-party Solutions: There are third-party solutions and managed services that offer CDC capabilities and could be configured to work between Snowflake and Druid. It’s worth exploring these if you’re looking for a more managed solution.
Sample Query
To verify that the data was ingested, select the query interface from within the Druid console and execute SQL similar to the example below:
Summary
By migrating analytical data to Druid, organizations can enhance their data warehousing capabilities and tap into its real-time analytics power. Druid opens up a whole new set of use cases by offering improved performance and scalability. Following a step-by-step migration process such as the one outlined in this blog and implementing best practices like data denormalization can greatly simplify the migration process. So if you are looking to unlock the full potential of your analytics, consider moving datasets to Apache Druid.
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.
Other blogs you might find interesting
No records found...
Sep 21, 2023
Migrate Analytics Data from MongoDB to Apache Druid
This blog presents a concise guide on migrating data from MongoDB to Druid. It includes Python scripts to extract data from MongoDB, save it as CSV, and then ingest it into Druid. It also touches on maintaining...
How Druid Facilitates Real-Time Analytics for Mass Transit
Mass transit plays a key role in reimagining life in a warmer, more densely populated world. Learn how Apache Druid helps power data and analytics for mass transit.
Apache Kafka, Flink, and Druid: Open Source Essentials for Real-Time Applications
Apache Kafka, Flink, and Druid, when used together, create a real-time data architecture that eliminates all these wait states. In this blog post, we’ll explore how the combination of these tools enables...
Visualizing Data in Apache Druid with the Plotly Python Library
In today's data-driven world, making sense of vast datasets can be a daunting task. Visualizing this data can transform complicated patterns into actionable insights. This blog delves into the utilization of...
Bringing Real-Time Data to Solar Power with Apache Druid
In a rapidly warming world, solar power is critical for decarbonization. Learn how Apache Druid empowers a solar equipment manufacturer to provide real-time data to users, from utility plant operators to homeowners
When to Build (Versus Buy) an Observability Application
Observability is the key to software reliability. Here’s how to decide whether to build or buy your own solution—and why Apache Druid is a popular database for real-time observability
How Innowatts Simplifies Utility Management with Apache Druid
Data is a key driver of progress and innovation in all aspects of our society and economy. By bringing digital data to physical hardware, the Internet of Things (IoT) bridges the gap between the online and...
Three Ways to Use Apache Druid for Machine Learning Workflows
An excellent addition to any machine learning environment, Apache Druid® can facilitate analytics, streamline monitoring, and add real-time data to operations and training
Apache Druid® is an open-source distributed database designed for real-time analytics at scale. Apache Druid 27.0 contains over 350 commits & 46 contributors. This release's focus is on stability and scaling...
Unleashing Real-Time Analytics in APJ: Introducing Imply Polaris on AWS AP-South-1
Imply, the company founded by the original creators of Apache Druid, has exciting news for developers in India seeking to build real-time analytics applications. Introducing Imply Polaris, a powerful database-as-a-Service...
In this guide, we will walk you through creating a very simple web app that shows a different embedded chart for each user selected from a drop-down. While this example is simple it highlights the possibilities...
Automate Streaming Data Ingestion with Kafka and Druid
In this blog post, we explore the integration of Kafka and Druid for data stream management and analysis, emphasizing automatic topic detection and ingestion. We delve into the creation of 'Ingestion Spec',...
This guide explores configuring Apache Druid to receive Kafka streaming messages. To demonstrate Druid's game-changing automatic schema discovery. Using a real-world scenario where data changes are handled...
Imply Polaris, our ever-evolving Database-as-a-Service, recently focused on global expansion, enhanced security, and improved data handling and visualization. This fully managed cloud service, based on Apache...
Introducing hands-on developer tutorials for Apache Druid
The objective of this blog is to introduce the new set of interactive tutorials focused on the Druid API fundamentals. These tutorials are available as Jupyter Notebooks and can be downloaded as a Docker container.
In this blog article I’ll unpack schema auto-discovery, a new feature now available in Druid 26.0, that enables Druid to automatically discover data fields and data types and update tables to match changing...
Druid now has a new function, Unnest. Unnest explodes an array into individual elements. This blog contains design methodology and examples for this new Unnest function both from native and SQL binding perspectives.
What’s new in Imply Polaris – Our Real-Time Analytics DBaaS
Every week we add new features and capabilities to Imply Polaris. This month, we’ve expanded security capabilities, added new query functionality, and made it easier to monitor your service with your preferred...
Apache Druid® 26.0, an open-source distributed database for real-time analytics, has seen significant improvements with 411 new commits, a 40% increase from version 25.0. The expanded contributor base of 60...
How to Build a Sentiment Analysis Application with ChatGPT and Druid
Leveraging ChatGPT for sentiment analysis, when combined with Apache Druid, offers results from large data volumes. This integration is easily achievable, revealing valuable insights and trends for businesses...
In this blog, we will compare Snowflake and Druid. It is important to note that reporting data warehouses and real-time analytics databases are different domains. Choosing the right tool for your specific requirements...
Learn how to achieve sub-second responses with Apache Druid
Learn how to achieve sub-second responses with Apache Druid. This article is an in-depth look at how Druid resolves queries and describes data modeling techniques that improve performance.
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...
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...
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...
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.
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...
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...
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...
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...
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...
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.
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.
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.
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.
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...
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
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...
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...
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
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.
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...
Apache Druid 0.23.0 contains over 450 updates, including new features, major performance enhancements, bug fixes, and major documentation improvements.
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:...
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...
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.
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.
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...
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.
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...
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?
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.
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...
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,...
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...
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...
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!.
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...
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...
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.
Community Discoveries: multi-value dimensions in Apache Druid
Hellmar Becker is an Imply solutions engineer based in Germany, where he has been delving into the nooks-and-crannies of multi-valued dimension support in Druid. In this interview, Hellmar explains why...
Community Spotlight: Apache Pulsar and Apache Druid get close…
The community team at Imply spoke with an Apache Pulsar community member, Giannis Polyzos, about how collaboration between open source communities generates great things, and more specifically, about how...
Meet the team: Abhishek Agarwal, engineering lead in India
Abhishek is Imply’s first engineer in India. We spoke to him about setting up our operations in Bangalore and asked what kind of local talent the company is looking for.
Jihoon Son is a software engineer at Imply who works on Apache Druid®. He explains what drew him to Imply five years ago and why he’s even more inspired by the company today.