To remain competitive, businesses today seek out the best Business Intelligence Software. Are you trying to make a choice between Elasticsearch vs. Snowflake? In this article, we assist you in selecting the optimal solution by comparing Elasticsearch vs. Snowflake in 6 critical points. You can choose which software suits you the best after reading and analyzing the differences.
What is ElasticSearch?
Elasticsearch is a distributed, open-source search and analytics engine designed for fast full-text search, real-time data analytics, and log monitoring. It allows users to store, search, and analyze large volumes of unstructured data with low latency. It is part of the Elastic Stack, often used for search-based applications, log analysis, and anomaly detection.
What is Snowflake?
Snowflake is a cloud-based data warehouse platform designed for storing and analyzing structured and semi-structured data at scale. It provides a fully managed, scalable environment with support for SQL-based queries, allowing users to run complex analytics without managing the underlying infrastructure. Snowflake’s multi-cloud architecture enables seamless data storage and computing across AWS, Azure, and Google Cloud.
Hevo provides a straightforward, no-code solution for integrating Elasticsearch, enabling users to effortlessly ingest, transform, and load data in real time. With Hevo’s intuitive interface, you can connect to Elasticsearch in minutes, ensuring efficient data flow and quick access to valuable insights.
Additionally, Hevo supports Snowflake as a destination, allowing users to effortlessly store and analyze structured and semi-structured data at scale. By seamlessly transferring data from various sources to Snowflake, Hevo simplifies the process of maintaining a robust data warehouse environment.
What Hevo Offers for Elasticsearch and Snowflake Integration:
- Automated Schema Management: Hevo automatically manages schema detection and updates, streamlining data organization in both Elasticsearch and Snowflake.
- No-Code Connectivity: Easily connect to Elasticsearch and Snowflake without requiring complex coding or configurations.
- Real-Time Data Ingestion: Stream data into Elasticsearch or Snowflake in real time, ensuring immediate access to up-to-date information.
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Elasticsearch vs. Snowflake: Key Differences
Below we have mentioned 6 key points of difference between Elasticsearch vs. Snowflake which can help you make the right choice according to your needs.
General Information
| Elasticsearch | Snowflake |
| – Elasticsearch is a distributed, open-source, cost-free search and analytics platform for text, numbers, geographic, structured, and unstructured data. The Elastic stack, a collection of open-source, free technologies for data intake, enrichment, storage, analysis, and visualization, is led by Elasticsearch. It is frequently known as the ELK Stack (after Elasticsearch, Logstash, and Kibana). | – Snowflake is a data warehouse designed for the healthcare, gaming, media and advertising, financial services, software, supply chain, and logistics industries etc. |
Pricing Information
| Elasticsearch | Snowflake |
| – Elastic’s SMB and enterprise pricing information is available only upon request. You can contact the company for more details and ask for a quote. They offer a free edition too. | Snowflake offers four quote-based enterprise pricing plans.
Standard Edition:
-Complete SQL data warehouse.
-Native support for Avro, JSON, Parquet, and XML.
-Has connectors for ETL, BI, and other apps.
-Unlimited data size.
-Web-based user interface.
-Support only during business hours.
-Adat encryption is always enabled.
-Multifactor authentication.
-Role-based access control.
-Service response time is 4 hours.
-SOC 2type 2.
Premier Edition:
-Everything that’s included in the standard edition.
-Premier support available.
1 hour response time for service.
-Instant zero-copy cloning feature.
-Service availability SLA (Service level Availability).
-1-day data retention.
Enterprise Edition:
-Everything that’s included in the premier edition.
-Multi-cluster warehouse.
-Data retention history for 90 days.
-Federated authentication.
-Annual rekeying of encrypted data.
Enterprise Edition (for sensitive data):
-Everything that’s included in the Enterprise edition.
-Enhanced data and security policies.
-Data transmission is encrypted inside Snowflake.
-Support for PHI HIPAA data.
-PCI DSS compliance.
-HIPAA compliance is supported. |
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Integrations Supported
| Elasticsearch | Snowflake |
– AWS
– Azure
– Google Cloud
– Hadoop | – Looker
– Amazon Web Services
– Tableau
– Talend
– Fivetran
– Native integrations with Snowflake Native Apps. |
Company Size
| Elasticsearch | Snowflake |
| – Suitable for small enterprises, large enterprises, and medium businesses. | – Suitable for large enterprises and medium businesses. |
Customer Support
| Elasticsearch | Snowflake |
– Email: You can reach out to support@elastic.co for help.
– Training: You can join Elastic experts for upcoming live, virtual Elasticsearch training in your region.
– Phone: You can also contact them at (888) 225-0080 for further assistance. | – Live Support: Snowflake uses a personalized performance profile to proactively monitor your account and counsel you on any potential difficulties at a speed that allows you to fully utilize the Data Cloud.
– Phone: You can contact the customer support team at (844)766-9355. |
Use Cases
Elasticsearch Use Cases:
- Retailers are utilizing Elasticsearch to index their product inventories, catalogs, and attributes so that when customers search for a particular attribute, their store will present the appropriate products straight away.
- Businesses can improve user experience and customer experience by using Elasticsearch to process billions of events daily to analyze logs, guarantee constant system performance, or spot anomalies.
- To increase your user engagement KPIs, site content search can help you better understand what and why your users are seeking.
Snowflake Use Cases:
- Data lakes with raw data, ODS with staged data, and data warehouses and data marts with presentable and modeled data are just a few of the many use cases that Snowflake may be utilized for. Data processing is made simpler since users may combine data, analyze it, and change it against many kinds of data structures using just one language, SQL.
Integrate ElasticSearch to Snowflake
Integrate ElasticSearch to Redshift
Integrate AWS Elasticsearch to BigQuery
Final Thoughts
In this blog post, we have discussed Elasticsearch vs. Snowflake and their key features and compared them against different parameters.
Getting data from or into Elasticsearch or Snowflake can be a time-consuming and resource-intensive task, especially if you have multiple data sources. Instead of spending months developing and maintaining such data integrations, you can enjoy a smooth ride with Hevo Data’s 150+ plug-and-play integrations (including 40+ free sources).
Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
Frequently Asked Questions
1. What is the alternative of Snowflake in AWS?
The primary alternative to Snowflake in AWS (Amazon Web Services) is Amazon Redshift.
2. Is Snowflake elastic?
Yes, Snowflake is designed to be elastic.
3. Is Snowflake faster?
Snowflake is often considered faster for specific types of workloads compared to traditional or some cloud-based data warehouses.
Sharon is a data science enthusiast with a hands-on approach to data integration and infrastructure. She leverages her technical background in computer science and her experience as a Marketing Content Analyst at Hevo Data to create informative content that bridges the gap between technical concepts and practical applications. Sharon's passion lies in using data to solve real-world problems and empower others with data literacy.
