Elasticsearch vs Google BigQuery

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Elasticsearch

Stacks35.5K

Followers27.1K

Votes1.6K

Elasticsearch vs Google BigQuery: What are the differences?

Elasticsearch and Google BigQuery are both powerful tools for managing and analyzing large volumes of data. However, there are several key differences between these two platforms that set them apart in terms of functionality and use cases.

Scalability: Elasticsearch is designed for horizontal scalability, allowing users to easily add or remove nodes as their data grows. It uses a distributed architecture and supports sharding and replication to ensure high availability and performance. On the other hand, Google BigQuery is a fully managed data warehouse that automatically scales its resources based on the query workload. It can handle large datasets and complex queries efficiently without the need for manual scaling.
Data Structure: Elasticsearch is a full-text search and analytics engine that is schema-less and document-oriented. It stores data as JSON documents and provides powerful search capabilities using inverted indexes. Google BigQuery, on the other hand, is a structured data warehouse that organizes data in tables and columns. It is designed for running SQL-like queries on structured datasets, making it suitable for analytical and reporting purposes.
Real-time Analytics: Elasticsearch excels at real-time analytics and provides near-instantaneous search results. It supports real-time indexing and enables users to perform complex aggregations and calculations on the fly. Google BigQuery, on the other hand, offers batch processing for large-scale analytics. While it can handle real-time data ingestion, it may not provide the same level of near real-time results as Elasticsearch.
Cost: Elasticsearch is an open-source project that can be self-hosted or managed by a cloud provider. Its cost depends on factors such as storage, compute resources, and support subscriptions. Google BigQuery, on the other hand, is a fully managed service offered by Google Cloud. It has a pay-as-you-go pricing model based on the amount of data processed and stored, with different pricing tiers and options for optimizing costs.

5.User interface and ecosystem: Elasticsearch provides a RESTful API and a web-based management interface called Kibana, which offers data visualization and exploration capabilities. It also has a rich ecosystem of plugins and integrations, making it highly customizable and extensible. Google BigQuery offers a web-based console for managing and querying data, along with integrations with other Google Cloud services such as Dataflow and Cloud Storage. It also supports integration with popular BI tools like Tableau and PowerBI.

Deployment and Management: Elasticsearch can be self-hosted on-premises or deployed on cloud platforms like AWS, Azure, and Google Cloud. It requires manual setup, configuration, and maintenance of the cluster. On the other hand, Google BigQuery is a fully managed service that abstracts away the infrastructure management. Users can focus on data analysis and query optimizations without worrying about infrastructure provisioning and maintenance.

In Summary, Elasticsearch and Google BigQuery differ in their scalability, data structure, real-time analytics capabilities, cost, user interface, ecosystem, and deployment options.

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Advice on Google BigQuery, Elasticsearch

Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceon

Firebase

Elasticsearch

Algolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Detailed Comparison

Google BigQuery	Elasticsearch
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.	Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.	Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License
Statistics
Stacks 1.8K	Stacks 35.5K
Followers 1.5K	Followers 27.1K
Votes 152	Votes 1.6K
Pros & Cons
Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas	Pros 329 Powerful api 315 Great search engine 231 Open source 214 Restful 200 Near real-time search Cons 7 Resource hungry 6 Diffecult to get started 5 Expensive 4 Hard to keep stable at large scale
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	Kibana Beats Logstash

What are some alternatives to Google BigQuery, Elasticsearch?

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Amazon CloudSearch

Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.

Amazon Elasticsearch Service

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.

Manticore Search

It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

Related Comparisons

Elasticsearch vs Google BigQuery: What are the differences?

Scalability: Elasticsearch is designed for horizontal scalability, allowing users to easily add or remove nodes as their data grows. It uses a distributed architecture and supports sharding and replication to ensure high availability and performance. On the other hand, Google BigQuery is a fully managed data warehouse that automatically scales its resources based on the query workload. It can handle large datasets and complex queries efficiently without the need for manual scaling.
Data Structure: Elasticsearch is a full-text search and analytics engine that is schema-less and document-oriented. It stores data as JSON documents and provides powerful search capabilities using inverted indexes. Google BigQuery, on the other hand, is a structured data warehouse that organizes data in tables and columns. It is designed for running SQL-like queries on structured datasets, making it suitable for analytical and reporting purposes.
Real-time Analytics: Elasticsearch excels at real-time analytics and provides near-instantaneous search results. It supports real-time indexing and enables users to perform complex aggregations and calculations on the fly. Google BigQuery, on the other hand, offers batch processing for large-scale analytics. While it can handle real-time data ingestion, it may not provide the same level of near real-time results as Elasticsearch.
Cost: Elasticsearch is an open-source project that can be self-hosted or managed by a cloud provider. Its cost depends on factors such as storage, compute resources, and support subscriptions. Google BigQuery, on the other hand, is a fully managed service offered by Google Cloud. It has a pay-as-you-go pricing model based on the amount of data processed and stored, with different pricing tiers and options for optimizing costs.

Deployment and Management: Elasticsearch can be self-hosted on-premises or deployed on cloud platforms like AWS, Azure, and Google Cloud. It requires manual setup, configuration, and maintenance of the cluster. On the other hand, Google BigQuery is a fully managed service that abstracts away the infrastructure management. Users can focus on data analysis and query optimizations without worrying about infrastructure provisioning and maintenance.

In Summary, Elasticsearch and Google BigQuery differ in their scalability, data structure, real-time analytics capabilities, cost, user interface, ecosystem, and deployment options.

Elasticsearch vs Google BigQuery

Overview