Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Google BigQuery and Elasticsearch so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Google BigQuery and Elasticsearch perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

Google BigQuery vs Elasticsearch Breakdown


 
Database Model

Data warehouse

Distributed search and analytics engine, document-oriented

Architecture

BigQuery is a fully managed, serverless data warehouse provided by Google Cloud Platform. It is designed for high-performance analytics and utilizes Google’s infrastructure for data processing. BigQuery uses a columnar storage format for fast querying and supports standard SQL. Data is automatically sharded and replicated across multiple availability zones within a Google Cloud region

Elasticsearch is built on top of Apache Lucene and uses a RESTful API for communication. It stores data in a flexible JSON document format, and the data is automatically indexed for fast search and retrieval. Elasticsearch can be deployed as a single node, in a cluster configuration, or as a managed cloud service (Elastic Cloud)

License

Closed source

Elastic License

Use Cases

Business analytics, large-scale data processing, data integration

Full-text search, log and event data analysis, real-time application monitoring, analytics

Scalability

Serverless, petabyte-scale data warehouse that can handle massive amounts of data with no upfront capacity planning required

Horizontally scalable with support for data sharding, replication, and distributed querying

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Google BigQuery Overview

Google BigQuery is a fully-managed, serverless data warehouse and analytics platform developed by Google Cloud. Launched in 2011, BigQuery is designed to handle large-scale data processing and querying, enabling users to analyze massive datasets in real-time. With a focus on performance, scalability, and ease of use, BigQuery is suitable for a wide range of data analytics use cases, including business intelligence, log analysis, and machine learning.

Elasticsearch Overview

Elasticsearch is an open-source distributed search and analytics engine built on top of Apache Lucene. It was first released in 2010 and has since become popular for its scalability, near real-time search capabilities, and ease of use. Elasticsearch is designed to handle a wide variety of data types, including structured, unstructured, and time-based data. It is often used in conjunction with other tools from the Elastic Stack, such as Logstash for data ingestion and Kibana for data visualization.


Google BigQuery for Time Series Data

BigQuery can be used for storing and analyzing time series data, although it is more focused on traditional data warehouse use cases. BigQuery may struggle for use cases where low latency response times are required

Elasticsearch for Time Series Data

Elasticsearch can be used for time series data storage and analysis, thanks to its distributed architecture, near real-time search capabilities, and support for aggregations. However, it might not be as optimized for time series data as dedicated time series databases. Despite this, Elasticsearch is widely used for log and event data storage and analysis which can be considered time series data.


Google BigQuery Key Concepts

Some important concepts related to Google BigQuery include:

  • Projects: A project in BigQuery represents a top-level container for resources such as datasets, tables, and views.
  • Datasets: A dataset is a container for tables, views, and other data resources in BigQuery.
  • Tables: Tables are the primary data storage structure in BigQuery and consist of rows and columns.
  • Schema: A schema defines the structure of a table, including column names, data types, and constraints.

Elasticsearch Key Concepts

  • Inverted Index: A data structure used by Elasticsearch to enable fast and efficient full-text searches.
  • Cluster: A group of Elasticsearch nodes that work together to distribute data and processing tasks.
  • Shard: A partition of an Elasticsearch index that allows data to be distributed across multiple nodes for improved performance and fault tolerance.


Google BigQuery Architecture

Google BigQuery’s architecture is built on top of Google’s distributed infrastructure and is designed for high performance and scalability. At its core, BigQuery uses a columnar storage format called Capacitor, which enables efficient data compression and fast query performance. Data is automatically partitioned and distributed across multiple storage nodes, providing high availability and fault tolerance. BigQuery’s serverless architecture automatically allocates resources for queries and data storage, eliminating the need for users to manage infrastructure or capacity planning.

Elasticsearch Architecture

Elasticsearch is a distributed, RESTful search and analytics engine that uses a schema-free JSON document data model. It is built on top of Apache Lucene and provides a high-level API for indexing, searching, and analyzing data. Elasticsearch’s architecture is designed to be horizontally scalable, with data distributed across multiple nodes in a cluster. Data is indexed using inverted indices, which enable fast and efficient full-text searches.

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Google BigQuery Features

Columnar Storage

BigQuery’s columnar storage format, Capacitor, enables efficient data compression and fast query performance, making it suitable for large-scale data analytics.

Integration with Google Cloud

BigQuery integrates seamlessly with other Google Cloud services, such as Cloud Storage, Dataflow, and Pub/Sub, making it easy to ingest, process, and analyze data from various sources.

Machine Learning Integration

BigQuery ML enables users to create and deploy machine learning models directly within BigQuery, simplifying the process of building and deploying machine learning applications.

Elasticsearch Features

Elasticsearch provides powerful full-text search capabilities with support for complex queries, scoring, and relevance ranking.

Scalability

Elasticsearch’s distributed architecture enables horizontal scalability, allowing it to handle large volumes of data and high query loads.

Aggregations

Elasticsearch supports various aggregation operations, such as sum, average, and percentiles, which are useful for analyzing and summarizing data.


Google BigQuery Use Cases

Business Intelligence and Reporting

BigQuery is widely used for business intelligence and reporting, enabling users to analyze large volumes of data and generate insights to inform decision-making. Its fast query performance and seamless integration with popular BI tools, such as Google Data Studio and Tableau, make it an ideal solution for this use case.

Machine Learning and Predictive Analytics

BigQuery ML enables users to create and deploy machine learning models directly within BigQuery, simplifying the process of building and deploying machine learning applications. BigQuery’s fast query performance and support for large-scale data processing make it suitable for predictive analytics use cases.

Data Warehousing and ETL

BigQuery’s distributed architecture and columnar storage format make it an excellent choice for data warehousing and ETL (Extract, Transform, Load) workflows. Its seamless integration with other Google Cloud services, such as Cloud Storage and Dataflow, simplifies the process of ingesting and processing data from various sources.

Elasticsearch Use Cases

Log and Event Data Analysis

Elasticsearch is widely used for storing and analyzing log and event data, such as web server logs, application logs, and network events, to help identify patterns, troubleshoot issues, and monitor system performance.

Elasticsearch is a popular choice for implementing full-text search functionality in applications, websites, and content management systems due to its powerful search capabilities and flexible data model.

Security Analytics

Elasticsearch, in combination with other Elastic Stack components, can be used for security analytics, such as monitoring network traffic, detecting anomalies, and identifying potential threats.


Google BigQuery Pricing Model

Google BigQuery pricing is based on a pay-as-you-go model, with costs determined by data storage, query, and streaming. There are two main components to BigQuery pricing:

  • Storage Pricing: Storage costs are based on the amount of data stored in BigQuery. Users are billed for both active and long-term storage, with long-term storage offered at a discounted rate for infrequently accessed data.
  • Query Pricing: Query costs are based on the amount of data processed during a query. Users can choose between on-demand pricing, where they pay for the data processed per query, or flat-rate pricing, which provides a fixed monthly cost for a certain amount of query capacity.

Elasticsearch Pricing Model

Elasticsearch is open-source software and can be self-hosted without any licensing fees. However, operational costs, such as hardware, hosting, and maintenance, should be considered. Elasticsearch also offers a managed cloud service called Elastic Cloud, which provides various pricing tiers based on factors like storage, computing resources, and support. Elastic Cloud includes additional features and tools, such as Kibana, machine learning, and security features.