Elasticsearch vs Google BigQuery

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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  1. 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.

Advice on Elasticsearch and Google BigQuery
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 376.2K views
Needs advice

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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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 8 upvotes · 280.4K views

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

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Mike Endale
Cloud FirestoreCloud Firestore

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

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Pros of Elasticsearch
Pros of Google BigQuery
  • 328
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
  • 200
    Near real-time search
  • 98
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
  • 26
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
  • 2
  • 2
    Easy setup
  • 1
  • 1
    Easy to get hot data
  • 1
  • 1
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
  • 1
    Not stable
  • 1
  • 0
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
  • 12
    Big Data
  • 11
    Full table scans in seconds, no indexes needed
  • 8
    Always on, no per-hour costs
  • 6
    Good combination with fluentd
  • 4
    Machine learning
  • 1
    Easy to manage
  • 0
    Easy to learn

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Cons of Elasticsearch
Cons of Google BigQuery
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
  • 4
    Hard to keep stable at large scale
  • 1
    You can't unit test changes in BQ data

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What are some alternatives to Elasticsearch and Google BigQuery?
Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
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.
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