Alternatives to Google BigQuery logo
Google Cloud Bigtable, Amazon Redshift, Hadoop, Snowflake, and Google Analytics are the most popular alternatives and competitors to Google BigQuery.
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What is Google BigQuery?

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.
Google BigQuery is a tool in the Big Data as a Service category of a tech stack.

Google BigQuery alternatives & related posts

Google Cloud Bigtable logo

Google Cloud Bigtable

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The same database that powers Google Search, Gmail and Analytics
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Amazon Redshift logo

Amazon Redshift

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Fast, fully managed, petabyte-scale data warehouse service
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Julien DeFrance
Julien DeFrance
Full Stack Engineering Manager at ValiMail · | 16 upvotes · 183.5K views
atSmartZipSmartZip
Amazon DynamoDB
Amazon DynamoDB
Ruby
Ruby
Node.js
Node.js
AWS Lambda
AWS Lambda
New Relic
New Relic
Amazon Elasticsearch Service
Amazon Elasticsearch Service
Elasticsearch
Elasticsearch
Superset
Superset
Amazon Quicksight
Amazon Quicksight
Amazon Redshift
Amazon Redshift
Zapier
Zapier
Segment
Segment
Amazon CloudFront
Amazon CloudFront
Memcached
Memcached
Amazon ElastiCache
Amazon ElastiCache
Amazon RDS for Aurora
Amazon RDS for Aurora
MySQL
MySQL
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
Docker
Docker
Capistrano
Capistrano
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Rails API
Rails API
Rails
Rails
Algolia
Algolia

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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Ankit Sobti
Ankit Sobti
CTO at Postman Inc · | 10 upvotes · 53.1K views
atPostmanPostman
dbt
dbt
Amazon Redshift
Amazon Redshift
Stitch
Stitch
Looker
Looker

Looker , Stitch , Amazon Redshift , dbt

We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

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Hadoop logo

Hadoop

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Open-source software for reliable, scalable, distributed computing
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StackShare Editors
StackShare Editors
| 4 upvotes · 13K views
atUber TechnologiesUber Technologies
Hadoop
Hadoop
Logstash
Logstash
Elasticsearch
Elasticsearch
Kibana
Kibana
Kafka
Kafka

With interactions across each other and mobile devices, logging is important as it is information for internal cases like debugging and business cases like dynamic pricing.

With multiple Kafka clusters, data is archived into Hadoop before expiration. Data is ingested in realtime and indexed into an ELK stack. The ELK stack comprises of Elasticsearch, Logstash, and Kibana for searching and visualization.

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StackShare Editors
StackShare Editors
Apache Thrift
Apache Thrift
Kotlin
Kotlin
Presto
Presto
HHVM (HipHop Virtual Machine)
HHVM (HipHop Virtual Machine)
gRPC
gRPC
Kubernetes
Kubernetes
Apache Spark
Apache Spark
Airflow
Airflow
Terraform
Terraform
Hadoop
Hadoop
Swift
Swift
Hack
Hack
Memcached
Memcached
Consul
Consul
Chef
Chef
Prometheus
Prometheus

Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.

Apps
  • Web: a mix of JavaScript/ES6 and React.
  • Desktop: And Electron to ship it as a desktop application.
  • Android: a mix of Java and Kotlin.
  • iOS: written in a mix of Objective C and Swift.
Backend
  • The core application and the API written in PHP/Hack that runs on HHVM.
  • The data is stored in MySQL using Vitess.
  • Caching is done using Memcached and MCRouter.
  • The search service takes help from SolrCloud, with various Java services.
  • The messaging system uses WebSockets with many services in Java and Go.
  • Load balancing is done using HAproxy with Consul for configuration.
  • Most services talk to each other over gRPC,
  • Some Thrift and JSON-over-HTTP
  • Voice and video calling service was built in Elixir.
Data warehouse
  • Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
Etc
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Snowflake logo

Snowflake

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The data warehouse built for the cloud
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    Snowflake
    Snowflake
    Google BigQuery
    Google BigQuery

    I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.

    What's nice too is that it has SQL-based ML tools, and it has great GIS support!

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    related Google Analytics posts

    Tassanai Singprom
    Tassanai Singprom
    Slack
    Slack
    BrowserStack
    BrowserStack
    Sentry
    Sentry
    Kibana
    Kibana
    Visual Studio Code
    Visual Studio Code
    npm
    npm
    GitLab
    GitLab
    GitHub
    GitHub
    Git
    Git
    Elasticsearch
    Elasticsearch
    Postman
    Postman
    Google Analytics
    Google Analytics
    MariaDB
    MariaDB
    GraphQL
    GraphQL
    Amazon RDS
    Amazon RDS
    Lumen
    Lumen
    Laravel
    Laravel
    Firebase
    Firebase
    Vue.js
    Vue.js
    Sass
    Sass
    Ubuntu
    Ubuntu
    Amazon EC2
    Amazon EC2
    Redis
    Redis
    jQuery
    jQuery
    HTML5
    HTML5
    PHP
    PHP
    JavaScript
    JavaScript

    This is my stack in Application & Data

    JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

    My Utilities Tools

    Google Analytics Postman Elasticsearch

    My Devops Tools

    Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

    My Business Tools

    Slack

    See more
    Jack Graves
    Jack Graves
    Head of Product Development at Automation Consultants · | 5 upvotes · 4.3K views
    atAutomation ConsultantsAutomation Consultants
    Google Analytics
    Google Analytics
    Segment
    Segment

    We currently use Google Analytics in our stack to monitor page views across our Marketplace listings, Documentation site and primary website. We're currently also investigating using Segment to provide in-app analytics in our Cloud App offerings. We expect to use Segment to guide our App development efforts, by monitoring, anonymously which features our clients use the most - but this is in early stages and will involve an update to our privacy policy.

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    related Amazon Athena posts

    Google BigQuery
    Google BigQuery
    Amazon Athena
    Amazon Athena

    I use Amazon Athena because similar to Google BigQuery , you can store and query data easily. Especially since you can define data schema in the Glue data catalog, there's a central way to define data models.

    However, I would not recommend for batch jobs. I typically use this to check intermediary datasets in data engineering workloads. It's good for getting a look and feel of the data along its ETL journey.

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    Stitch logo

    Stitch

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    All your data. In your data warehouse. In minutes.
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    Ankit Sobti
    Ankit Sobti
    CTO at Postman Inc · | 10 upvotes · 53.1K views
    atPostmanPostman
    dbt
    dbt
    Amazon Redshift
    Amazon Redshift
    Stitch
    Stitch
    Looker
    Looker

    Looker , Stitch , Amazon Redshift , dbt

    We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

    For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

    See more
    Cloudera Enterprise logo

    Cloudera Enterprise

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    Enterprise Platform for Big Data
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      Alooma logo

      Alooma

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      Integrate any data source like databases, applications, and any API - with your own Amazon Redshift
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        Xplenty logo

        Xplenty

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        Code-free data integration, data transformation and ETL in the cloud
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        Matillion logo

        Matillion

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        An ETL Tool for BigData
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          etleap logo

          etleap

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          user-friendly, sophisticated ETL-as-a-service on AWS
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            Dremio logo

            Dremio

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            Self-service data for everyone
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