Alternatives to Google BigQuery logo

Alternatives to Google BigQuery

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 and what are its top alternatives?

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.

Top Alternatives to Google BigQuery

  • Google Cloud Bigtable

    Google Cloud Bigtable

    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail. ...

  • Amazon Redshift

    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. ...

  • Hadoop

    Hadoop

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...

  • Snowflake

    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. ...

  • Google Analytics

    Google Analytics

    Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications. ...

  • Elasticsearch

    Elasticsearch

    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). ...

  • Amazon Athena

    Amazon Athena

    Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. ...

  • MySQL

    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

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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PROS OF GOOGLE CLOUD BIGTABLE
  • 9
    High performance
  • 9
    Fully managed
  • 5
    High scalability
CONS OF GOOGLE CLOUD BIGTABLE
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    related Google Cloud Bigtable posts

    Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

    Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

    Check Out My Architecture: CLICK ME

    Check out the GitHub repo attached

    See more
    Amazon Redshift logo

    Amazon Redshift

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    PROS OF AMAZON REDSHIFT
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      Data Warehousing
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      Scalable
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      SQL
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      Backed by Amazon
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      Encryption
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      Cheap and reliable
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      Isolation
    • 1
      Best Cloud DW Performance
    • 1
      Fast columnar storage
    CONS OF AMAZON REDSHIFT
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      related Amazon Redshift posts

      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

      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.

      See more
      Ankit Sobti

      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
      Hadoop logo

      Hadoop

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      PROS OF HADOOP
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        Great ecosystem
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        One stack to rule them all
      • 4
        Great load balancer
      • 1
        Amazon aws
      • 1
        Java syntax
      CONS OF HADOOP
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        related Hadoop posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1M views

        Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

        Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

        https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

        (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

        See more
        Shared insights
        on
        KafkaKafkaHadoopHadoop
        at

        The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

        For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

        Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

        See more
        Snowflake logo

        Snowflake

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        The data warehouse built for the cloud
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        PROS OF SNOWFLAKE
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          Good Performance
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          User Friendly
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          Serverless
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          Great Documentation
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          Multicloud
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          Public and Private Data Sharing
        • 1
          Usage based billing
        • 1
          Innovative
        • 1
          Economical
        CONS OF SNOWFLAKE
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          related Snowflake posts

          Shared insights
          on
          Google BigQueryGoogle BigQuerySnowflakeSnowflake

          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!

          See more
          Shared insights
          on
          SnowflakeSnowflakeHadoopHadoopMarkLogicMarkLogic

          For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?

          See more
          Google Analytics logo

          Google Analytics

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          Enterprise-class web analytics.
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          PROS OF GOOGLE ANALYTICS
          • 1.5K
            Free
          • 925
            Easy setup
          • 888
            Data visualization
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            Real-time stats
          • 403
            Comprehensive feature set
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            Goals tracking
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            Powerful funnel conversion reporting
          • 137
            Customizable reports
          • 83
            Custom events try
          • 53
            Elastic api
          • 13
            Updated regulary
          • 8
            Interactive Documentation
          • 3
            Google play
          • 2
            Advanced ecommerce
          • 2
            Industry Standard
          • 2
            Walkman music video playlist
          • 1
            Medium / Channel data split
          • 1
            Financial Management Challenges -2015h
          • 1
            Lifesaver
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            Easy to integrate
          CONS OF GOOGLE ANALYTICS
          • 9
            Confusing UX/UI
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            Super complex
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            Very hard to build out funnels
          • 3
            Poor web performance metrics
          • 2
            Very easy to confuse the user of the analytics
          • 2
            Time spent on page isn't accurate out of the box

          related Google Analytics posts

          Tassanai Singprom

          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

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          Max Musing
          Founder & CEO at BaseDash · | 8 upvotes · 127.3K views

          Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

          Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

          See more
          Elasticsearch logo

          Elasticsearch

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          PROS OF ELASTICSEARCH
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            Powerful api
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            Great search engine
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            Open source
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            Restful
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            Near real-time search
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            Free
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            Search everything
          • 54
            Easy to get started
          • 45
            Analytics
          • 26
            Distributed
          • 6
            Fast search
          • 5
            More than a search engine
          • 3
            Great docs
          • 3
            Awesome, great tool
          • 3
            Easy to scale
          • 2
            Document Store
          • 2
            Nosql DB
          • 2
            Great piece of software
          • 2
            Great customer support
          • 2
            Intuitive API
          • 2
            Fast
          • 2
            Easy setup
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            Highly Available
          • 1
            Not stable
          • 1
            Scalability
          • 1
            Open
          • 1
            Reliable
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            Github
          • 1
            Elaticsearch
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            Actively developing
          • 1
            Responsive maintainers on GitHub
          • 1
            Ecosystem
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            Easy to get hot data
          • 1
            Potato
          • 0
            Community
          CONS OF ELASTICSEARCH
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            Resource hungry
          • 6
            Diffecult to get started
          • 5
            Expensive
          • 3
            Hard to keep stable at large scale

          related Elasticsearch posts

          Tim Abbott

          We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

          We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

          And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

          I can't recommend it highly enough.

          See more
          Tymoteusz Paul
          Devops guy at X20X Development LTD · | 23 upvotes · 4.7M views

          Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

          It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

          I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

          We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

          If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

          The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

          Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

          See more
          Amazon Athena logo

          Amazon Athena

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          Query S3 Using SQL
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          PROS OF AMAZON ATHENA
          • 15
            Use SQL to analyze CSV files
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            Glue crawlers gives easy Data catalogue
          • 6
            Cheap
          • 5
            Query all my data without running servers 24x7
          • 4
            No data base servers yay
          • 3
            Easy integration with QuickSight
          • 2
            Query and analyse CSV,parquet,json files in sql
          • 2
            Also glue and athena use same data catalog
          • 1
            No configuration required
          • 0
            Ad hoc checks on data made easy
          CONS OF AMAZON ATHENA
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            related Amazon Athena posts

            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.

            See more

            Hi all,

            Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

            See more
            MySQL logo

            MySQL

            85.8K
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              Sql
            • 672
              Free
            • 556
              Easy
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              Widely used
            • 485
              Open source
            • 180
              High availability
            • 160
              Cross-platform support
            • 104
              Great community
            • 78
              Secure
            • 75
              Full-text indexing and searching
            • 25
              Fast, open, available
            • 14
              SSL support
            • 13
              Reliable
            • 13
              Robust
            • 8
              Enterprise Version
            • 7
              Easy to set up on all platforms
            • 2
              NoSQL access to JSON data type
            • 1
              Relational database
            • 1
              Easy, light, scalable
            • 1
              Sequel Pro (best SQL GUI)
            • 1
              Replica Support
            CONS OF MYSQL
            • 14
              Owned by a company with their own agenda
            • 1
              Can't roll back schema changes

            related MySQL posts

            Tim Abbott

            We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

            We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

            And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

            I can't recommend it highly enough.

            See more
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 21 upvotes · 1.1M views

            Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

            The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

            https://eng.uber.com/mysql-migration/

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