Alternatives to Trino logo

Alternatives to Trino

GraphQL, SQL, Apache Spark, Prisma, and Splunk are the most popular alternatives and competitors to Trino.
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What is Trino and what are its top alternatives?

It is a fast distributed SQL query engine for big data analytics that helps you explore your data universe. It is designed to query large data sets distributed over one or more heterogeneous data sources.
Trino is a tool in the Query Languages category of a tech stack.
Trino is an open source tool with 8.5K GitHub stars and 2.5K GitHub forks. Here鈥檚 a link to Trino's open source repository on GitHub

Top Alternatives to Trino

  • GraphQL
    GraphQL

    GraphQL is a data query language and runtime designed and used at Facebook to request and deliver data to mobile and web apps since 2012. ...

  • Apache Spark
    Apache Spark

    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. ...

  • SQL
    SQL

    SQL is designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). ...

  • Prisma
    Prisma

    Prisma is an open-source database toolkit. It replaces traditional ORMs and makes database access easy with an auto-generated query builder for TypeScript & Node.js. ...

  • Oracle PL/SQL
    Oracle PL/SQL

    It is a powerful, yet straightforward database programming language. It is easy to both write and read, and comes packed with lots of out-of-the-box optimizations and security features. ...

  • Oracle PL/SQL
    Oracle PL/SQL

    It is a powerful, yet straightforward database programming language. It is easy to both write and read, and comes packed with lots of out-of-the-box optimizations and security features. ...

  • Splunk
    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • Apache Flink
    Apache Flink

    Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala. ...

Trino alternatives & related posts

GraphQL logo

GraphQL

31.5K
26.1K
310
A data query language and runtime
31.5K
26.1K
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310
PROS OF GRAPHQL
  • 75
    Schemas defined by the requests made by the user
  • 63
    Will replace RESTful interfaces
  • 62
    The future of API's
  • 49
    The future of databases
  • 13
    Self-documenting
  • 12
    Get many resources in a single request
  • 6
    Query Language
  • 6
    Ask for what you need, get exactly that
  • 3
    Fetch different resources in one request
  • 3
    Type system
  • 3
    Evolve your API without versions
  • 2
    Ease of client creation
  • 2
    GraphiQL
  • 2
    Easy setup
  • 1
    "Open" document
  • 1
    Fast prototyping
  • 1
    Supports subscription
  • 1
    Standard
  • 1
    Good for apps that query at build time. (SSR/Gatsby)
  • 1
    1. Describe your data
  • 1
    Better versioning
  • 1
    Backed by Facebook
  • 1
    Easy to learn
CONS OF GRAPHQL
  • 4
    Hard to migrate from GraphQL to another technology
  • 4
    More code to type.
  • 2
    Takes longer to build compared to schemaless.
  • 1
    No support for caching
  • 1
    All the pros sound like NFT pitches
  • 1
    No support for streaming
  • 1
    Works just like any other API at runtime
  • 1
    N+1 fetch problem
  • 1
    No built in security

related GraphQL posts

Shared insights
on
Node.jsNode.jsGraphQLGraphQLMongoDBMongoDB

I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery

For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:

  1. Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have

  2. GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.

  3. MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website

See more
Nick Rockwell
SVP, Engineering at Fastly | 44 upvotes 路 2.6M views

When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

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Apache Spark logo

Apache Spark

2.9K
3.4K
139
Fast and general engine for large-scale data processing
2.9K
3.4K
+ 1
139
PROS OF APACHE SPARK
  • 60
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    In memory Computation
  • 2
    Machine learning libratimery, Streaming in real
CONS OF APACHE SPARK
  • 4
    Speed

related Apache Spark posts

Eric Colson
Chief Algorithms Officer at Stitch Fix | 21 upvotes 路 5.3M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 7 upvotes 路 2.6M 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 )

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SQL logo

SQL

1.7K
92
0
It is a domain-specific language used in programming
1.7K
92
+ 1
0
PROS OF SQL
    Be the first to leave a pro
    CONS OF SQL
      Be the first to leave a con

      related SQL posts

      Prisma logo

      Prisma

      832
      878
      54
      Modern Database Access for TypeScript & Node.js
      832
      878
      + 1
      54
      PROS OF PRISMA
      • 12
        Type-safe database access
      • 10
        Open Source
      • 8
        Auto-generated query builder
      • 6
        Supports multible database systems
      • 6
        Increases confidence during development
      • 4
        Built specifically for Postgres and TypeScript
      • 4
        Productive application development
      • 2
        Supports multible RDBMSs
      • 2
        Robust migrations system
      CONS OF PRISMA
      • 2
        Doesn't support downward/back migrations
      • 1
        Doesn't support JSONB
      • 1
        Do not support JSONB
      • 1
        Mutation of JSON is really confusing
      • 1
        Do not support JSONB

      related Prisma posts

      Divine Bawa
      at PayHub Ghana Limited | 16 upvotes 路 385.3K views

      I just finished a web app meant for a business that offers training programs for certain professional courses. I chose this stack to test out my skills in graphql and react. I used Node.js , GraphQL , MySQL for the #Backend utilizing Prisma as a database interface for MySQL to provide CRUD APIs and graphql-yoga as a server. For the #frontend I chose React, styled-components for styling, Next.js for routing and SSR and Apollo for data management. I really liked the outcome and I will definitely use this stack in future projects.

      See more
      Munkhtegsh Munkhbat
      Software Engineer Consultant at LoanSnap | 9 upvotes 路 216.7K views

      In my last side project, I built a web posting application that has similar features as Facebook and hosted on Heroku. The user can register an account, create posts, upload images and share with others. I took an advantage of graphql-subscriptions to handle realtime notifications in the comments section. Currently, I'm at the last stage of styling and building layouts.

      For the #Backend I used graphql-yoga, Prisma, GraphQL with PostgreSQL database. For the #FrontEnd: React, styled-components with Apollo. The app is hosted on Heroku.

      See more
      Oracle PL/SQL logo

      Oracle PL/SQL

      708
      559
      8
      It is a combination of SQL along with the procedural features of programming languages
      708
      559
      + 1
      8
      PROS OF ORACLE PL/SQL
      • 2
        Multiple ways to accomplish the same end
      • 2
        Powerful
      • 1
        Not mysql
      • 1
        Massive, continuous investment by Oracle Corp
      • 1
        Extensible to external langiages
      • 1
        Pl/sql
      CONS OF ORACLE PL/SQL
      • 2
        High commercial license cost

      related Oracle PL/SQL posts

      Oracle PL/SQL logo

      Oracle PL/SQL

      708
      559
      8
      It is a combination of SQL along with the procedural features of programming languages
      708
      559
      + 1
      8
      PROS OF ORACLE PL/SQL
      • 2
        Multiple ways to accomplish the same end
      • 2
        Powerful
      • 1
        Not mysql
      • 1
        Massive, continuous investment by Oracle Corp
      • 1
        Extensible to external langiages
      • 1
        Pl/sql
      CONS OF ORACLE PL/SQL
      • 2
        High commercial license cost

      related Oracle PL/SQL posts

      Splunk logo

      Splunk

      572
      958
      14
      Search, monitor, analyze and visualize machine data
      572
      958
      + 1
      14
      PROS OF SPLUNK
      • 2
        Ability to style search results into reports
      • 2
        Alert system based on custom query results
      • 2
        API for searching logs, running reports
      • 2
        Query engine supports joining, aggregation, stats, etc
      • 1
        Query any log as key-value pairs
      • 1
        Splunk language supports string, date manip, math, etc
      • 1
        Granular scheduling and time window support
      • 1
        Custom log parsing as well as automatic parsing
      • 1
        Dashboarding on any log contents
      • 1
        Rich GUI for searching live logs
      CONS OF SPLUNK
      • 1
        Splunk query language rich so lots to learn

      related Splunk posts

      Shared insights
      on
      KibanaKibanaSplunkSplunkGrafanaGrafana

      I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

      See more
      Shared insights
      on
      SplunkSplunkElasticsearchElasticsearch

      We are currently exploring Elasticsearch and Splunk for our centralized logging solution. I need some feedback about these two tools. We expect our logs in the range of upwards > of 10TB of logging data.

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      Apache Flink logo

      Apache Flink

      503
      828
      38
      Fast and reliable large-scale data processing engine
      503
      828
      + 1
      38
      PROS OF APACHE FLINK
      • 16
        Unified batch and stream processing
      • 8
        Easy to use streaming apis
      • 8
        Out-of-the box connector to kinesis,s3,hdfs
      • 4
        Open Source
      • 2
        Low latency
      CONS OF APACHE FLINK
        Be the first to leave a con

        related Apache Flink posts

        Surabhi Bhawsar
        Technical Architect at Pepcus | 7 upvotes 路 686.2K views
        Shared insights
        on
        KafkaKafkaApache FlinkApache Flink

        I need to build the Alert & Notification framework with the use of a scheduled program. We will analyze the events from the database table and filter events that are falling under a day timespan and send these event messages over email. Currently, we are using Kafka Pub/Sub for messaging. The customer wants us to move on Apache Flink, I am trying to understand how Apache Flink could be fit better for us.

        See more

        I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?

        See more