Alternatives to Trifacta logo

Alternatives to Trifacta

Tableau, OpenRefine, Talend, Power BI, and MySQL are the most popular alternatives and competitors to Trifacta.
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What is Trifacta and what are its top alternatives?

It is an Intelligent Platform that Interoperates with Your Data Investments. It sits between the data storage and processing environments and the visualization, statistical or machine learning tools used downstream
Trifacta is a tool in the Big Data Tools category of a tech stack.
Trifacta is an open source tool with GitHub stars and GitHub forks. Here’s a link to Trifacta's open source repository on GitHub

Top Alternatives to Trifacta

  • Tableau
    Tableau

    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. ...

  • OpenRefine
    OpenRefine

    It is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data. ...

  • Talend
    Talend

    It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms. ...

  • Power BI
    Power BI

    It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. ...

  • 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. ...

  • PostgreSQL
    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

  • MongoDB
    MongoDB

    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. ...

  • Redis
    Redis

    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. ...

Trifacta alternatives & related posts

Tableau logo

Tableau

1.3K
8
Tableau helps people see and understand data.
1.3K
8
PROS OF TABLEAU
  • 6
    Capable of visualising billions of rows
  • 1
    Intuitive and easy to learn
  • 1
    Responsive
CONS OF TABLEAU
  • 3
    Very expensive for small companies

related Tableau posts

Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

See more
Shared insights
on
TableauTableauQlikQlikPowerBIPowerBI

Hello everyone,

My team and I are currently in the process of selecting a Business Intelligence (BI) tool for our actively developing company, which has over 500 employees. We are considering open-source options.

We are keen to connect with a Head of Analytics or BI Analytics professional who has extensive experience working with any of these systems and is willing to share their insights. Ideally, we would like to speak with someone from companies that have transitioned from proprietary BI tools (such as PowerBI, Qlik, or Tableau) to open-source BI tools, or vice versa.

If you have any contacts or recommendations for individuals we could reach out to regarding this matter, we would greatly appreciate it. Additionally, if you are personally willing to share your experiences, please feel free to reach out to me directly. Thank you!

See more
OpenRefine logo

OpenRefine

33
0
Desktop application for data cleanup and transformation
33
0
PROS OF OPENREFINE
    Be the first to leave a pro
    CONS OF OPENREFINE
      Be the first to leave a con

      related OpenRefine posts

      Jan Vlnas
      Senior Software Engineer at Mews · | 5 upvotes · 456.8K views

      From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

      I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

      Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

      See more
      Shehryar Mallick
      Associate Data Engineer at Virtuosoft · | 5 upvotes · 22.7K views

      I've been going over the documentation and couldn't find answers to different questions like:

      Apache Hive is built on top of Hadoop meaning if I wanted to scale it up I could do either horizontal scaling or vertical scaling. but if I want to scale up openrefine to cater more data then how can this be achieved? the only thing I could find was to allocate more memory like 2 of 4GB but using this approach would mean that we would run out of memory to allot. so thoughts on this?

      Secondly, Hadoop has MapReduce meaning a task is reduced to many mapper running in parallel to perform the task which in turn increase the processing speed, is there a similar mechanism in OpenRefine or does it only have a single processing unit (as it is running locally). thoughts?

      See more
      Talend logo

      Talend

      152
      0
      A single, unified suite for all integration needs
      152
      0
      PROS OF TALEND
        Be the first to leave a pro
        CONS OF TALEND
          Be the first to leave a con

          related Talend posts

          Power BI logo

          Power BI

          923
          27
          Empower team members to discover insights hidden in your data
          923
          27
          PROS OF POWER BI
          • 18
            Cross-filtering
          • 2
            Database visualisation
          • 2
            Powerful Calculation Engine
          • 2
            Access from anywhere
          • 2
            Intuitive and complete internal ETL
          • 1
            Azure Based Service
          CONS OF POWER BI
            Be the first to leave a con

            related Power BI posts

            Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

            See more

            Which among the two, Kyvos and Azure Analysis Services, should be used to build a Semantic Layer?

            I have to build a Semantic Layer for the data warehouse platform and use Power BI for visualisation and the data lies in the Azure Managed Instance. I need to analyse the two platforms and find which suits best for the same.

            See more
            MySQL logo

            MySQL

            125.6K
            3.8K
            The world's most popular open source database
            125.6K
            3.8K
            PROS OF MYSQL
            • 800
              Sql
            • 679
              Free
            • 562
              Easy
            • 528
              Widely used
            • 490
              Open source
            • 180
              High availability
            • 160
              Cross-platform support
            • 104
              Great community
            • 79
              Secure
            • 75
              Full-text indexing and searching
            • 26
              Fast, open, available
            • 16
              Reliable
            • 16
              SSL support
            • 15
              Robust
            • 9
              Enterprise Version
            • 7
              Easy to set up on all platforms
            • 3
              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
            • 16
              Owned by a company with their own agenda
            • 3
              Can't roll back schema changes

            related MySQL posts

            Nick Rockwell
            SVP, Engineering at Fastly · | 46 upvotes · 4.1M 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.

            See more
            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
            PostgreSQL logo

            PostgreSQL

            98.4K
            3.5K
            A powerful, open source object-relational database system
            98.4K
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            PROS OF POSTGRESQL
            • 764
              Relational database
            • 510
              High availability
            • 439
              Enterprise class database
            • 383
              Sql
            • 304
              Sql + nosql
            • 173
              Great community
            • 147
              Easy to setup
            • 131
              Heroku
            • 130
              Secure by default
            • 113
              Postgis
            • 50
              Supports Key-Value
            • 48
              Great JSON support
            • 34
              Cross platform
            • 33
              Extensible
            • 28
              Replication
            • 26
              Triggers
            • 23
              Multiversion concurrency control
            • 23
              Rollback
            • 21
              Open source
            • 18
              Heroku Add-on
            • 17
              Stable, Simple and Good Performance
            • 15
              Powerful
            • 13
              Lets be serious, what other SQL DB would you go for?
            • 11
              Good documentation
            • 9
              Scalable
            • 8
              Free
            • 8
              Reliable
            • 8
              Intelligent optimizer
            • 7
              Transactional DDL
            • 7
              Modern
            • 6
              One stop solution for all things sql no matter the os
            • 5
              Relational database with MVCC
            • 5
              Faster Development
            • 4
              Full-Text Search
            • 4
              Developer friendly
            • 3
              Excellent source code
            • 3
              Free version
            • 3
              Great DB for Transactional system or Application
            • 3
              Relational datanbase
            • 3
              search
            • 3
              Open-source
            • 2
              Text
            • 2
              Full-text
            • 1
              Can handle up to petabytes worth of size
            • 1
              Composability
            • 1
              Multiple procedural languages supported
            • 0
              Native
            CONS OF POSTGRESQL
            • 10
              Table/index bloatings

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            Simon Reymann
            Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11.6M views

            Our whole DevOps stack consists of the following tools:

            • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
            • Respectively Git as revision control system
            • SourceTree as Git GUI
            • Visual Studio Code as IDE
            • CircleCI for continuous integration (automatize development process)
            • Prettier / TSLint / ESLint as code linter
            • SonarQube as quality gate
            • Docker as container management (incl. Docker Compose for multi-container application management)
            • VirtualBox for operating system simulation tests
            • Kubernetes as cluster management for docker containers
            • Heroku for deploying in test environments
            • nginx as web server (preferably used as facade server in production environment)
            • SSLMate (using OpenSSL) for certificate management
            • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
            • PostgreSQL as preferred database system
            • Redis as preferred in-memory database/store (great for caching)

            The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

            • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
            • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
            • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
            • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
            • Scalability: All-in-one framework for distributed systems.
            • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
            See more
            Jeyabalaji Subramanian

            Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

            We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

            Based on the above criteria, we selected the following tools to perform the end to end data replication:

            We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

            We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

            In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

            Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

            In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

            See more
            MongoDB logo

            MongoDB

            93.7K
            4.1K
            The database for giant ideas
            93.7K
            4.1K
            PROS OF MONGODB
            • 828
              Document-oriented storage
            • 593
              No sql
            • 553
              Ease of use
            • 464
              Fast
            • 410
              High performance
            • 255
              Free
            • 218
              Open source
            • 180
              Flexible
            • 145
              Replication & high availability
            • 112
              Easy to maintain
            • 42
              Querying
            • 39
              Easy scalability
            • 38
              Auto-sharding
            • 37
              High availability
            • 31
              Map/reduce
            • 27
              Document database
            • 25
              Easy setup
            • 25
              Full index support
            • 16
              Reliable
            • 15
              Fast in-place updates
            • 14
              Agile programming, flexible, fast
            • 12
              No database migrations
            • 8
              Easy integration with Node.Js
            • 8
              Enterprise
            • 6
              Enterprise Support
            • 5
              Great NoSQL DB
            • 4
              Support for many languages through different drivers
            • 3
              Schemaless
            • 3
              Aggregation Framework
            • 3
              Drivers support is good
            • 2
              Fast
            • 2
              Managed service
            • 2
              Easy to Scale
            • 2
              Awesome
            • 2
              Consistent
            • 1
              Good GUI
            • 1
              Acid Compliant
            CONS OF MONGODB
            • 6
              Very slowly for connected models that require joins
            • 3
              Not acid compliant
            • 2
              Proprietary query language

            related MongoDB posts

            Jeyabalaji Subramanian

            Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

            We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

            Based on the above criteria, we selected the following tools to perform the end to end data replication:

            We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

            We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

            In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

            Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

            In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

            See more
            Robert Zuber

            We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

            As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

            When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

            See more
            Redis logo

            Redis

            59.6K
            3.9K
            Open source (BSD licensed), in-memory data structure store
            59.6K
            3.9K
            PROS OF REDIS
            • 886
              Performance
            • 542
              Super fast
            • 513
              Ease of use
            • 444
              In-memory cache
            • 324
              Advanced key-value cache
            • 194
              Open source
            • 182
              Easy to deploy
            • 164
              Stable
            • 155
              Free
            • 121
              Fast
            • 42
              High-Performance
            • 40
              High Availability
            • 35
              Data Structures
            • 32
              Very Scalable
            • 24
              Replication
            • 22
              Great community
            • 22
              Pub/Sub
            • 19
              "NoSQL" key-value data store
            • 16
              Hashes
            • 13
              Sets
            • 11
              Sorted Sets
            • 10
              NoSQL
            • 10
              Lists
            • 9
              Async replication
            • 9
              BSD licensed
            • 8
              Bitmaps
            • 8
              Integrates super easy with Sidekiq for Rails background
            • 7
              Keys with a limited time-to-live
            • 7
              Open Source
            • 6
              Lua scripting
            • 6
              Strings
            • 5
              Awesomeness for Free
            • 5
              Hyperloglogs
            • 4
              Transactions
            • 4
              Outstanding performance
            • 4
              Runs server side LUA
            • 4
              LRU eviction of keys
            • 4
              Feature Rich
            • 4
              Written in ANSI C
            • 4
              Networked
            • 3
              Data structure server
            • 3
              Performance & ease of use
            • 2
              Dont save data if no subscribers are found
            • 2
              Automatic failover
            • 2
              Easy to use
            • 2
              Temporarily kept on disk
            • 2
              Scalable
            • 2
              Existing Laravel Integration
            • 2
              Channels concept
            • 2
              Object [key/value] size each 500 MB
            • 2
              Simple
            CONS OF REDIS
            • 15
              Cannot query objects directly
            • 3
              No secondary indexes for non-numeric data types
            • 1
              No WAL

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            Russel Werner
            Lead Engineer at StackShare · | 32 upvotes · 2.8M views

            StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

            Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

            #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

            See more
            Simon Reymann
            Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11.6M views

            Our whole DevOps stack consists of the following tools:

            • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
            • Respectively Git as revision control system
            • SourceTree as Git GUI
            • Visual Studio Code as IDE
            • CircleCI for continuous integration (automatize development process)
            • Prettier / TSLint / ESLint as code linter
            • SonarQube as quality gate
            • Docker as container management (incl. Docker Compose for multi-container application management)
            • VirtualBox for operating system simulation tests
            • Kubernetes as cluster management for docker containers
            • Heroku for deploying in test environments
            • nginx as web server (preferably used as facade server in production environment)
            • SSLMate (using OpenSSL) for certificate management
            • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
            • PostgreSQL as preferred database system
            • Redis as preferred in-memory database/store (great for caching)

            The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

            • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
            • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
            • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
            • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
            • Scalability: All-in-one framework for distributed systems.
            • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
            See more