Alternatives to Dataform logo

Alternatives to Dataform

dbt, Slick, NumPy, Pandas, and Tableau are the most popular alternatives and competitors to Dataform.
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What is Dataform and what are its top alternatives?

Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.
Dataform is a tool in the Business Intelligence category of a tech stack.
Dataform is an open source tool with 603 GitHub stars and 104 GitHub forks. Here’s a link to Dataform's open source repository on GitHub

Top Alternatives to Dataform

  • dbt
    dbt

    dbt is a transformation workflow that lets teams deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines. ...

  • Slick
    Slick

    It is a modern database query and access library for Scala. It allows you to work with stored data almost as if you were using Scala collections while at the same time giving you full control over when a database access happens and which data is transferred. ...

  • NumPy
    NumPy

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...

  • Pandas
    Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...

  • 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. ...

  • Metabase
    Metabase

    It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating. ...

  • Metabase
    Metabase

    It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating. ...

  • 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. ...

Dataform alternatives & related posts

dbt logo

dbt

349
344
13
dbt helps data teams work like software engineers—to ship trusted data, faster.
349
344
+ 1
13
PROS OF DBT
  • 3
    Easy for SQL programmers to learn
  • 2
    CI/CD
  • 2
    Schedule Jobs
  • 2
    Reusable Macro
  • 2
    Faster Integrated Testing
  • 2
    Modularity, portability, CI/CD, and documentation
CONS OF DBT
  • 1
    Only limited to SQL
  • 1
    Cant do complex iterations , list comprehensions etc .
  • 1
    People will have have only sql skill set at the end
  • 1
    Very bad for people from learning perspective

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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
Slick logo

Slick

9.2K
1.2K
0
Database query and access library for Scala
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1.2K
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0
PROS OF SLICK
    Be the first to leave a pro
    CONS OF SLICK
      Be the first to leave a con

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      NumPy logo

      NumPy

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      Fundamental package for scientific computing with Python
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      PROS OF NUMPY
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        Great for data analysis
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        Faster than list
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        Server side

        We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

        • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

        • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

        • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

        Client side

        • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

        • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

        • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

        Cache

        • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

        Database

        • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

        Infrastructure

        • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

        Other Tools

        • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

        • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

        See more

        Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?

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        Pandas logo

        Pandas

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        High-performance, easy-to-use data structures and data analysis tools for the Python programming language
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        PROS OF PANDAS
        • 21
          Easy data frame management
        • 1
          Extensive file format compatibility
        CONS OF PANDAS
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          Server side

          We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

          • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

          • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

          • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

          Client side

          • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

          • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

          • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

          Cache

          • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

          Database

          • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

          Infrastructure

          • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

          Other Tools

          • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

          • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

          See more

          Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?

          See more
          Tableau logo

          Tableau

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          Tableau helps people see and understand data.
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          PROS OF TABLEAU
          • 5
            Capable of visualising billions of rows
          • 1
            Intuitive and easy to learn
          • 1
            Responsive
          CONS OF TABLEAU
          • 1
            Very expensive for small companies

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          Metabase logo

          Metabase

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          An open-source business intelligence tool
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          PROS OF METABASE
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            Database visualisation
          • 42
            Open Source
          • 40
            Easy setup
          • 35
            Dashboard out of the box
          • 20
            Free
          • 14
            Simple
          • 8
            Support for many dbs
          • 7
            Easy embedding
          • 6
            Easy
          • 6
            It's good
          • 5
            AGPL : wont help with adoption but depends on your goal
          • 5
            BI doesn't get easier than that
          • 4
            Multiple integrations
          • 4
            Google analytics integration
          • 3
            Easy set up
          CONS OF METABASE
          • 5
            Harder to setup than similar tools

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          Metabase logo

          Metabase

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          1.1K
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          An open-source business intelligence tool
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          PROS OF METABASE
          • 57
            Database visualisation
          • 42
            Open Source
          • 40
            Easy setup
          • 35
            Dashboard out of the box
          • 20
            Free
          • 14
            Simple
          • 8
            Support for many dbs
          • 7
            Easy embedding
          • 6
            Easy
          • 6
            It's good
          • 5
            AGPL : wont help with adoption but depends on your goal
          • 5
            BI doesn't get easier than that
          • 4
            Multiple integrations
          • 4
            Google analytics integration
          • 3
            Easy set up
          CONS OF METABASE
          • 5
            Harder to setup than similar tools

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          Power BI logo

          Power BI

          767
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          Empower team members to discover insights hidden in your data
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          PROS OF POWER BI
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            Cross-filtering
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            Database visualisation
          • 1
            Intuitive and complete internal ETL
          • 1
            Access from anywhere
          • 1
            Azure Based Service
          • 1
            Powerful Calculation Engine
          CONS OF POWER BI
            Be the first to leave a con

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            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.

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