What is Incorta and what are its top alternatives?
Incorta is a unified data analytics platform that provides real-time insights for business decision-making. Its key features include data extraction, transformation, and loading (ETL) capabilities, integrated data storage, in-memory analytics, and interactive dashboards. However, some limitations of Incorta include a steep learning curve for new users and limited customization options for advanced users.
- Tableau: Tableau is a powerful data visualization tool that allows users to create interactive and shareable dashboards. Key features include a drag-and-drop interface, real-time data analytics, and seamless integration with various data sources. Pros of Tableau include a user-friendly interface and a large online community for support, while cons include high pricing for advanced features.
- Power BI: Power BI is a business analytics tool by Microsoft that offers data preparation, data visualization, and sharing capabilities. Key features include AI-powered insights, interactive reports, and integration with Microsoft products. Pros of Power BI include seamless integration with Microsoft ecosystem, while cons include limitations in handling large datasets.
- Looker: Looker is a data exploration and analytics platform that provides data visualization, exploration, and sharing capabilities. Key features include data modeling, SQL support, and embedded analytics. Pros of Looker include powerful data modeling capabilities, while cons include a complex pricing model.
- QlikView: QlikView is a business intelligence tool that offers data visualization, reporting, and dashboarding capabilities. Key features include associative data modeling, interactive dashboards, and self-service analytics. Pros of QlikView include in-memory processing for fast data analysis, while cons include a limited set of visualization options.
- Domo: Domo is a cloud-based business intelligence platform that provides data visualization, collaboration, and integration capabilities. Key features include real-time data integration, executive dashboards, and mobile analytics. Pros of Domo include ease of use and collaboration features, while cons include limited customization options.
- Sisense: Sisense is a business intelligence software that offers data preparation, analysis, and visualization capabilities. Key features include data mashup, drag-and-drop interface, and white-labeling options. Pros of Sisense include fast data processing speed, while cons include a limited set of visualization options.
- Yellowfin: Yellowfin is a business intelligence platform that provides data analytics, collaboration, and data governance capabilities. Key features include storytelling with data, AI-driven insights, and embedded BI. Pros of Yellowfin include ease of use and advanced analytics capabilities, while cons include limitations in data connection options.
- MicroStrategy: MicroStrategy is a business intelligence tool that offers data discovery, mobile analytics, and reporting capabilities. Key features include data governance, predictive analytics, and embedded analytics. Pros of MicroStrategy include a comprehensive set of analytics tools, while cons include a complex pricing model.
- GoodData: GoodData is a cloud-based business intelligence platform that provides embedded analytics, data visualization, and insights delivery capabilities. Key features include AI-powered analytics, white-labeling options, and automated insights. Pros of GoodData include scalable analytics solutions, while cons include limitations in customization options.
- Periscope Data: Periscope Data is an analytics platform that offers data visualization, SQL querying, and collaboration capabilities. Key features include SQL editor, drag-and-drop interface, and embedding capabilities. Pros of Periscope Data include a user-friendly interface, while cons include limited advanced analytics features.
Top Alternatives to Incorta
NumPyBesides 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. ...
PandasFlexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...
TableauTableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. ...
SciPyPython-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering. ...
Power BIIt aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. ...
MetabaseIt 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
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. ...
LookerWe've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way. ...
Incorta alternatives & related posts
- Great for data analysis10
- Faster than list4
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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.
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?
Pandas
- Easy data frame management21
- Extensive file format compatibility2
related Pandas posts
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.
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?
- Capable of visualising billions of rows6
- Intuitive and easy to learn1
- Responsive1
- Very expensive for small companies2
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.
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!
related SciPy posts
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- Powerful Calculation Engine2
- Access from anywhere2
- Intuitive and complete internal ETL2
- Database visualisation2
- Azure Based Service1
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.
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.
- Database visualisation62
- Open Source45
- Easy setup41
- Dashboard out of the box36
- Free23
- Simple14
- Support for many dbs9
- Easy embedding7
- Easy6
- It's good6
- AGPL : wont help with adoption but depends on your goal5
- BI doesn't get easier than that5
- Google analytics integration4
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Need to create a dashboard with a variety of charts having a good drill-down feature with good UI/UX and easy to manage users and roles with some authentication. I am confused between Superset and Metabase, so please suggest.
- Database visualisation62
- Open Source45
- Easy setup41
- Dashboard out of the box36
- Free23
- Simple14
- Support for many dbs9
- Easy embedding7
- Easy6
- It's good6
- AGPL : wont help with adoption but depends on your goal5
- BI doesn't get easier than that5
- Google analytics integration4
- Multiple integrations4
- Easy set up4
- Harder to setup than similar tools7
related Metabase posts
Need to create a dashboard with a variety of charts having a good drill-down feature with good UI/UX and easy to manage users and roles with some authentication. I am confused between Superset and Metabase, so please suggest.
- Real time in app customer chat support4
- GitHub integration4
- Reduces the barrier of entry to utilizing data1
- Price3
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We are a consumer mobile app IOS/Android startup. The app is instrumented with branch and Firebase. We use Google BigQuery. We are looking at tools that can support engagement and cohort analysis at an early stage price which we can grow with. Data Studio is the default but it would seem Looker provides more power. We don't have much insight into Amplitude other than the fact it is a popular PM tool. Please provide some insight.