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There are a few things to consider while choosing the right data science platform to handle the gathering, scrubbing, transformation, and application of data. When it comes to fast-growing startups, cost and time efficiency are two of the main ones.
If you’re managing a considerably large set of data for your application, you’d often need a way to efficiently process, analyze and present the data generated from multiple sources. In this case, having one place where you can centralize your workflow becomes very important.
Data-driven companies would simply never mature to the full potential we can today without powerful data science platforms backing it up behind the scenes. They influence the entire process from data ingestion to trends prediction, dashboards creation, analytics reports and many other use cases.
There are many options available in the market if you’re deciding which data science platform to build your application on, so which one should you choose? What is the difference between a good and a great data science platform? Let’s analyze a few of them to answer this question.
The Modern Data Science Platform
A data science platform enables the gathering, structuring, transformation, and control of your data by working also as a security layer for users and applications. It is crucial for you to be able to process multiple types and sizes of data with it, such as massive scale structured, semi-structured, or unstructured data. In other words, it has to be flexible.
To develop complex Machine Learning applications, as many applications require, you need as much flexibility you can get.
But only having data flexibility by itself does not often mean that you’re using a future-proof option for your data product. It’s important to also look for a platform where you’re not locked into any type of framework, cloud provider, programming language or any other service that can become obsolete in a few years.
Another thing to keep in mind when doing platform research for your project, that can save you a lot of future headaches, is making sure they have good customer support and product reliability.
A good data science platform is a place where you can scale and unify your workflow, helping you to coordinate, transform and provide data to an end-user.
A great data science platform is the place where you can do all of this while also running it wherever you want (cloud, multi-cloud or on premise), integrating it with any tool, framework or service available and bonus points for customer support and doing it cost and time efficiently.
The 5 Best Data Science Platforms for Startups in 2023
Now that you have an idea of what to look for in a data science platform, we made a list of the 5 best ones out there and how they fit for some particular use cases. This comparison will cover important traits like flexibility, pricing, customer support, tools, user experience, and enhanced APIs.
1. Shakudo
What is Shakudo?
Shakudo is a Cloud data science platform known for offering extremely high flexibility when compared to other competitors, along with providing not only fair pricing but also the best time efficiency you can get. Beneficial to any company looking to scale, but especially essential to Startups, since with the Shakudo Platform, a small team can achieve valuable results in half the time and save money with its optimized infrastructure.
Launch a cloud development environment in seconds, scale data workloads, run hundreds of parallel pipelines, deploy to production, monitor and never worry about maintenance. Shakudo’s great product and customer support are the reasons the company has an incredible 100% customer retention.
But what makes Shakudo get the first place in our list is because it doesn’t lock the user into any infrastructure, tool or necessary way to develop your application. The team can also choose to build the whole application inside the platform, not having to use any other services to develop or deploy the application.
The current customer base range from small startups to unicorns such as Quantum Metric, Risk Thinking AI, Manifest Climate, KIN and Empowered.
Shakudo quick facts:
🟢Startup friendly
🟢Flexibility
🟢No maintenance
🟢Cost efficiency
🟢Option to run on-premises
🟢Multi-cloud support
2. Domino Data Labs
What is Domino Data Lab?
Domino Data Lab is a scalable MLOps Platform that increases team productivity by combining data frameworks, tools, and programming languages together for each industry use case. Among the company offerings are processes to explore data, train machine learning models, validate, deploy, and monitor them.
The automation of repetitive tasks and ease to switch between development environments and its integrated project management features makes them a great choice for big teams to stay organized and efficient.
The platform also doesn’t lock you in to any tools and makes it easy for you to scale computing. Although if you’re still on a small team some of the platform advantages may not work so well since it requires many people to operate some complex tasks and it’s very Data Science focused.
But with that being said it has one of the best Workspace IDE varieties you can get one click away, making it very flexible and open in that sense. If you’re a Data Scientist working in an Enterprise level company that’s definitely a choice you should look into.
The current customer base includes Admiral Group, Cox Automotive, gsk, DBRS and Bayer.
Domino Data Lab quick facts:
🔴Startup friendly
🟢Flexibility
🔴No maintenance
🟠Cost efficiency
🟢Option to run on-premises
🟢Multi-cloud support
🔴Blockchain support
🟢14 day free-trial
3. Databricks
What is Databricks?
Databricks is considered one of the largest DataOps providers we have today. The company is known for its Lakehouse platform, combining features of a data warehouse and data lake to eliminate siloing, now used by hundreds of companies.
It helps its customers unify their analytics across the business, data science, and data engineering, and provides tools for data engineering and business teams to build data products.
With it you get fully managed Spark clusters, an interactive workspace for exploration and visualization, a production pipeline scheduler, and a platform for powering Spark-based applications.
Although its many great features and functionalities, Databricks is still a platform hard to use and get started, making it a good option for enterprise companies but a not so good one when it comes to Startups looking for high efficiency. If you’re a Startup looking for a similar offering with Spark clusters, some other providers like Shakudo or Domino Data Labs may be a better choice.
The current customer base include AT&T, Shell, Scribd, Amgen and Comcast.
Databricks quick facts:
🔴Startup friendly
🟠Flexibility
🟠No maintenance
🔴Cost efficiency
🔴Option to run on-premises
🟢Multi-cloud support
🔴Blockchain support
🟢14 day free-trial
4. Dataiku
What is Dataiku?
Dataiku is a data science and machine learning platform made to facilitate the creation of projects involving artificial intelligence and giving analytics insights. Dataiku makes it easy for businesses to scale to enterprise AI providing a controlled and prepared environment to retail, financial, pharmaceutical, and manufacturing industries primarily.
As an easy to learn and not so expensive solution, Dataiku is also a good choice for medium sized teams getting started with their Data Science application. It supports code and low-code development, but you get more flexibility by having a technical team of data scientists, engineers, and analysts working on it.
Their on-boarding process and community can be a great way to get started with this tool and if you’re a Startup, this can be a company that may fit well if you’re only looking for a data management tool only.
The current customer base include Sephora, Cisco, Zurich, Ubisoft, Merck and Accor.
Dataiku quick facts:
🔴Startupfriendly
🟠Flexibility
🟠No maintenance
🟢Cost efficiency
🟢Option to run on-premises
🔴Multi-cloud support
🔴Blockchain support
🟢14 day free-trial
5. Iguazio
What is Iguazio used for?
Iguazio is a MLOps Platform that allows the team to scale development, deploy and manage their workflow in one place. It assists your entire Machine Learning development process, also allowing you to deploy your application anywhere you want, whether it’s on cloud, multi-cloud or on-premises.
The hardest part of the Machine Learning development process is maintaining and getting models to production. Iguazio makes it a lot easier to get ML/AI projects deployed without the need of managing infrastructure.
Like Domino Labs, the focus of Iguazio lies on traditional Data Science use cases, so you might need some Data Scientists to get full advantage of it and its tools, although not ideal if you’re building a computer vision or deep learning specific application
But overall that's also a great option for Startups wanting to get to production quickly and relatively easy, especially if their project fits a traditional Data Science use case.
The current customer base includes Ecolab, Sheba, NetApp, Payoneer and Latam.
Iguazio quick facts:
🟠Startup friendly
🟠Flexibility
🟢No maintenance
🟠Cost efficiency
🟢Option to run on-premises
🟢Multi-cloud support
🔴Blockchain support
🟢30 day free-trial
The best data science platform for startups in 2023- Summary
Based on the total number of increased team efficiency, scalability, reliability, flexibility, and customer support, Shakudo has the best data science platform for Startups or small to medium sized teams. It allows you not only to process large amounts of data and handle jobs efficiently, but also provides an end-to-end environment for a whole application to be built without spending time or money integrating it with any other service. To start building your data application, reduce your infrastructure cost, increase your team efficiency or get access to blockchain data, Sign up for a Demo of Shakudo Today.
Sabrina Aquino
Sabrina is a creative Software Developer who has managed to create a huge community by sharing her personal experiences with technologies and products. A great problem solver, an above average Age of Empires II player and a mediocre Linux user. Sabrina is currently an undergraduate in Computer Engineering at UFRN (Federal University of Rio Grande do Norte).
There are a few things to consider while choosing the right data science platform to handle the gathering, scrubbing, transformation, and application of data. When it comes to fast-growing startups, cost and time efficiency are two of the main ones.
If you’re managing a considerably large set of data for your application, you’d often need a way to efficiently process, analyze and present the data generated from multiple sources. In this case, having one place where you can centralize your workflow becomes very important.
Data-driven companies would simply never mature to the full potential we can today without powerful data science platforms backing it up behind the scenes. They influence the entire process from data ingestion to trends prediction, dashboards creation, analytics reports and many other use cases.
There are many options available in the market if you’re deciding which data science platform to build your application on, so which one should you choose? What is the difference between a good and a great data science platform? Let’s analyze a few of them to answer this question.
The Modern Data Science Platform
A data science platform enables the gathering, structuring, transformation, and control of your data by working also as a security layer for users and applications. It is crucial for you to be able to process multiple types and sizes of data with it, such as massive scale structured, semi-structured, or unstructured data. In other words, it has to be flexible.
To develop complex Machine Learning applications, as many applications require, you need as much flexibility you can get.
But only having data flexibility by itself does not often mean that you’re using a future-proof option for your data product. It’s important to also look for a platform where you’re not locked into any type of framework, cloud provider, programming language or any other service that can become obsolete in a few years.
Another thing to keep in mind when doing platform research for your project, that can save you a lot of future headaches, is making sure they have good customer support and product reliability.
A good data science platform is a place where you can scale and unify your workflow, helping you to coordinate, transform and provide data to an end-user.
A great data science platform is the place where you can do all of this while also running it wherever you want (cloud, multi-cloud or on premise), integrating it with any tool, framework or service available and bonus points for customer support and doing it cost and time efficiently.
The 5 Best Data Science Platforms for Startups in 2023
Now that you have an idea of what to look for in a data science platform, we made a list of the 5 best ones out there and how they fit for some particular use cases. This comparison will cover important traits like flexibility, pricing, customer support, tools, user experience, and enhanced APIs.
1. Shakudo
What is Shakudo?
Shakudo is a Cloud data science platform known for offering extremely high flexibility when compared to other competitors, along with providing not only fair pricing but also the best time efficiency you can get. Beneficial to any company looking to scale, but especially essential to Startups, since with the Shakudo Platform, a small team can achieve valuable results in half the time and save money with its optimized infrastructure.
Launch a cloud development environment in seconds, scale data workloads, run hundreds of parallel pipelines, deploy to production, monitor and never worry about maintenance. Shakudo’s great product and customer support are the reasons the company has an incredible 100% customer retention.
But what makes Shakudo get the first place in our list is because it doesn’t lock the user into any infrastructure, tool or necessary way to develop your application. The team can also choose to build the whole application inside the platform, not having to use any other services to develop or deploy the application.
The current customer base range from small startups to unicorns such as Quantum Metric, Risk Thinking AI, Manifest Climate, KIN and Empowered.
Shakudo quick facts:
🟢Startup friendly
🟢Flexibility
🟢No maintenance
🟢Cost efficiency
🟢Option to run on-premises
🟢Multi-cloud support
2. Domino Data Labs
What is Domino Data Lab?
Domino Data Lab is a scalable MLOps Platform that increases team productivity by combining data frameworks, tools, and programming languages together for each industry use case. Among the company offerings are processes to explore data, train machine learning models, validate, deploy, and monitor them.
The automation of repetitive tasks and ease to switch between development environments and its integrated project management features makes them a great choice for big teams to stay organized and efficient.
The platform also doesn’t lock you in to any tools and makes it easy for you to scale computing. Although if you’re still on a small team some of the platform advantages may not work so well since it requires many people to operate some complex tasks and it’s very Data Science focused.
But with that being said it has one of the best Workspace IDE varieties you can get one click away, making it very flexible and open in that sense. If you’re a Data Scientist working in an Enterprise level company that’s definitely a choice you should look into.
The current customer base includes Admiral Group, Cox Automotive, gsk, DBRS and Bayer.
Domino Data Lab quick facts:
🔴Startup friendly
🟢Flexibility
🔴No maintenance
🟠Cost efficiency
🟢Option to run on-premises
🟢Multi-cloud support
🔴Blockchain support
🟢14 day free-trial
3. Databricks
What is Databricks?
Databricks is considered one of the largest DataOps providers we have today. The company is known for its Lakehouse platform, combining features of a data warehouse and data lake to eliminate siloing, now used by hundreds of companies.
It helps its customers unify their analytics across the business, data science, and data engineering, and provides tools for data engineering and business teams to build data products.
With it you get fully managed Spark clusters, an interactive workspace for exploration and visualization, a production pipeline scheduler, and a platform for powering Spark-based applications.
Although its many great features and functionalities, Databricks is still a platform hard to use and get started, making it a good option for enterprise companies but a not so good one when it comes to Startups looking for high efficiency. If you’re a Startup looking for a similar offering with Spark clusters, some other providers like Shakudo or Domino Data Labs may be a better choice.
The current customer base include AT&T, Shell, Scribd, Amgen and Comcast.
Databricks quick facts:
🔴Startup friendly
🟠Flexibility
🟠No maintenance
🔴Cost efficiency
🔴Option to run on-premises
🟢Multi-cloud support
🔴Blockchain support
🟢14 day free-trial
4. Dataiku
What is Dataiku?
Dataiku is a data science and machine learning platform made to facilitate the creation of projects involving artificial intelligence and giving analytics insights. Dataiku makes it easy for businesses to scale to enterprise AI providing a controlled and prepared environment to retail, financial, pharmaceutical, and manufacturing industries primarily.
As an easy to learn and not so expensive solution, Dataiku is also a good choice for medium sized teams getting started with their Data Science application. It supports code and low-code development, but you get more flexibility by having a technical team of data scientists, engineers, and analysts working on it.
Their on-boarding process and community can be a great way to get started with this tool and if you’re a Startup, this can be a company that may fit well if you’re only looking for a data management tool only.
The current customer base include Sephora, Cisco, Zurich, Ubisoft, Merck and Accor.
Dataiku quick facts:
🔴Startupfriendly
🟠Flexibility
🟠No maintenance
🟢Cost efficiency
🟢Option to run on-premises
🔴Multi-cloud support
🔴Blockchain support
🟢14 day free-trial
5. Iguazio
What is Iguazio used for?
Iguazio is a MLOps Platform that allows the team to scale development, deploy and manage their workflow in one place. It assists your entire Machine Learning development process, also allowing you to deploy your application anywhere you want, whether it’s on cloud, multi-cloud or on-premises.
The hardest part of the Machine Learning development process is maintaining and getting models to production. Iguazio makes it a lot easier to get ML/AI projects deployed without the need of managing infrastructure.
Like Domino Labs, the focus of Iguazio lies on traditional Data Science use cases, so you might need some Data Scientists to get full advantage of it and its tools, although not ideal if you’re building a computer vision or deep learning specific application
But overall that's also a great option for Startups wanting to get to production quickly and relatively easy, especially if their project fits a traditional Data Science use case.
The current customer base includes Ecolab, Sheba, NetApp, Payoneer and Latam.
Iguazio quick facts:
🟠Startup friendly
🟠Flexibility
🟢No maintenance
🟠Cost efficiency
🟢Option to run on-premises
🟢Multi-cloud support
🔴Blockchain support
🟢30 day free-trial
The best data science platform for startups in 2023- Summary
Based on the total number of increased team efficiency, scalability, reliability, flexibility, and customer support, Shakudo has the best data science platform for Startups or small to medium sized teams. It allows you not only to process large amounts of data and handle jobs efficiently, but also provides an end-to-end environment for a whole application to be built without spending time or money integrating it with any other service. To start building your data application, reduce your infrastructure cost, increase your team efficiency or get access to blockchain data, Sign up for a Demo of Shakudo Today.
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5 Data Science Platforms for Startups in 2023
Thank you for filling out the form. The whitepaper you have requested is available for download below.
There are many options available in the market if you’re deciding which data science platform to build your application on, so which one should you choose? What is the difference between a good and a great data science platform? Let’s analyze a few of them to answer this question.
There are a few things to consider while choosing the right data science platform to handle the gathering, scrubbing, transformation, and application of data. When it comes to fast-growing startups, cost and time efficiency are two of the main ones.
If you’re managing a considerably large set of data for your application, you’d often need a way to efficiently process, analyze and present the data generated from multiple sources. In this case, having one place where you can centralize your workflow becomes very important.
Data-driven companies would simply never mature to the full potential we can today without powerful data science platforms backing it up behind the scenes. They influence the entire process from data ingestion to trends prediction, dashboards creation, analytics reports and many other use cases.
There are many options available in the market if you’re deciding which data science platform to build your application on, so which one should you choose? What is the difference between a good and a great data science platform? Let’s analyze a few of them to answer this question.
The Modern Data Science Platform
A data science platform enables the gathering, structuring, transformation, and control of your data by working also as a security layer for users and applications. It is crucial for you to be able to process multiple types and sizes of data with it, such as massive scale structured, semi-structured, or unstructured data. In other words, it has to be flexible.
To develop complex Machine Learning applications, as many applications require, you need as much flexibility you can get.
But only having data flexibility by itself does not often mean that you’re using a future-proof option for your data product. It’s important to also look for a platform where you’re not locked into any type of framework, cloud provider, programming language or any other service that can become obsolete in a few years.
Another thing to keep in mind when doing platform research for your project, that can save you a lot of future headaches, is making sure they have good customer support and product reliability.
A good data science platform is a place where you can scale and unify your workflow, helping you to coordinate, transform and provide data to an end-user.
A great data science platform is the place where you can do all of this while also running it wherever you want (cloud, multi-cloud or on premise), integrating it with any tool, framework or service available and bonus points for customer support and doing it cost and time efficiently.
The 5 Best Data Science Platforms for Startups in 2023
Now that you have an idea of what to look for in a data science platform, we made a list of the 5 best ones out there and how they fit for some particular use cases. This comparison will cover important traits like flexibility, pricing, customer support, tools, user experience, and enhanced APIs.
1. Shakudo
What is Shakudo?
Shakudo is a Cloud data science platform known for offering extremely high flexibility when compared to other competitors, along with providing not only fair pricing but also the best time efficiency you can get. Beneficial to any company looking to scale, but especially essential to Startups, since with the Shakudo Platform, a small team can achieve valuable results in half the time and save money with its optimized infrastructure.
Launch a cloud development environment in seconds, scale data workloads, run hundreds of parallel pipelines, deploy to production, monitor and never worry about maintenance. Shakudo’s great product and customer support are the reasons the company has an incredible 100% customer retention.
But what makes Shakudo get the first place in our list is because it doesn’t lock the user into any infrastructure, tool or necessary way to develop your application. The team can also choose to build the whole application inside the platform, not having to use any other services to develop or deploy the application.
The current customer base range from small startups to unicorns such as Quantum Metric, Risk Thinking AI, Manifest Climate, KIN and Empowered.
Shakudo quick facts:
🟢Startup friendly
🟢Flexibility
🟢No maintenance
🟢Cost efficiency
🟢Option to run on-premises
🟢Multi-cloud support
2. Domino Data Labs
What is Domino Data Lab?
Domino Data Lab is a scalable MLOps Platform that increases team productivity by combining data frameworks, tools, and programming languages together for each industry use case. Among the company offerings are processes to explore data, train machine learning models, validate, deploy, and monitor them.
The automation of repetitive tasks and ease to switch between development environments and its integrated project management features makes them a great choice for big teams to stay organized and efficient.
The platform also doesn’t lock you in to any tools and makes it easy for you to scale computing. Although if you’re still on a small team some of the platform advantages may not work so well since it requires many people to operate some complex tasks and it’s very Data Science focused.
But with that being said it has one of the best Workspace IDE varieties you can get one click away, making it very flexible and open in that sense. If you’re a Data Scientist working in an Enterprise level company that’s definitely a choice you should look into.
The current customer base includes Admiral Group, Cox Automotive, gsk, DBRS and Bayer.
Domino Data Lab quick facts:
🔴Startup friendly
🟢Flexibility
🔴No maintenance
🟠Cost efficiency
🟢Option to run on-premises
🟢Multi-cloud support
🔴Blockchain support
🟢14 day free-trial
3. Databricks
What is Databricks?
Databricks is considered one of the largest DataOps providers we have today. The company is known for its Lakehouse platform, combining features of a data warehouse and data lake to eliminate siloing, now used by hundreds of companies.
It helps its customers unify their analytics across the business, data science, and data engineering, and provides tools for data engineering and business teams to build data products.
With it you get fully managed Spark clusters, an interactive workspace for exploration and visualization, a production pipeline scheduler, and a platform for powering Spark-based applications.
Although its many great features and functionalities, Databricks is still a platform hard to use and get started, making it a good option for enterprise companies but a not so good one when it comes to Startups looking for high efficiency. If you’re a Startup looking for a similar offering with Spark clusters, some other providers like Shakudo or Domino Data Labs may be a better choice.
The current customer base include AT&T, Shell, Scribd, Amgen and Comcast.
Databricks quick facts:
🔴Startup friendly
🟠Flexibility
🟠No maintenance
🔴Cost efficiency
🔴Option to run on-premises
🟢Multi-cloud support
🔴Blockchain support
🟢14 day free-trial
4. Dataiku
What is Dataiku?
Dataiku is a data science and machine learning platform made to facilitate the creation of projects involving artificial intelligence and giving analytics insights. Dataiku makes it easy for businesses to scale to enterprise AI providing a controlled and prepared environment to retail, financial, pharmaceutical, and manufacturing industries primarily.
As an easy to learn and not so expensive solution, Dataiku is also a good choice for medium sized teams getting started with their Data Science application. It supports code and low-code development, but you get more flexibility by having a technical team of data scientists, engineers, and analysts working on it.
Their on-boarding process and community can be a great way to get started with this tool and if you’re a Startup, this can be a company that may fit well if you’re only looking for a data management tool only.
The current customer base include Sephora, Cisco, Zurich, Ubisoft, Merck and Accor.
Dataiku quick facts:
🔴Startupfriendly
🟠Flexibility
🟠No maintenance
🟢Cost efficiency
🟢Option to run on-premises
🔴Multi-cloud support
🔴Blockchain support
🟢14 day free-trial
5. Iguazio
What is Iguazio used for?
Iguazio is a MLOps Platform that allows the team to scale development, deploy and manage their workflow in one place. It assists your entire Machine Learning development process, also allowing you to deploy your application anywhere you want, whether it’s on cloud, multi-cloud or on-premises.
The hardest part of the Machine Learning development process is maintaining and getting models to production. Iguazio makes it a lot easier to get ML/AI projects deployed without the need of managing infrastructure.
Like Domino Labs, the focus of Iguazio lies on traditional Data Science use cases, so you might need some Data Scientists to get full advantage of it and its tools, although not ideal if you’re building a computer vision or deep learning specific application
But overall that's also a great option for Startups wanting to get to production quickly and relatively easy, especially if their project fits a traditional Data Science use case.
The current customer base includes Ecolab, Sheba, NetApp, Payoneer and Latam.
Iguazio quick facts:
🟠Startup friendly
🟠Flexibility
🟢No maintenance
🟠Cost efficiency
🟢Option to run on-premises
🟢Multi-cloud support
🔴Blockchain support
🟢30 day free-trial
The best data science platform for startups in 2023- Summary
Based on the total number of increased team efficiency, scalability, reliability, flexibility, and customer support, Shakudo has the best data science platform for Startups or small to medium sized teams. It allows you not only to process large amounts of data and handle jobs efficiently, but also provides an end-to-end environment for a whole application to be built without spending time or money integrating it with any other service. To start building your data application, reduce your infrastructure cost, increase your team efficiency or get access to blockchain data, Sign up for a Demo of Shakudo Today.
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