Azure Synapse Analytics is described as the former Azure SQL Data Warehouse, evolved, and as a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives users the freedom to query data using either serverless or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
$4,700
per month 5000 Synapse Commit Units (SCUs)
Dataiku
Score 8.5 out of 10
N/A
The Dataiku platform unifies data work from analytics to Generative AI. It supports enterprise analytics with visual, cloud-based tooling for data preparation, visualization, and workflow automation.
N/A
Posit
Score 10.0 out of 10
N/A
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
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Pricing
Azure Synapse Analytics
Dataiku
Posit
Editions & Modules
Tier 1
$4,700
per month 5,000 Synapse Commit Units (SCUs)
Tier 2
$9,200
per month 10,000 Synapse Commit Units (SCUs)
Tier 3
$21,360
per month 24,000 Synapse Commit Units (SCUs)
Tier 4
$50,400
per month 60,000 Synapse Commit Units (SCUs)
Tier 5
$117,000
per month 150,000 Synapse Commit Units (SCUs)
Tier 6
$259,200
per month 360,000 Synapse Commit Units (SCUs)
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Offerings
Pricing Offerings
Azure Synapse Analytics
Dataiku
Posit
Free Trial
No
Yes
Yes
Free/Freemium Version
No
Yes
Yes
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Optional
Additional Details
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Community Pulse
Azure Synapse Analytics
Dataiku
Posit
Considered Multiple Products
Azure Synapse Analytics
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Dataiku
Verified User
Engineer
Chose Dataiku
Open source availability is a critical factor given licensing cost of other platforms and budget reasons. Secondly, the available features in the community version covers most of the use cases, thus making it comparable or even outdo commercial versions of other software. …
It's well suited for large, fastly growing, and frequently changing data warehouses (e.g., in startups). It's also suited for companies that want a single, relatively easy-to-use, centralized cloud service for all their data needs. Larger, more structured organizations could still benefit from this service by using Synapse Dedicated SQL Pools, knowing that costs will be much higher than other solutions. I think this product is not suited for smaller, simpler workloads (where an Azure SQL Database and a Data Factory could be enough) or very large scenarios, where it may be better to build custom infrastructure.
Dataiku is an awesome tool for data scientists. It really makes our lives easier. It is also really good for non technical users to see and follow along with the process. I do think that people can fall into the trap of using it without any knowledge at all because so much is automated, but I dont think that is the fault of Dataiku.
In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.
Quick to return data. Queries in a SQL data warehouse architecture tend to return data much more quickly than a OLTP setup. Especially with columnar indexes.
Ability to manage extremely large SQL tables. Our databases contain billions of records. This would be unwieldy without a proper SQL datawarehouse
Backup and replication. Because we're already using SQL, moving the data to a datawarehouse makes it easier to manage as our users are already familiar with SQL.
The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.
With Azure, it's always the same issue, too many moving parts doing similar things with no specialisation. ADF, Fabric Data Factory and Synapse pipeline serve the same purpose. Same goes for Fabric Warehouse and Synapse SQL pools.
Could do better with serverless workloads considering the competition from databricks and its own fabric warehouse
Synapse pipelines is a replica of Azure Data Factory with no tight integration with Synapse and to a surprise, with missing features from ADF. Integration of warehouse can be improved with in environment ETl tools
The integrated windows of frontend and backend in web applications make it cumbersome for the developer.
When dealing with multiple data flows, it becomes really confusing, though they have introduced a feature (Zones) to cater to this issue.
Bundling, exporting, and importing projects sometimes create issues related to code environment. If the code environment is not available, at least the schema of the flow we should be able to import should be.
Python integration is newer and still can be rough, especially with when using virtual environments.
RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.
There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.
The data warehouse portion is very much like old style on-prem SQL server, so most SQL skills one has mastered carry over easily. Azure Data Factory has an easy drag and drop system which allows quick building of pipelines with minimal coding. The Spark portion is the only really complex portion, but if there's an in-house python expert, then the Spark portion is also quiet useable.
The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level
For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when
RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses
Microsoft does its best to support Synapse. More and more articles are being added to the documentation, providing more useful information on best utilizing its features. The examples provided work well for basic knowledge, but more complex examples should be added to further assist in discovering the vast abilities that the system has.
The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.
Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.
In comparing Azure Synapse to the Google BigQuery - the biggest highlight that I'd like to bring forward is Azure Synapse SQL leverages a scale-out architecture in order to distribute computational processing of data across multiple nodes whereas Google BigQuery only takes into account computation and storage.
Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by even other kinds of users.
RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.
RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.
Licensing fees is replaced with Azure subscription fee. No big saving there
More visibility into the Azure usage and cost
It can be used a hot storage and old data can be archived to data lake. Real time data integration is possible via external tables and Microsoft Power BI
Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).
