The Dataiku platform unifies all data work, from analytics to Generative AI. It can modernize enterprise analytics and accelerate time to insights with visual, cloud-based tooling for data preparation, visualization, and workflow automation.
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Talend Data Integration
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The Talend Integration Suite, from Talend, is a set of tools for data integration.
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Talend Data Integration
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Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the …
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. …
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 …
Talend has many built-in components that reduce the development work. We were able to complete the project sooner than expected. Easy to on-board resources as it is straightforward to use. We can manage all the pipelines in the cloud with simple alerting. No major downtimes. …
In comparison with the other ETLs I used, Talend is more flexible than Data Services (where you cannot create complex commands). It is similar to Datastage speaking about commands and interfaces. It is more user-friendly than ODI, which has a metadata point of view on its own, …
SAP Business Object Data Services is another ETL offering that I have used before Talend. The biggest advantage is that Talend is open source and very user-friendly. SAP BODS needs to improve on the user interface. Also, it works well with relational databases but is not very …
Data Preparation is something which can improved and connectivity with more visualization tools are few factors which can be improved. As Talend Data Integration becomes more cloud focused, the gap in features / functionality widens between on-premise functionality and the …
Talend is the best for ETL out of all the other products we looked at. Of course, it is not meant for synchronous services. But, batch jobs that we schedule and run on bulk data are the best fit for Talend Data Integration. Though Talend does not provide a preview of …
Most other tools of similar nature work well for small and medium sized data warehouses, but fail to maintain performance for very large data warehouses. However, Talend works decently well on large data as well. On the other hand, there are software tools like Oracle Data …
Talend is much versatile in assimilating various different business use cases. It covers more functionality and is packed with tons of features to explore. It has the ability to fine tune to each project, and not be a one-fits all solution. Problems with Excel, is that it is …
There are code building and code conversions internally in most of these ETL tools. The repository is an overhead in the ETL process. There may be a need for a full-time administrator to manage deployments and monitor jobs. With Talend, it seems to be transparent with the pure …
Compared to Microsoft SQL Server Integration Services (SSIS) talend gives developers much more tools and flexibility in order to achieve different ETL processes. For instance, SSIS, separates processing from data management, and Talend mixes both stages so that you can perform …
Talend has all the data integration features needed for an enterprise along with big data integration. It stands tall as a data integration suite and has a low cost as compared to some of its commercial counterparts. Talend does not have reporting tools like Pentaho but its …
I would recommend it because it's an amazing tool for different levels of users. From Business Analysts to Data Scientists to Managers, various employees can make use of this tool to make data-driven decisions. I'm not sure about where it would be less appropriate as I'm using it as Data Scientist and so far it pretty much caters to my need.
The same way you design data integration job can be used to design services. It is easy to enhance by custom components and can adapt to all requirements. Talend Data Integration connects to [a] multitude of data sources and streaming service. Very easy interface to design complex applications without spending much time on coding. Easy to learn and master. Talend constantly strives to better itself by adding more features and functionalities.
We used Talend to ETLing the data from myriad sources such Oracle Database, Clarify, Salesforce, Sugar CRM, SQL DB, MQ, Stibo Step, FTP, Netezza, and Files.
We leverage Talend transformation capabilities for stitching the data , unions and join
We successfully created the final unified set that can be used by business
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
We use Talend Data Integration day in and day out. It is the best and easiest tool to jump on to and use. We can build a basic integration super-fast. We could build basic integrations as fast as within the hour. It is also easy to build transformations and use Java to perform some operations.
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.
Good support, specially when it relates to PROD environment. The support team has access to the product development team. Things are internally escalated to development team if there is a bug encountered. This helps the customer to get quick fix or patch designed for problem exceptions. I have also seen support showing their willingness to help develop custom connector for a newly available cloud based big data solution
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
Talend has many built-in components that reduce the development work. We were able to complete the project sooner than expected. Easy to on-board resources as it is straightforward to use. We can manage all the pipelines in the cloud with simple alerting. No major downtimes. Connectors to all new applications in the market.
It’s only been a positive RoI with Talend given we’ve interfaced large datasets between critical on-Prem and cloud-native apps to efficiently run our business operations.