SnapLogic Reviews

My main use case for SnapLogic is mostly for doing ETL processes, moving data from heterogeneous data sources into our organizational data warehouses and data stores.

A specific example of how I use SnapLogic for ETL in my organization is that we have various clients in the health industry that submit data to us for insurance and diagnostic reasons. We receive the data in various forms such as CSV files or text files, and I have SnapLogic pipelines that regularly check for these files to drop at certain particular times. I load them from their text files into our database warehouse, doing necessary transformations based on whatever specifications we have from the clients.

SnapLogic gives us a lot more ability to perform many transformations. In my transformations, I can write scripts in Python and other scripting languages. The snaps that I use are very useful. Special snaps can do certain things for you, which reduces the amount of coding you have to do, especially when you get to the transformation areas. The volumes, convenience, and software interface make it easier, particularly visually when mixing objects, tasks, and snaplets while working with code behind it.

SnapLogic has positively impacted my organization because it makes it easier to concentrate a lot of our ETL processes. Because of the diversity of data sources, we can process a lot of data from various clients that have different sources sending data to us. The fact that SnapLogic is hosted on AWS makes it very current and convenient to use.

I can share specific outcomes or metrics that show how SnapLogic has benefited my organization. Previously, I used other applications, and using SnapLogic allows me to connect to data sources, perform all the snaps needed to manipulate the data, write conversions and transformations, and connect and direct to data output. Some projects from the past, many years ago, required me to go through evaluating tables and sources. With SnapLogic, I have been able to complete projects in approximately four times less time than I used to.

The best features SnapLogic offers are its interface and the snaps available for various functions.

The interface makes it stand out for me. I can look at the selection menu where I can pick specialized snaps and join them together. The Intellisense available and the snap environment that allows automatically picking snaplets to put them together are impressive. Recently, with ChatGPT making good suggestions based on how I want to build my ETL process, those features are useful. I can visually see the progression of my solution as I go along, which is helpful.

The features are good enough, and the representation and the way the various things one would want to do are designed is excellent. I have the designer interface, then the monitor interface, and then the administrative interface where I deal with users and roles. The design is very well separated out, so I know which area I am in and what I am doing. What is put on the canvas in front of me as I work on those objects, whether building a pipeline or working on user configurations, makes the interface environment pretty easy and intuitive.

One of the things I notice with the new interfaces is that a lot of functionality is condensed into the available windows where we work. I would like to spread them out a little bit so developers do not get overwhelmed during development. When too many things are in the window, for example when I am monitoring processes and looking at the various pipelines, there is small real estate and a lot of information that I have to look at in order to figure out where my sources are, where my targets are, and what pipelines are running. There is so much information condensed that sometimes I do not have enough room to actually look at the IDs of some of the objects I am using because the space available in the design does not allow the whole label or significant portion of the label to be displayed. Not condensing too much information into the application editor where developers work would be a good thing. Splitting them up into smaller interfaces so one could easily move from one to the other and not be confused or overwhelmed with the development information available would be beneficial.

At the moment, those are the things that I have noticed that probably, when improved, could change the experience of development.

I have been using SnapLogic for the past ten years.

I would rate SnapLogic overall as a nine out of ten. I give it a nine because I am able to do all my ETL processes conveniently and better than I used to do in the past ten to fifteen years ago. The technology has improved a lot and is basically good.

Regarding SnapLogic's AI capabilities, I think governance and security are handled well as my organization takes care of it. One of the key concerns is if data leaves my environment, my garden environment with proprietary data, and gets into the outer world where AI can use it for whatever reasons, since not everybody out there has good intentions. I am a bit concerned about data going beyond the walls of my organization and being used for whatever reason. I leave the governance to my organization who takes care of it and makes sure that people stick within the limits of what they are supposed to do and do not mistakenly let data seep out of our organization where other agents or AI agents can take care of it and run off with it.

Regarding SnapLogic's AI capabilities, I think the accuracy and reliability of output are fair and close to almost accurate. I believe it all depends on how good or how well I prime it and the questions I put to it. For the simple tasks I have been doing and solutions I am looking for, I am usually able to prime it properly and I do get very accurate answers back with minimal changes for me to achieve my objectives. I would say it is close to pretty accurate.

I would advise others looking into using SnapLogic to give it a try. My overall rating for SnapLogic is nine out of ten.

Hybrid Cloud

Amazon Web Services (AWS)

My main use case for SnapLogic is to build integrations between two different applications or systems, mostly to facilitate the integrations part.

I can give you an example of an integration project I have built using SnapLogic. I built one of the more complex integrations, which was near real-time customer data synchronization between the Salesforce on-premise ERP system using SnapLogic. Their goal was to make sure both systems stay aligned on customer records, orders, and their status updates without any human intervention. So, the main goal is to make it all automatic with SnapLogic process. I designed a triggered pipeline using a Salesforce listener tap that captures all the record changes, ensuring no human intervention is needed. This data flows through validations and transformation Snaps, where I standardize the formats, handle a few operations, and ensure full consistency with the data. I also implemented a reusable error handling sub-pipeline that logs the failures in monitoring databases and sends alerts through email or channel notifications. For the ERP side, I exposed SOAP services, configured the SOAP execute Snaps with dynamic requests, and generated the payload as well. I ensured performance optimization issues were addressed as the volume increased by batching requests and parallelizing the process. This integration is now fully automated and monitored, requiring no human intervention. This is one of the integration projects among many I have worked on with SnapLogic.

I have also handled various integration use cases with SnapLogic. I have built REST API pipelines used to expose backend security to external applications, utilizing API tasks, API policies, and pipeline parameters. I have focused on batch ETL data pipelines for migrating large datasets from databases, like Snowflake to other cases, using bulk Snaps throughout. Additionally, I have worked on event-driven integrations using ultra pipelines for low latencies. I have connected applications, integrating CRM to ERP and ERP to CRM, where I handled mapping, transformations, validations, and reconciliation reporting. Another use case is for file processing automation, particularly with automated ingestion of CSV, XML, and JSON files, where I parsed and validated the file structures before loading into databases and generating reports and success/error messages. Lastly, for error handling and monitoring frameworks, I built and logged failures to database log services, created alerts via email or Slack, and stored failed payloads for retrievability, ensuring data quality and transformation pipelines with standardized formats. These represent some of the many use cases I have worked on.

SnapLogic positively impacts my organization, mainly in three areas: speed, system reliability, and maintainability. Before adopting SnapLogic, integrations were either custom coded or handled through scripts, leading to fragility and scaling challenges. With SnapLogic's reusable pipelines and pre-built Snaps, development time for new integrations drops significantly, in some cases from weeks to just a few days, as I no longer need to rebuild connection logic from scratch. The built-in error handling improves reliability, with monitoring dashboards and retry mechanisms reducing production failures and providing visibility into pipeline performance, allowing me to detect and resolve issues proactively instead of relying on business user reports. Another significant improvement is maintainability; the visual and modular nature of pipelines simplifies onboarding for new team members, making it easier to learn and standardize parameterization.

Speaking of metrics, I would emphasize a specific example: in one of my integrations, I synchronized customer and order data between Salesforce and my ERP. Initially, builds took about two to three weeks with custom scripts and manual API logic. After transitioning to SnapLogic, I averaged this down to just days for similar integrations, primarily due to the reusable pipelines that streamline efforts. The majority of time savings stemmed from the pre-built Snaps, eliminating the need to write authentication or pagination logic anew. Regarding reliability, before SnapLogic, I experienced approximately eight integration failures per month due to timeout errors, schema mismatches, or unhandled null data. After implementing structured error handling pipelines, retries, and validation layers, this number has dropped to around two to three incidents monthly, mostly attributed to upstream system issues rather than pipeline failures. From a maintenance angle, onboarding new developers who previously needed weeks to confidently modify integrations has been dramatically reduced with SnapLogic's visual pipelines and standardized design approaches, leading to faster delivery, fewer production issues, and less time spent debugging.

SnapLogic offers numerous features that stand out. One of the key features is the pipeline designer visualization, where users can drag and drop components based on their use cases, making it user-friendly. SnapLogic execution transparency and preview at each data step provide vital information about how components work and their utility. Another highlight is SnapPacks, featuring pre-built connectors, which save time. There are hundreds of connectors for APIs, databases, SaaS applications, files, and messaging systems, with built-in authentication handling. Ultra pipelines boast impressive response times in milliseconds and are persistent in executing nodes. Additionally, SnapLogic's modular designs promote reusability, allowing developers to maintain structured development. The error handling frameworks also enable production-grade setups without needing custom frameworks, facilitating retry logic and hybrid architecture flexibility. SnapLogic excels in data transformations, monitoring, and observability, providing scalability controls for the pipelines.

I would like to highlight the expression language feature, which is primarily based on JavaScript and allows for logic to be embedded directly in the pipeline components. Its strength lies in dynamic routine logic, making it easy to write clean, efficient expressions for various use cases. Another notable feature is the pipeline execution mode offering options for trigger tasks, scheduled tasks, alter tasks, and execution patterns. This flexibility aids in designing execution strategies. SnapLogic also integrates metadata and schema handling, with automatic design and schema capabilities being significant differentiators. Experienced engineers truly understand schema stability with SnapLogic. Other features such as pipeline patterns, design, and scalability further contribute to its robustness. However, if I had to choose a favorite feature, it would be the reusable child pipelines with parameterization because it enforces standard logic, reduces duplication, and underscores SnapLogic's role as an integration platform rather than just a tool, allowing for team scaling and ensuring consistency.

While SnapLogic is powerful, there are several areas for improvement that could enhance user experience. Version control remains an area needing attention as it currently lacks effective features. Debugging complex pipelines can be painful, especially when dealing with deeply nested structures, making it difficult to trace data lineage across pipelines. Improvements in centralized execution and trace visualizations are also necessary. Furthermore, compared to other code-based tools, there is room for advancement in structured transformations, making this a critical area for improvement.

I have been using SnapLogic for eight years.

I would rate customer service as a 5.

Positive

For monitoring and alerting my SnapLogic integrations, I utilize various dashboards. I implement a layered approach, conducting platform-level monitoring, pipeline-level logging, and proactive measures. Using the built-in dashboard for runtime metrics and execution histories provides operational visibility. I design pipelines with a centralized logging and alert framework, ensuring failures are immediately detected rather than discovered by users.

I manage versioning and deployment of pipelines using a structured promotion model across environments, including development, QA, and production. Pipelines are developed and tested in development projects and promoted to higher environments using SnapLogic's project export functionality. Environment-specific values remain externalized through parameters and accounts, enabling the same pipeline to operate across all environments without modification. For version control, I maintain backups and track versions with naming conventions for proper documentation and repository snapshots. Prior to deployment, I validate dependencies and conduct test executions to ensure stability, minimizing configuration drift and securing successful deployments.

SnapLogic supports data transformations primarily through its mapper Snap and expression languages that facilitate complex field mapping. This includes conditional logic, data restructuring, and format conversions. In my projects, I utilize the mapper Snap for most transformations, as it allows for visual mapping of schemas while concurrently supporting advanced logic through expressions. For complex scenarios, I combine the mapper with scripting and aggregate, router, and join Snaps to develop more modular transformation pipelines. This approach maintains transformation processes that are reusable and scalable across integrations.

My advice for anyone considering SnapLogic is to view it as an integration platform rather than merely a tool. Doing so can yield stronger results when teams design pipelines with scalability, modularity, and governance in mind from the onset. Organizations should invest early in defining naming standards, reusable components, parameterization strategies, and monitoring frameworks. SnapLogic can significantly accelerate development, with its real value revealing itself when implemented with architectural discipline rather than for quick, one-off integrations.

As a closing thought about SnapLogic, I would emphasize that it is indeed a powerful integration platform with clear strengths, but it also has defined limits. Its effectiveness comes to the forefront when used properly, and its success heavily relies on implementation discipline rather than solely the tool itself. I have given this review a rating of 9.

Hybrid Cloud

Amazon Web Services (AWS)

Our main use case for SnapLogic is the integration of student data, some finance data, and some internal Stanford data processes.

A specific example of how I use SnapLogic for integrating student or finance data is when new staff, students, or faculty join Stanford; they have to be added to different workgroups or their email has to be set up, so many processes are done via SnapLogic integration. On the finance side, it involves processing data related to the Office of Development, where some gift processing happens. Multiple teams at Stanford, including the School of Business, use SnapLogic for tasks such as booking meeting rooms.

We use SnapLogic for various integration purposes, including integration from Salesforce to Oracle, from flat files to Oracle, and integration from FileMaker to Oracle, among others. Although many more integrations are running as pipelines, these are a few I can recall as an infrastructure team member.

The best features SnapLogic offers are its ease of development for pipelines, where the authentication and account configuration are straightforward, and it allows for easy connections to sources and targets, as well as simple transformations and making multiple copies of the same data. I find the development part very easy, especially from an infrastructure perspective, since we host all Snaplexes on-premises servers; upgrades are seamless, and the impact on pipelines during rolling upgrades is minimal.

The development interface is user-friendly, making the process of connecting to sources straightforward, especially when setting up accounts using any Snap or API, which allows anyone to use it easily. Making configuration changes or moving servers between environments is also very easy.

SnapLogic has positively impacted our organization by providing an alternative to Informatica, which was challenging due to licensing and other issues. With SnapLogic, we can now connect to various SaaS and cloud products, and its usage has spread across different departments, making it a central tool for both on-cloud and on-premises ETL processes.

Areas for improvement for SnapLogic include enhancing the documentation, especially by providing more complex examples in each Snappack, to cover various use cases that different team members may encounter.

Another improvement I suggest is enhancing the monitor page on the dashboard to allow quicker and more efficient access to historical data, as it currently becomes slow and sometimes hangs when accessing older data.

I have been using SnapLogic for six to seven years.

I have been using SnapLogic for a while, and I believe the tool has become more stable, although there are still monitoring features I have had to build on my own. More robust APIs would assist anyone looking to automate processes.

My advice for others looking into using SnapLogic is to participate in training sessions provided by SnapLogic support, which can significantly help new users understand the UI and its functions through self-learning videos. I would rate my overall experience with SnapLogic a 9 out of 10.

On-premises

Our main use case for SnapLogic Intelligent Integration Platform (IIP) involves several workflow integrations where we use SnapLogic Intelligent Integration Platform (IIP) as our integration tool to pull data from multiple source systems and load the data to our target systems using the pipeline integrators, task schedulers, and SnapGPT as the latest AI insight. We use SnapGPT for many questions, along with many models within SnapLogic Intelligent Integration Platform (IIP) for pharma benefit management, FirstARx integrations, and pharmacy integrations.

SnapGPT and those AI insights help our team immensely, particularly since we have over 500 developers who use SnapLogic Intelligent Integration Platform (IIP) within our organization. Our developers use SnapGPT for expressions, building pipelines, pipeline reviews, documentation, and more.

SnapLogic Intelligent Integration Platform (IIP) is our primary ETL tool that we utilize within Prime for a lot of integrations.

SnapLogic Intelligent Integration Platform (IIP) has positively impacted our organization by helping us onboard many projects very quickly and efficiently. We have migrated most of our Informatica ETL use cases, and all the ETL workflows have been transitioned to SnapLogic Intelligent Integration Platform (IIP). It is a great tool that we use for regular ETL use cases as well as API integration use cases.

After migrating from Informatica to SnapLogic Intelligent Integration Platform (IIP), we have seen specific outcomes, including saving almost 2 million dollars from one of our migration projects. Efficient use of SnapLogic Intelligent Integration Platform (IIP) has reduced our timelines, enabling us to develop our projects rapidly and save countless man-hours. We are also saving significantly on support, as we do not need a lot of assistance to maintain the platforms and pipelines.

SnapLogic Intelligent Integration Platform (IIP) offers excellent features, including an easy user interface where users do not have to do a lot of development. Our users can simply log in, pull the connectors, connect their source systems and target systems, review validations, check the data, and build pipelines very seamlessly.

SnapLogic Intelligent Integration Platform (IIP) has a user interface built on HTML5, making the designer very easy to use for all users. They can log into SnapLogic Intelligent Integration Platform (IIP), drag and drop the Snaps to connect their data or target systems or source systems, develop the pipelines, and then deploy them.

SnapLogic Intelligent Integration Platform (IIP) has the best security embedded within the tool, adhering to the highest security standards. Based on our extensive use of SnapLogic Intelligent Integration Platform (IIP), we rely heavily on this platform, and security-wise, they are really strong, adhering to best practices and the latest security protocols.

To improve SnapLogic Intelligent Integration Platform (IIP), we would like to see how we can integrate many models within SnapLogic Intelligent Integration Platform (IIP) to enhance pipeline enhancements or migrations. We want to explore how easily we can migrate from existing tools like Informatica, IIS, or DataStage to SnapLogic Intelligent Integration Platform (IIP), aiming for more expedited use cases.

We have been using SnapLogic Intelligent Integration Platform (IIP) for almost 10 years, making it our integration platform where we regularly use it for data loads, ETL, and API integrations and workflow integrations. SnapLogic Intelligent Integration Platform (IIP) is a great tool.

SnapLogic Intelligent Integration Platform (IIP) is very stable.

In terms of scalability, we have auto-scaling in place, so we encounter no issues with scaling the nodes.

Customer support for SnapLogic Intelligent Integration Platform (IIP) is fantastic. I would rate customer support a seven based on the support experience we have had. They are professional with solid technical expertise.

We previously used Informatica for our ETL jobs before moving to modern technologies and choosing SnapLogic Intelligent Integration Platform (IIP) as our primary ETL tool.

We have seen a return on investment, saving a lot of development hours from a development standpoint. The time to develop any project is very quick, and we do not need to worry about coding.

My experience with pricing, setup costs, and licensing is that our vendor management team deals with it, and I believe they are satisfied with the pricing we received from SnapLogic Intelligent Integration Platform (IIP).

Before choosing SnapLogic Intelligent Integration Platform (IIP), we evaluated multiple other options, including Workato and Boomi.

SnapLogic Intelligent Integration Platform (IIP) is deployed in our organization on-premises, where we have installed our SnapLogic Intelligent Integration Platform (IIP) on AWS, which is owned and maintained by the Prime organization. We use Amazon Web Services (AWS) as our cloud provider.

For any users looking to try SnapLogic Intelligent Integration Platform (IIP) for their ETL solutions or API solutions, I recommend choosing SnapLogic Intelligent Integration Platform (IIP) as their preferred tool. I rate SnapLogic Intelligent Integration Platform (IIP) a seven based on our extensive use of this platform over quite a while, which reflects all the use cases we have developed for the many integrations we have established, and I really love using this product on a day-to-day basis.

On-premises

Amazon Web Services (AWS)

My main use case for SnapLogic involves all the ETL applications where I need to get data from sources and then perform transformations and either send it to downstream applications within our firm or to any external vendors that need specific data sets.

For one of those ETL processes, I can give a specific example where some applications need data related to people and some of their transactions, which we hold in different applications. For people data, we use SnapLogic to connect to Workday and get their information such as first name, surname, preferred surname, and HR number, while their transactions, which are the things they actually work on, do not exist in Workday. These transactions are stored in a different application called 3E. In the same integration, SnapLogic gets people data from Workday and financial data from 3E, which is related to different users, and then we map them together by using joins and send all the user-related data along with their related financial transactions. We group them together, and each of the user data will be sent to an external vendor.

This main use case is our primary use case in most of the integrations.

SnapLogic has positively impacted our organization because over time, we have only started building more and more integrations within SnapLogic. Although about three or four integrations have been decommissioned, within that time span, we have built at least ten or fifteen more integrations, so I can see it is only growing bigger.

Regarding the outcomes I have seen, recently since AI tools have picked up, there was SnapGPT within SnapLogic, which understands everything specifically to SnapLogic, and that has helped me a lot. It also builds the pipelines for me and does the mappings when I use basic English to explain what I want as an output from the data that is going into the snaps, making it quite helpful in using SnapGPT.

The best features SnapLogic offers make it very simple to use, so it only took me around two or three months to get used to the interface, and in the past five to six years, I have noticed that there have not been many big changes. It is nothing very difficult to learn, as it has always been very simple with the interface and everything being quite easy to learn.

I would add that all the connectors we have include the basic set of Snap Packs, and I am very much familiar with them. Over time we did purchase some premium Snap Packs because we wanted to connect to different applications which were not offered by default, such as Azure Service Bus and Databricks, and we do have documentation available online, which helps us understand clearly without actually having to reach out to support to understand how to use them.

SnapLogic can be improved by generating SQL queries within SnapGPT, including the variables, as it would be very useful because lately, I have been using other AI tools such as Claude or ChatGPT to generate SQL queries. If SnapLogic SnapGPT can do that efficiently, then that would be very helpful.

It would be easier if there were a support chat option within the pipeline so that we do not have to raise a ticket on the portal to explain what is happening and then grant access. If by default, in the test environment and dev, not in prod, we could add a chat option where a live chat person can actually be contacted within SnapLogic itself, that would be very helpful.

I have been using SnapLogic for six years.

In my experience, SnapLogic is stable.

SnapLogic's scalability measures are evident as we use it for small integrations that run under thirty seconds and some integrations that run for about five to six hours as well. So far, I do not think anything has caused any issues, so it is stable. Scalability-wise, I think we can go from small minimum integrations to a massive one.

SnapLogic customer support is quite helpful, and we have a customer success manager who increases the priority on our tickets when we say something is really important and we want a resolution as soon as possible. Overall, support and our CSM have been very helpful. On a scale of one to ten, I rate SnapLogic customer support a nine.

Before SnapLogic, I do not think we used any other solutions or similar applications, as I believe it has been about eight years since we introduced SnapLogic. Prior to that, we had SAP and Workday, with all different applications sitting on their own without connecting many applications, from what I know. SnapLogic was the first application of its kind that we introduced in our firm.

Before choosing SnapLogic, I was not there eight years ago, so I would not know if they evaluated other options.

SnapLogic is deployed in our organization where all our SnapLogic nodes are on-premises, so we have not migrated to the cloud yet. The advice I would give to others looking into using SnapLogic is that it is very simple to use. I understand this is a low-code platform, but being a low-code platform, it is quite efficient, and I could recommend it to anyone. I rate SnapLogic an overall nine out of ten.

On-premises

SnapLogic is used in three different areas within our organization: primarily data ingestion, reverse ETL, and the most critical aspect is building APIs. We build APIs in SnapLogic that get exposed to both third parties and internally.

A specific example of how I use SnapLogic for building APIs is in our CPQ integration, where a sales representative can make a quote by using Salesforce to call the API we created, connecting to our internal systems for customer and product information as well as Zuora to create a preview order, including calculations such as taxes. Once the quote is complete, the salesperson makes another call when the order gets created, pushing that order into our provisioning system and Zuora for billing, all managed with another API. Additionally, once the order is fulfilled in Zuora, we receive an event through a real-time API that pushes data back to Salesforce.

In addition to the CPQ example, we also create or transform data in Databricks using SnapLogic to send data back to our various systems. One of the more complex use cases involves sending emails to Iterable, as the data in one system is stored in a very structured way, while Iterable uses a more complex JSON structure, consuming the most memory and resources in our SnapLogic nodes.

The best features SnapLogic offers include ease of use. I have a lot of experience with other tools such as Informatica, Talend, Boomi, and Pentaho. At Asana, when I needed to start onboarding new team members and building the team, SnapLogic was already in place, allowing me to build the first integration in about half an hour and understand how the system works, which is critical for time to market. This capability reduces the need for additional resources or capacity during significant deployments or projects.

SnapLogic's user interface and workflow design aid my team in moving quickly. The concept behind the name Snap is that you can easily connect different features. For instance, remapping a database query into JSON for a different application is straightforward and feels like building a puzzle. With features such as SnapGPT and systems that generate JSON for SnapLogic, the process becomes very easy. When I started five and a half years ago, we scaled up from having five developers due to increasing needs, and with advancements in SnapGPT, we managed to reduce our contractor base while delivering more in less time.

SnapLogic is very feature-rich, and I appreciate the predefined snaps. For example, integrating with NetSuite has always been challenging for us, but after a recent upgrade, we updated our connector and it did all the work for us, finishing what would have taken weeks in just a couple of days, including testing.

SnapLogic has positively impacted our organization by improving time to market and reducing our reliance on experienced Python developers or hardcore coders to generate outputs and deliver results. Consequently, the platform has significantly helped us with timing and costs. It also offers flexibility with our use of Groundplex and Cloudplex; we utilize Cloudplex for internet-friendly tasks and Groundplex for secure tasks that cannot have data leave our premises.

From our development perspective, we have requested SnapLogic to enhance their SnapGPT to be friendlier, improve documentation capabilities, and enable more Cursor-based type development. While not all developers are fond of the current environment, SnapLogic has been responsive to our requests for improvement, often providing fast solutions for testing and implementation.

Additionally, we wished for a better implementation of the GitHub integration, as the existing method resembles older practices, where pushing a pull request involves pushing an entire package. Fortunately, they have implemented new features that allow us to be more flexible in this regard, but we still desire the ability to manage multiple folders more efficiently rather than having to create a separate repository for each folder.

Five and a half years.

My advice for others looking into using SnapLogic is to conduct comparisons of the competition without falling for their marketing tactics. After extensive analysis, including RFP processes and scrutinizing details, I believe SnapLogic stands ahead of other tools, especially when it comes to handling large data movements under the radar. It is important to seek tools that facilitate both data delivery to a warehouse and capabilities for transformation and reverse ETL.

SnapLogic has been an exceptional partner in implementing solutions and maintaining constant communication, particularly during a recent downtime issue, where their updates and care for customer relations were greatly appreciated. I would rate this review an eight out of ten.

Hybrid Cloud

Amazon Web Services (AWS)

My main use case for SnapLogic is developing pipelines to transfer data from a source into our Dynamic CRM. We are thinking about using APIs and more with AI within our pipelines, and hopefully, in the future, we want to develop pipelines to write into our data warehouse once we have that implemented.

SnapLogic offers excellent features based on my experience. The snaps are very easy to work with, and it is straightforward to understand what the different kinds of snaps are available and when to use them. I specifically like SnapGPT, which has been very useful. As a solo developer at Mind, having SnapGPT to perform code checks or identify any risks within my pipeline has been invaluable.

SnapLogic has positively impacted my organization because our previous ETL tool called Scribe was going to be decommissioned, and we really needed a different way of transferring data from a source, mainly a CSV file, into CRM. Without it, we would not be able to import any of our CRM data into our Dynamics, which would prevent our CRM users from performing supporter relations, handling our supporters, and thanking them.

Regarding my experience with SnapGPT, I always ask it if my pipeline has any risk. When there are risks, such as not catching a certain data type, it lets me know that perhaps instead of returning an empty string, it should return null. It has been useful for helping me develop code. Recently, I wanted to take a string where it identifies the first part as address line one, and I wanted to search for the postcode within the long string. SnapGPT was able to give me an expression that allowed me to take part one as address line one, and because the UK postcode has a certain way of being written, it was able to identify where the postcode is within the string.

SnapLogic could improve its notification system. When a pipeline fails, the notification sometimes comes back as not very useful. I had a recent case where it just told me that a snap had failed unexpectedly without providing any reason, which is not helpful for troubleshooting, especially on the live system. To resolve this, I had to manually validate the pipeline to find the issue, which allowed me to see the data running into those individual snaps and what the error was. I would like to see if the execution can show the data flowing into those individual snaps and provide the same kind of error that I receive when validating, as that would be much more useful.

I would give SnapLogic a higher rating if the error handling and notification were more specific and user-friendly to read. Apart from that, I do not think there is anything else that I can identify.

I have been using SnapLogic for a year and three months.

In my experience, SnapLogic is very stable.

The scalability of SnapLogic is good. We develop our pipelines to be quite robust so they can be scalable in the future if users want more complex pipelines. Mind is quite early in using data, so the data we currently work with does not have that much complexity.

The customer support for SnapLogic is excellent.

SnapLogic has positively impacted my organization because our previous ETL tool called Scribe was going to be decommissioned, and we really needed a different way of transferring data from a source, mainly a CSV file, into CRM. Without it, we would not be able to import any of our CRM data into our Dynamics, which would prevent our CRM users from performing supporter relations, handling our supporters, and thanking them.

While I do not have any metrics, SnapLogic has definitely saved a lot of time because of how easy it is to implement designs for our data transfer. It has definitely saved time and streamlined many ways of working. Previously, a person would drop a file and then have to perform a lot of data manipulation within the file to push it into our Scribe process and then into CRM. SnapLogic saves a lot of users' time and reduces potential errors that could occur from a person manually updating the data.

If others are looking into using SnapLogic for Dynamics 365, my advice is that there is a significant learning curve in understanding how SnapLogic and Dynamics should communicate with each other. There is not really much documentation regarding using SnapLogic and Dynamics together, so there are quite a lot of nuances to figure out before being able to connect the mappings to the correct fields in Dynamics, using the OData bind functionality, and sometimes needing to know whether to use null or empty strings for certain fields. This knowledge would have been valuable to have at the beginning. I would rate this review an 8 out of 10.

Private Cloud

Amazon Web Services (AWS)

We are building the Finance Operation Warehouse for organizations with SnapLogic Intelligent Integration Platform (IIP), bringing data from heterogeneous sources and converting it for consumption by some other cloud platform or ERP platform.

Once accounting is done, we are pulling it back from the ERP via API calls and putting it into a Snowflake layer, which is the typical use case where we leveraged SnapLogic Intelligent Integration Platform (IIP).

SnapLogic Intelligent Integration Platform (IIP) provides workflow automation and a data integration pipeline that is seamless, as it is just drag and drop with double-click functionality and no need to publish the metadata, as it is intelligent enough to figure out the metadata by looking at the source connector.

The deployment process with SnapLogic Intelligent Integration Platform (IIP) is very easy, requiring just a ten-minute job to move the data pipelines from one environment to another if you plan it properly.

We implemented SnapLogic Intelligent Integration Platform (IIP) by ourselves as we are a team of twenty people for the data experience side, actually trying to replace MuleSoft and a combination of Informatica, and we leveraged SnapLogic Intelligent Integration Platform (IIP). It is low-code, no-code, drag and drop, so we were able to replace that complex system architecture with the help of SnapLogic Intelligent Integration Platform (IIP).

SnapLogic Intelligent Integration Platform (IIP) is a good tool, and no challenges have been observed when implementing the solution as everything has gone smoothly.

We tried to leverage SnapLogic Intelligent Integration Platform (IIP) with the AWS platform, but utilizing SnapLogic Intelligent Integration Platform (IIP) towards the AWS platform has been a little complex when compared to an Azure private cloud.

I have been leveraging SnapLogic Intelligent Integration Platform (IIP) for more than four years, starting in 2019 and using it until 2022.

We have not used Iris AI with SnapLogic Intelligent Integration Platform (IIP) because our organization is not allowed to use any AI feature under the impression that the data will go outside, so they do not want to expose their data to any AI agent.

I would rate the technical support of SnapLogic Intelligent Integration Platform (IIP) as nine out of ten, as usually you do not require too much support from a user's standpoint; the documents are self-sufficient, and the blogs are available to check out for sufficient help.

I would rate SnapLogic Intelligent Integration Platform (IIP) on its batch and real-time processing capabilities as an eight.

I would rate the IoT connectivity with SnapLogic Intelligent Integration Platform (IIP) as an eight out of ten.

I am not aware of the pricing for SnapLogic Intelligent Integration Platform (IIP), as I do not have much experience with enterprise licensing, so I am not aware of what the negotiation has been.

I would rate this review a nine out of ten overall.

My main use case for SnapLogic is data integrations, automation, and data transformations.

A specific example of how I use SnapLogic for data integration and automation involves reading data from SharePoint and Salesforce, then dumping that data into Snowflake. While performing the data transfer, I conduct data integrity checks to ensure consistency, implement validations, maintain error pipelines with retries in place, and capture any errors through the error pipeline before sending them to DataDog.

SnapLogic is mostly used for integrations that connect from one platform to another, retrieve data, perform cleaning and transformation, and then push the data to the main data warehouse.

SnapLogic has positively impacted my organization by increasing developer efficiency. I do not have to invest significant time writing extensive Python code because SnapLogic offers pre-written snaps that improve development time rather than requiring engineers to write code from scratch. Tools such as SnapGPT enable me to create an initial pipeline by typing simple English, which generates a sketch that I can customize according to my needs. This increases efficiency and improves the development lifecycle by providing pre-designed patterns that eliminate the need for lengthy thinking, designing, and testing phases.

Before implementing SnapLogic, engineers spent approximately a week writing Python code for small integrations such as connecting to MongoDB, SQL, or Mongo Atlas, including design and coding phases. Now, engineers use pre-designed, pre-written snaps in SnapLogic by simply inputting credentials and connecting to infrastructure, whether cloud-based or on-premises. The difference is substantial.

SnapLogic's best features include UltraTask, which I rely on heavily. I have Kafka producers pushing messages to SnapLogic with a Kafka listener that picks up those messages, transforms them, and sends them to different tools such as Salesforce for marketing. This represents one of the best use cases for processing real-time data and events.

UltraTask and Kafka integration help my team daily through seamless integration. I previously experienced delays, but after adjusting settings with SnapLogic support, the integration improved significantly. SnapLogic now serves as the intermediate layer between Kafka and Salesforce Marketing Cloud, a role I previously lacked tooling for. SnapLogic captures Kafka events by listening through UltraTask, which runs continuously without stopping, and sends them to Salesforce Marketing Cloud. This allows my users and customers to access lounges, increasing business efficiency.

SnapLogic maintains good monitoring capabilities. The monitoring dashboard displays memory utilization, consumption levels during peak and off-peak times, and allows me to view granular details on charts to identify reasons for CPU utilization spikes. These monitoring features provide excellent control over my platform. I utilize a hybrid infrastructure and am not entirely reliant on cloud services. Additionally, I use SnapLogic's Private Link feature, which connects my internal tooling from the SnapLogic Cloud instance with CloudPlexes to my internal applications without passing through the public internet. This feature is particularly valuable for Snowflake.

Regarding SnapLogic's AI capabilities, I have not explored those features extensively. However, I have used SnapGPT, which performs very well in designing pipelines from scratch and analyzing existing pipelines. SnapGPT is a valuable tool for entry-level developers starting with SnapLogic, though I have not used it to an advanced extent.

Regarding improvements for SnapLogic, I observe that networking and security technology continue to evolve, but SnapLogic lacks the transit gateway feature. As an integration tool, SnapLogic should prioritize establishing newer networking features such as transit gateway and PNI so that users and businesses can reliably use them, particularly for organizations requiring everything within a private network.

Beyond transit gateway and networking improvements, I do not see other areas requiring immediate improvement at this time.

I have been using SnapLogic for almost two and a half to three years.

SnapLogic is a good integration tool that enables connections with different platforms and third parties. The drag and drop interface makes it easy to use, and the customer success management provides excellent support. Knowledgeable customer success managers in various regions facilitate easy onboarding and long-term utilization of SnapLogic. I rate SnapLogic an eight out of ten because transit gateway capabilities would enable me to move to a fully cloud-based infrastructure, and this feature represents the primary area for enhancement.

Hybrid Cloud

Amazon Web Services (AWS)

My main use case for SnapLogic is integrating multiple systems.

For integrating those systems, I am using SnapLogic to integrate the whole order-to-cash flow where the quote originates or an opportunity originates from Salesforce, goes through a whole lifecycle with SAP and multiple internal systems, and comes back to Salesforce as an order for revenue generation.

In addition to integrating the whole order-to-cash flow, I am using SnapLogic to connect multiple systems including multiple APIs, DBs, MySQL DBs, Cloud Spanner DBs, GCP, Pub/Sub, Vertex AI, and APIs.

The best features SnapLogic offers include the inbuilt Snaps which you can use to create connections between multiple systems, and I would say that is one of the best features. The UI is very intuitive.

What I appreciate about the Pin Snaps is that they help me identify the correct Snap for each and every integration, and it is easy to drag and drop the Snaps into the canvas, which speeds up the integration considerably faster than traditional coding.

SnapLogic has positively impacted my organization by helping me increase revenue substantially by automating the flow between the order-to-cash, which I have already mentioned.

Since using SnapLogic, I have seen specific outcomes where creating an order or opportunity in Salesforce and syncing it to SAP, which used to take around a day to complete, is now being done in minutes.

SnapLogic can be improved, but as a product, it is a mature product now compared to what it was four years ago, and the team is improving with every release, so I am satisfied with that.

Even if it is a small thing, I have asked them to create a new public API to create pipeline versions programmatically, rather than creating it manually, as that will help us in our CICD process.

I have been using SnapLogic for around four years.

SnapLogic is very stable, much more than before.

SnapLogic's scalability on the cloud is very good, as I have seen nodes spin up automatically, and on every restart, a new node is spinning up; scalability on Groundplex is also fair, but it involves a few manual activities.

I would say customer support is on point; whenever I raise a query, they respond properly, and if I ask for a working session, they are always ready to hop on a call and help us with the issues, resolving it within a day or two maximum.

Previously, we were using microservices.

My experience with pricing, setup cost, and licensing was very smooth during negotiations with the team, and we recently renewed our contract, so it was a very smooth process overall.

I have seen a return on investment, as previously there were around five to six developers, but now we are using only one to two developers, which is a great return on investment.

Before choosing SnapLogic, we evaluated other options, including Boomi.

I would rate SnapLogic a 10 out of 10.

I chose a 10 because SnapLogic helps me automate multiple things, and I have automated a whole bunch of flows using SnapLogic that reduces much of my time and effort, which is why I gave it the highest rating, along with its ease of use.

Regarding SnapLogic's AI capabilities, I think its governance and security are good, as every chat is new and, similar to ChatGPT chats, every time I open a session, it is new and the previous sessions are not saved.

For SnapLogic's AI capabilities, I would say its accuracy is around 85 to 90 percent, and reliability is also around the same, meaning I cannot blindly trust the output and should always review and improve it if required.

My advice to others looking into using SnapLogic is that it is a very intuitive platform that is easy to use and set up, with a lot of capabilities to integrate with multiple systems, and with its AI capabilities, it has gone one step further as well.