Mapp Intelligence, powered by Webtrekk vs. MongoDB

Webtrekk is a good tool to get insights on your website and app usage. It is possible to get more information on your campaigns and engagements. If Webtrekk is in use with Mapp Engage, it is possible to manage your campaigns with one tool.
It is not possible to use Webtrekk as data lake, but Mapp is actually developing a Customer Data Platform.
Webtrekk (Mapp Intelligence) is a web analytics tool, so it has to be used as web analytics tool.

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If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.

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• Good visualization in reports for marketing and c-level.
• Part of the big picture Mapp Suite (Engage Intelligence Aquire).
• Integrated tag management.
• No sampled data.
• It is possible to combine 90% variables and metrics.

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• Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
• You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
• Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.

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• Webtrekk (Mapp Intelligence) should invest to go real time or near real time.
• Build more APIs to use 3rd-party data in Webtrekk and use Webtrekk Data in 3rd-party tools.
• Webtrekk should build better click-path-analysis and journeys.

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• An aggregate pipeline can be a bit overwhelming as a newcomer.
• There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
• Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.

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I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.

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NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.

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We are talking weekly to our Partner Manager. We are Webtrekk experts, so we don't use much direct support.

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Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.

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While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.

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Webtrekk is a German company, in the U.S. company Mapp. It is fully GDPR-compliant. It is not expensive compared to Google and Adobe.

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We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.

• Get insights into your web data.
• Use the marketing automation for easy use cases (save your money by not using expensive 3rd-party tools for simple marketing automation use cases).
• 1 tool for over 20 apps and 100 websites.

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• Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
• You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB