msager
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Welcome to the Gold Star to the Winner Challenge - Halloween 2025 Edition! ⭐️
From time to time I send out to my team at SnapLogic fun pipeline building challenges that Expression Enthusiasts may enjoy solving. We have decided to open it up to the broader Snaplogic Community. The Gold Star to the Winner Challenge Halloween 2025 Edition is the spookiest challenge of the year. Your job will be to cast a powerful spell in the form of an expression to tame some monstrously messy data. As usual, this challenge is from a real world use case. It centers on schemalessly transforming ‘somewhat’ structured data into a perfectly structured, “OCD-approved” format. The Details: In the following dataset, there are two keys: “Name” and “Path”. The Trick is to craft an expression that can magically break apart the Path string into separate keys, numbering them sequentially (pathelement_1, pathelement_2, etc.).For example: a path with 3 elements in it would transform to 3 json keys:Input JSON: { “Path”:“my drive/matt/customers” } Output JSON: { “pathelement_1: “my drive”, “pathelement_2": “matt”, “pathelement_3": “customers” } Here’s the raw input to be put in a JSON Generator: [{"Name":"Fred","Path":"spooky/graveyard/tombstones/fog/cackles/witches/brewing/potions/spells/hauntedhouse.jpg"},{"Name":"Wilma","Path":"kids/yard ornaments/ghosts/goblins/monsters/jack o lantern/leaves/cocoa/chill/candysacks/excitement/pumpkins/tricks/treats.png"},{"Name":"Pebbles","Path":"shadows/bats/moonlight/screams/night/costumes/party.mp4"},{"Name":"Dino","Path":"creepy/cornfields/scarecrows/spiders/webs.gif"}] And the expected output: [{"pathelement_1":"spooky","pathelement_2":"graveyard","pathelement_3":"tombstones","pathelement_4":"fog","pathelement_5":"cackles","pathelement_6":"witches","pathelement_7":"brewing","pathelement_8":"potions","pathelement_9":"spells","pathelement_10":"hauntedhouse.jpg","Name":"Fred"},{"pathelement_1":"kids","pathelement_2":"yard ornaments","pathelement_3":"ghosts","pathelement_4":"goblins","pathelement_5":"monsters","pathelement_6":"jack o lantern","pathelement_7":"leaves","pathelement_8":"cocoa","pathelement_9":"chill","pathelement_10":"candysacks","pathelement_11":"excitement","pathelement_12":"pumpkins","pathelement_13":"tricks","pathelement_14":"treats.png","Name":"Wilma"},{"pathelement_1":"shadows","pathelement_2":"bats","pathelement_3":"moonlight","pathelement_4":"screams","pathelement_5":"night","pathelement_6":"costumes","pathelement_7":"party.mp4","Name":"Pebbles"},{"pathelement_1":"creepy","pathelement_2":"cornfields","pathelement_3":"scarecrows","pathelement_4":"spiders","pathelement_5":"webs.gif","Name":"Dino"}] Solution approaches: There are many ways to skin this cat; highlighting the flexibility of the SnapLogic platform. My solution contains a single expression in a mapper. Others (the purists) have solved this by configuring and connecting many transform Snaps. All solutions are good as long as the solutions matches the above expected output and is done in a completely schemaless way. The Prize: The winner will receive recognition in the form of SnapLogic Swag (👕🥤🍾 🎁...). The rules: To keep the playing field level, send solutions directly to me via email (msager@snaplogic.com) and attach your pipeline .slp file. (i.e. we don't want to give solutions out on this post for others to see) Contest ends on 10/31/2025 Good Luck to All! I look forward to seeing your solutions.24Views1like0CommentsInfor Visual 7
Hello, We are trying to integrate from Coupa (expenses that are OK to Pay) to the Infor Visual ERP. This is an old version of Visual (2011) and there are no APIs. Our approach is to go directly to the underlying SQL Server Database. Does anyone have any expertise with this version or Visual and, if so, provide guidance as to what tables would need to be updated?940Views0likes0CommentsRegex Primer Pattern
Created by @msager This pipeline pattern shows how regular expressions can be used in SnapLogic and common examples with 10 use cases therein e.g. Basic Matching, Greedy vs Non-greedy, Grouping, Ranges, Alternation, etc. Configuration Examples include: Basic Matching Global Match Alternation Global Match with case insensitivity Range sets with quantification Range sets with quantification and global Grouping Greedy Non-greedy Non-greedy with grouping Sources: JSON Generator Targets: Outputs results of Regex Snaps used: JSON Generator, Copy, Mapper Downloads Regex Patterns.slp (18.0 KB)3.6KViews1like0CommentsElastic Mapping with a simple Script Snap
An Elastic Mapper which outputs JSON that only contains keys that have non-null values Background: The use case came while trying to map fields from a Salesforce Outbound Message. This message would send a varying number of fields, from each record, each having non-null values. For example, the SFDC Object had 50 fields in total and one record would return a document with 35 of the 50 possible fields populated and the next record would send 25 and so on; the data is then moved to another database that is pre-populated with data for each record. The problem was that using a Mapper, all 50 fields would need to be mapped in advance, and therefore those fields with NULL values would potentially overwrite data when updating the database. Our Mapper is static. Target Schemas need to be defined up front. The problem to solve was how to create a Mapper that filters out the non-null fields from each input document so that the update can occur without overwriting data with the null values. The Magic: All you need is ONE line of code added to the ‘try’ block of the script snap (javascript in this case) that removes from the input map any elements that have null values: … this.log.info("Executing Transform Script"); while (this.input.hasNext()) { try{ // Read the next document, wrap it in a map and write out the wrapper var doc = this.input.next(); while (doc.values().remove(null)); var wrapper = new java.util.HashMap(); wrapper.put("original", doc); this.output.write(doc, doc); //ß Optional note: I modified this to flatten the output this.log.info("Transform Script finished"); } … ** In Python you can use this construct in two lines (please note the indentation required in python): while (in_doc.values().remove(None)): None See attached test case pipeline: Limitations: This was created to work only with flattened, non-hierarchical structures. **Elastic Mapping_2017_03_01.slp (9.6 KB)6.4KViews2likes5Comments