Solomon Kahn

Founder & CEO at Delivery Layer | ex Nielsen P&L owner | ex unicorn SaaS VP of Data | Host of the Data That Drives Revenue Podcast

I'm a long-time data leader, currently leading ReportingApplications.com, a consulting company focused on building - you guessed it - Reporting Applications.

Why Reporting Applications?

Because they’re tricky to build in ways that most companies don’t realize until they are live with customers and dealing with unexpected business problems.

We believe there’s an art to building great reporting applications, but not many people have focused on this area deeply enough to become experts.

By working with us you can benefit from our many years of lessons learned.

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Prior to starting ReportingApplications.com I was the founder of Delivery Layer - a SaaS product that powered customer facing reporting applications.

Before that I was VP of Data Strategy at Helium 10 where I led all things data (Data Engineering, Data Science, Data Product Development and Analytics). I previously ran product & analytics with P&L responsibility for Nielsen Sports, the global leader in Sports Sponsorship data.
  
Early in my career I ended up with the mix of statistical, business analysis and programming skills that would turn into the ideal background for a data scientist. Since then, I've been leading data teams, building data products and running data businesses.

My philosophy is that data isn't the hero - it's the sidekick. It's only valuable if it improves the lives of your customers, or helps someone make better decisions.

Get in touch if you need to build a reporting application!

And connect and follow along for lots of content around data leadership, becoming a better data person, and (of course) Reporting Applications.

Founder & CEO

Delivery Layer is a platform that powers revenue generating data through web applications and APIs that deliver your internal data to customers and partners.  
  
We dramatically improve time to market for data products. Enterprise scale products can launch in under a quarter, and consulting project front-ends can ship in days.  

This is only possible because we focus exclusively on applications that deliver your internal data to customers (as opposed to when customers send data into your systems).
  
Our pre-built infrastructure and unique battle-tested experience allows us to move at blazing speeds.

At a conference this morning, I gave an AI agent company my name, LinkedIn profile & phone number so it could create an AI version of me that I could chat with over text. So far "AI Solomon" has texted me nine times about meeting up at the happy hour later this afternoon 🤣🤣🤣

Didn't even try chatting me up first about reporting applications. Had to block the little dude...

Sneak peek into the future of runaway AI agents stuck in a loop!!!

Data Leader | Founder @ Reporting Applications | ex Nielsen P&L owner | ex unicorn SaaS VP of Data | Podcast Host

I'm absolutely certain that chat will be the data interface for business users in an AI world, because chatting "can you just pull some numbers real quick" HAS ALWAYS BEEN THE INTERFACE for how business users get data!!!

If you or someone you love need to build a reporting application next year, wanted to put ReportingApplications[.]com on your radar. That's literally all we do - we're a small consulting company that builds reporting applications (internal, custom and embedded).

That's it - not every LinkedIn post needs a hero's journey :)

Built dataGOATs.org Community | Simplifying Trail Stewardship @ TrailAdvocate.app | Chair of WCTA | Helping People Solve Problems with Emerging Tech, Storytelling, and Connection

Tomorrow, we'll be chatting with Solomon Kahn on all things analytics leadership, delivering value, and building things!

Won't make the live?  Drop a question here: https://lnkd.in/g5ecW6D7

Roy Hasson

My first post on Microsoft Fabric blog highlighting an integration with #OneLake I'm really excited about - querying database backups powered by Apache Iceberg and Eon.io

A best practice is to replicate operational databases into your lakehouse or warehouse for analytics and AI. This drives much of the integration tooling ecosystem with lots of products and OSS tools, including connectors, CDC and replication engines. 

CDC is a common pattern for replicating data, but it requires custom tools, continuously running pipelines and constant management. Even more important that most vendors won't tell you, is the need for the source DBs to support CDC or even be capable of handling the workload without falling over. 

Many enterprises simply can't enable CDC so what are they to do?

In addition, as you replicate operational DBs, data is duplicated. How can you avoid more copies of the same data?

Database backups are commonplace and simply a hard requirement these days. Even for cloud-native DBs, backups are a must. This means that now you have your data in the original DB, backup files and replicated out to your lakehouse for analytics - that's 3 copies.

One of them, the backups, are for risk mitigation and aren't super useful beyond that.  

Enter Eon.io

A modern data backup solution that stores these backups using Iceberg in your lake. This makes them queryable from your engine of choice.  

At #MSIgnite we announced an integration between #OneLake and Eon that allow users to query their DB backups without building any ETL pipelines, turning on CDC or deploying any new tooling.  Simply backup your data as you normally do, with Eon, and it's queryable from Fabric or your choice of engine - OneLake automatically converts between DeltaLake and Iceberg so you can really use any modern engine.

Read the post -

Product @ Microsoft | Data engineer | Advocate for better data

Open article: How to Query Database Backups in Microsoft Fabric Without Copies or ETL | Microsoft Fabric Blog | Microsoft Fabric by blog.fabric.microsoft.com

How to Query Database Backups in Microsoft Fabric Without Copies or ETL | Microsoft Fabric Blog | Microsoft Fabric

One of the key features of Apache Iceberg is the ability to delete rows. Beyond the obvious, this allows orgs to comply with privacy regulations, but it's actually much harder in practice then most assume...

Technically speaking, Iceberg enables the ability and functions to delete rows, clean up old files and remove unused snapshots.  This means users can implement ETL processes to ensure compliance with privacy regulations and corporate policies.

However, doing this successfully still requires building and maintaining ETL jobs with complex logic, across lots of tables 😥 

As with zero-ETL, the less ETL we need to write and manage the better.  So why can't compliance also be automated?

It's really cool to see Ryft tackling this problem. Automating compliance enforcement for Iceberg based lakehouses is a game changer for every organization.

🔥 Don't need to learn how Iceberg handles retention, file deletion and snapshot expiration - simply define a desired retention policy and Ryft takes care of the technical details to implement it efficiently and quickly.

🔥 Retention is partition-aware, minimizing data scans for large tables.

🔥 Deletion is verified before it's performed to ensure misconfiguration or other mismatches don't result in accidently loss of data

Stay tuned for more to come from Ryft.  They are tackling the hard problems of making Iceberg lakes enterprise grade and scalable in a data platform independent way.

Open article: Announcing Ryft Data Retention & Compliance Enforcement for Apache Iceberg | Yuval Yogev by ryft.io

Announcing Ryft Data Retention & Compliance Enforcement for Apache Iceberg

Announcing Ryft Data Retention & Compliance Enforcement for Apache Iceberg | Yuval Yogev

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The Battle

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