Mindfuel

Mindfuel is the pioneer of Data Product Management and developed the first SaaS for this category called Delight. 

Delight is the first AI & Data Impact Platform designed specifically for AI and data leaders and their teams in companies heavily investing in these technologies. It serves as a end-to-end management platform where organisations can collect and manage all business demands and data capabilities in one place, bringing together products, initiatives, projects and assets for greater transparency and efficiency.

- Create and execute the personal brand for the CEO of Mindfuel to drive growth on LinkedIn.
- Develop outbound strategy and messaging in English and German.
- Develop inbound sales assets = lead-generating content.

Growth Specialist

Starburst

recognitions:
> Global SDR of the Year of FY23 - 185% of YTD quota attainment 
> Global SDR Q3 FY23
> 2x 'Starwurst' of the Quarter - appreciation award from our CEMEA team

Starburst Enterprise is a data lake analytics platform that allows organizations to access and analyze data on the lake and additional connected sources, including cloud data warehouses, legacy and on-premise databases, and modern streaming and NoSQL sources, all through a single, unified interface.

Sales Development Representative

POINTLY

Pointly is a cloud-based and user-friendly SaaS to that enables fast training data generation for AI Use Cases. 

My main tasks:

Development of Pointly’s marketing from scratch and execution throughout all channels
- Social Media 
- Event Management
- Conception of the Website and content creation for it

Sales
- Demo Calls
- Preparation of presentations and pitch decks

Marketing and Sales

Supper & Supper GmbH

Supper & Supper is a full-service data science service provider, developing tailor-made solutions for machine learning, artificial intelligence, process optimization and digital transformation.

Business Development Manager

Work and Travel Australia

Waitress 

T-Systems International GmbH

Dual Student - Marketing, AppAgile (PaaS and Big Data)

Dual Student (Marketing), Internship

Data & AI Enthusiast | Attracting High-Quality Opportunities Through Personal Branding & Smart Outreach | German Expat Mastering Dutch Life

I’m Francie (you pronounce it like “fancy”) :P  — a Growth Specialist who’s diving into the world of personal branding, content creation and smart outbound. 

If you’re curious, here’s a quick rundown:

👋 Who am I? 
A bubbly, face-to-face extrovert learning to embrace the online world. My background? Everything from big corporations to tiny AI & Data startups. Now, I’m on a mission to translate that energy into driving growth online. *help*

🤝 What do I do? 
I help businesses create more high-quality opportunities using savvy outbound strategies, lead-generating content, and genuine personal branding. Fed up with outdated tactics? You’re in the right place!

💡 Why am I here? 
To share my journey — ups, downs, and all. I don’t have it all figured out, but I’m here to learn, experiment, and grow. If you’re on a similar path, let’s connect!

✨ What’s next? 
Expect tips, lessons, and insights on building your brand, writing content, attracting high-quality opportunities, and driving growth - especially in the data & AI space.

Thanks for stopping by! Let’s figure this out together. 😊

PS: Ever asked yourself "What if I start using LinkedIn properly?" I did, constantly - so here we are lol

The data stack you trust today may not survive your scale tomorrow 🫢 

LinkedIn’s recent architecture shifts tell an important story about where large-scale data systems are heading.

First, they moved from Kafka → Northguard after hitting hard limits at 32T events/day and 17PB/day. Partition-centric replication, centralized metadata, and heavy operational tooling simply couldn’t keep up. 

Northguard’s approach - sharded metadata, log striping, decentralized coordination, and self-balancing replication - solves problems Kafka was never designed for at that scale.

Now LinkedIn is shifting from Spark → Flink for batch workloads. 

And the connection isn’t accidental.

Once your log layer provides key-aligned ranges, strong consistency, natural reprocessing, and scalable metadata, batch processing doesn’t need a separate compute paradigm. 

Flink’s unified streaming/batch model fits the storage layer far more naturally than Spark’s shuffle-heavy, file-system-bound runtime.

The trend is clear:

➡️ As log and metadata layers become more elastic and deterministic, the compute layer must converge too - and Flink is built for that convergence.

It’s not about abandoning old systems; it’s about aligning storage and compute for the next decade of data scale.

Francie Kastl

If your “real-time AI” runs on batch data from yesterday, we need to talk. | Head of Global Sales Development | German potato enjoying the vibrant Netherlands :)

Ververica | Original creators of Apache Flink® was just named a Leader in The Forrester Wave™: Streaming Data Platforms, Q4 2025.

What this really means: enterprises no longer have to 
choose between power and flexibility.

Forrester highlighted our "high efficiency and scalability" 
for large-scale real-time workloads. 

Our customers are already seeing it. 

Fraud detection in milliseconds. 
IoT analytics that actually scale. 
Real-time systems that batch infrastructure can't touch.

The shift from batch to real-time isn't coming. 

It's already here.

And we're just getting started.

Access your complimentary copy of the report to see how Forrester evaluates the top vendors in streaming data platforms and why Ververica stands out. 👉 https://lnkd.in/dqcgmjrH

#ForresterWave #ApacheFlink #StreamingData #Ververica #RealTimeData

Apache Fluss 0.8 is here!

With nearly 400 commits, this milestone release brings next-generation streaming Lakehouse capabilities, tighter Apache Flink integration, and breakthrough performance for a streaming storage platform for real-time analytics. 🚀 

With the introduction of Delta Joins with Flink, this release redefines efficiency in stream processing by minimizing state, and maximizing speed. 

We’re proud to be part of this incredible release - with Giannis Polyzos, our Staff Streaming Product Architect, as a key contributor 🙌

Kudos to all the contributors who made this possible!

🌊 Learn more about how Fluss 0.8 powers a new era in streaming: real-time, unified, and zero-state 👀
https://hubs.li/Q03SlTTC0

We’re thrilled to share this amazing 5-star review from Booking.com in the Gartner Peer Insights Review! 🌟

At Ververica | Original creators of Apache Flink® , our mission is to empower organizations with enterprise-ready, resilient, and governed real-time data processing at scale and feedback like this fuels our passion even more:

“We are working with Ververica for more than 4 years now. Within the security department, Apache Flink and Ververica play a vital role in empowering security teams with real-time processing capabilities, thereby helping us identify and mitigate threats at scale with minimum operational overhead.”
— Group Product Manager, Booking.com

⭐ Overall Rating: 5.0

What stood out for Booking.com:

🔹 Enterprise-grade management of high-volume Flink workloads
🔹 Exceptional stability and governance through autoscaling, checkpointing, and savepoints
🔹 Deep Flink expertise, training, and support from our team
🔹 Strong product performance and services expertise that helped reduce time-to-market and drive operational efficiencies

We’re especially proud to support their on-premises deployments across the Netherlands and the UK, helping their security teams stay ahead of evolving threats in real time.

Thank you to Booking.com for trusting Ververica for more than four years and for the incredibly meaningful partnership. 🚀

5-10x faster sounds like marketing speak.

What it actually means in practice.

We rebuilt Apache Flink's execution engine from the ground up with VERA-X native vectorized processing. 

Here's what changes:

Before (standard Flink):
• Process events one at a time
• ~100K events/second per core
• High CPU overhead
• Complex queries = performance drop

After (VERA-X):
• Process events in batches (SIMD vectorization)
• ~500K-1M events/second per core
• 50% lower CPU usage
• Complex queries stay fast

Real-world example: 

A financial services customer was running fraud detection on 1 billion events/day. 
They needed 200 cores to keep up.

With VERA-X? 
40 cores. 
Same latency (< 10ms). 
80% cost reduction.

⬇️ 
That's not "5-10x faster" as a vanity metric. 

That's 5-10x fewer servers, 5-10x lower cloud bills, and systems that can finally scale to where you actually need them.

This is what happens when you stop optimizing around legacy architecture and rebuild for modern hardware (AVX-512, modern CPUs).

Performance still matters. 

A lot.

Black Friday 2025 🚀 is just around the corner, and online retail and ecommerce sites are getting ready for their most important sales period of the year. 

Filled with marketing campaigns, discounts, and sales strategies, businesses can leverage real-time data to dynamically adjust their offerings, recommend products, and provide the best customer experience for shoppers, all in order to stay relevant in a highly competitive market.

Apache Flink is the heartbeat companies need to enable instant insights into customer behavior, sales trends, and inventory levels. 

Flink’s low-latency analytics help track key KPIs, such as conversion rate, revenue per minute, and inventory turnover, thereby enhancing personalization and maximizing profits during high-demand Black Friday sales.

Here are the quick-win use cases companies can implement now to be ready for the virtual sales battle:

✅Customer Segmentation: Real-time data segments customers instantly based on behavior and preferences, enabling personalized recommendations and targeted marketing that leads to higher conversions.

✅Real-Time Sales Analytics: Track sales performance live, identify trends, and optimize pricing or promotions instantly, to boost revenue and efficiency.

✅Abandoned Shopping Cart: Instant cart tracking triggers timely reminders or discounts, re-engaging shoppers and recovering lost sales through personalized, real-time interventions

DM me "retail" if you'd like to get some more info :P

This Yelp success story is exactly why we coined the term “Streamhouse” back in 2023.

80% lower storage costs. Data freshness cut from 18 hours to 1 minute.
That’s what happens when you stop forcing teams to choose between real-time performance and lakehouse economics.

Traditional lakehouses were built for batch. Yelp needed something built for today: a streaming-first architecture that delivers real-time latency without blowing up complexity, cost, or operational load. That’s the Streamhouse.

What makes Streamhouse actually operationally different:
🔹 Streams-first foundation - no more giant batch jobs running catch-up
🔹 Live + historical data unified - one architecture, not a patchwork
🔹 Real-time performance at lakehouse costs - finally, no trade-offs

Yelp built theirs with Flink, Paimon, and MSK - and the operational gains were immediate: a simpler stack, faster feature delivery, easier onboarding via SQL, and a far flatter learning curve.

Where Apache Fluss completes the architecture:
Paimon and Iceberg handle historical persistence and governance. But the missing piece for operators has always been live data.

That’s Fluss - streaming storage built for real-time operations: streams treated as queryable tables, continuously updated and instantly available. 

With Fluss + Paimon/Iceberg, you get:
→ Fresh, queryable data always ready for real-time workloads
→ Efficient, compliant historical storage
→ A unified model - no stitching, syncing, or multi-system drift

This is the Streamhouse in practice: Flink for processing, Fluss for live data, Paimon/Iceberg for historical storage - one cohesive, predictable, operator-friendly platform. Keeping 1 copy of your data!

Yelp proved that streaming-first architectures scale globally without inflating operational burden. As more organizations follow this path, Fluss will be the key to keeping the architecture simple, fast, and cost-efficient.

Huge credit to Umesh Dangat and Toby C. for documenting the journey. If you’re modernizing your platform, this case study is absolutely required reading.

New to data streaming and Apache Flink?
Me (kinda) too. It’s dense. It’s technical. And the learning curve can feel… well, icy.

So I decided to unwrap it one day at a time. Literally.
And what better format than a cutesy, festive Advent Calendar?

🎄 Welcome to the North Pole Data Center 🎄
Introducing: My Apache Flink Advent Calendar
24 days of streaming concepts explained in the most Christmas-y way possible.

Think of Apache Flink as Santa’s real-time operations platform.
While everyone else is still working through yesterday’s Naughty/Nice list (batch processing), Santa already knows right now who just did something kind, or well… not so kind.

❄️ That’s streaming.
🎅 That’s Flink.

🧊 What is Apache Flink?

Apache Flink is an open-source framework for distributed stream processing. It handles both unbounded (streaming) and bounded (batch) data with:

> High throughput: Millions of events per second

> Low latency: Milliseconds, not minutes

> Fault tolerance: Automatic recovery

> Exactly-once semantics: No duplicates, no mistakes

> Stateful processing: It remembers context

> Event-time handling: Deals gracefully with out-of-order data

Companies like Netflix, Uber, Alibaba, and LinkedIn rely on Flink for real-time analytics, fraud detection, ML pipelines, and large-scale data movement. It’s one of the engines powering the modern real-time world.

🎁 The goal

Make streaming data as easy and fun to understand as popping open a chocolate Advent Calendar.

One day, one concept, one tiny window of Flink at a time.

(And don’t worry, I’ll explain stuff like ‘Exactly-once semantics’ in the coming days.)

If you’re also new to streaming and Flink, hit follow and learn along with me. ☃️

Understanding Apache Flink_Advent Calendar

Understanding Apache Flink® - Advent Calendar - Day 2

Imagine Santa's workshop. Toys don't sit around waiting to be made in one giant batch on December 23rd. (That would be chaos.)

Instead, toys flow through the workshop continuously. 

Wood arrives → elves carve → painters paint → QA checks → wrapping station → sleigh.

That's stream processing.

Apache Flink is the workshop manager. 

It coordinates every station, makes sure nothing breaks, and keeps toys flowing 24/7.

Batch processing? 

That's making all toys on one day. 
Stressful. 
Inefficient. 
Nobody wants that.

Streaming? 

Smooth, continuous, always ready.

What is stream processing?

Stream processing treats data as a continuous flow rather than a static dataset. 

Instead of collecting data, storing it, and processing it later (batch), stream processing analyzes data in motion, as it's generated. 

Think: sensor readings, user clicks, financial transactions, log events. 

Apache Flink processes these events with millisecond latency, applies transformations, aggregations, and business logic, then outputs results to dashboards, databases, or other systems. 

All in real-time. 

The goal: 

Process data the moment it arrives. 
Not tomorrow. 
Not in an hour. 
Now.

#streamprocessing #apacheflink

Understanding Apache Flink® - Advent Calendar - Day 7

Batch vs. Streaming = Making Cookies

Let's talk cookies. (The edible kind.)

Batch processing = Bake 100 cookies, wait for them to cool, frost them all at once, then serve. You eat cookies hours after mixing the dough.

Stream processing = Cookie dough enters the oven, cookies bake continuously, frosted the moment they're cool, and served immediately. Fresh cookies every minute.

Which sounds better?

Apache Flink does streaming. Continuous. Fresh. Real-time.
Most companies still do batch. Stale cookies. Stale insights.

Batch vs. Streaming: 

What's the real difference?

👉 Batch processing: 
Collect data over time (hours, days), then process it all at once. Think: nightly ETL jobs, daily reports, weekly analytics. Data is bounded (finite), processing happens after collection.

👉 Stream processing: 
Process data continuously as it arrives. Data is unbounded (infinite), processing happens immediately. Think: fraud detection, real-time dashboards, live personalization.

Batch = complete picture, delayed insights. 
Streaming = incomplete picture, instant insights.

The trend? 

Companies are moving from "batch by default" to "streaming by default" because waiting hours for insights means lost revenue, missed fraud, and bad customer experiences.

The goal: Get insights as fresh as cookies straight from the oven.

Understanding Apache Flink® - Advent Calendar - Day 4

Reindeer Routing = Event-Driven Architecture

Santa doesn't plan Rudolph's route in advance and hopes for the best. He adjusts in real-time based on weather, air traffic, and chimney availability.

That's event-driven architecture.

Apache Flink reacts to events as they happen:

> New order placed? Process payment immediately.
> Fraudulent transaction detected? Block it in milliseconds.
> User clicked checkout? Trigger personalized recommendations NOW.

Every event triggers an immediate action. 
No waiting. 
No batch windows. 
Just real-time reactions.

What is event-driven architecture?

Event-driven architecture (EDA) is a design pattern in which systems respond to events in real-time, rather than following predefined schedules or requests.

Key components:

> Event producers: Generate events (user clicks, sensor readings, transactions)
> Event stream: Transport layer (Kafka, Kinesis, Pulsar)
> Event processors: React to events (Apache Flink)
> Event consumers: Receive results (databases, dashboards, alerting systems)

Flink sits in the middle, consuming events, applying business logic, and producing new events or actions. This enables reactive systems that respond instantly to changing conditions, which is critical for fraud prevention, personalization, monitoring, and automation.

The goal: Build systems that respond to the world as it happens, not as it was an hour ago.

What events are you reacting to in real-time? Or wish you were? Maybe you can put it on Santa's wishlist :P

We're bringing the Apache Flink® Lab Day to Hamburg on December 9th.

This is a hands-on, half-day workshop where you'll build a complete streaming application from scratch, working through a real-world use case that covers data ingestion, real-time processing, and visualization.

Our Solution Engineer Alex Campos will guide you through the exercises. You'll need basic SQL knowledge and a laptop. Everything else runs in the browser.

What you'll get:

→ Practical experience with stream processing fundamentals
→ Direct access to Flink experts
→ A working real-time application by the end
→ Breakfast, coffee, and lunch included

Spots are limited. 

If you're ready to build with Flink, register now:

Open article: Apache Flink® Lab Day in Hamburg | Ververica. by ververica.com

Apache Flink® Lab Day in Hamburg | Ververica.

Hey Berliners 🐻 Join us for our upcoming streaming data meetup!

Whether you're in industry, academia, or anywhere in between - if you're passionate about real-time data streaming, this community meetup is for you.

On November 25th, Apache Flink® practitioners from T-Systems and JustEat will share real-world use cases, lessons learned, and what’s next for streaming data architectures.

🍕 After the talks
Stick around for free drinks, pizza, and networking with the Flink community. Swap ideas, share your streaming stories, and help shape the future of data processing 🚀

📍 Mindspace EventSpace (Zimmerstraße 78, Berlin)
📅 November 25, 2025 | 6pm - 8pm

Reserve your spot now 👉 https://bit.ly/49wgudC

85% cost savings over Apache Kafka.

That's not a typo. That's Apache Fluss.

I spent the last week delving into this technology, and honestly, it's transforming how I think about streaming architectures.

Here's the problem companies face:

You have Kafka for messaging.
You have Flink for processing.
You have a data warehouse for storage.

Three separate systems. Three sets of operational overhead. Data copied multiple times across your infrastructure.

It works, but it's expensive and complex.

So let's take a look at Apache Fluss... 

👉 Streaming storage that combines messaging, processing, and long-term storage into one unified platform.

Think of it as a real-time lakehouse, but actually real-time (not "refresh every 15 minutes" real-time).

What caught my attention:

→ 85% lower costs than Kafka (columnar storage + reduced data movement)
→ 100% Kafka API compatible (no rip-and-replace migration)
→ Sub-second queries on historical AND streaming data simultaneously
→ Zero-state analytics (offload stateful jobs to the storage layer)

The use case that made it click for me:

Imagine you're building a recommendation engine. Today, you need:
- Kafka to ingest user events
- Flink to process and enrich them
- A feature store to serve ML models
- A data lake for historical analysis

With Fluss, it's one system. Events are stored in queryable tables. Your ML models read directly from the stream. Historical analysis is conducted on the same data and infrastructure.

No copying. No syncing. No "eventually consistent" headaches.

The catch:

It's new. Apache Fluss is an emerging technology. If you need battle-tested, decade-old systems, stick with what you have.

But if you're architecting for the next 5 years, this deserves a serious look.

I'm still learning this tech, but the potential is clear. Especially for AI/ML workloads that need fresh data constantly.

Apache Fluss on Ververica

Fraud never waits.
And neither can the systems that fight it.

If you have spent time keeping real time pipelines alive,
you already know the moment latency creeps in,
fraud slips through the cracks.

Most teams try to patch the problem with more compute.
Or more services.
Or more monitoring rules.
But that only works until the next spike hits.

What the best engineering teams are doing instead is simple.
They are moving from reactive fraud detection
to continuous real time processing.

It means every transaction is evaluated the moment it arrives.
It means exactly once guarantees you can trust.
It means scaling without rewriting half the pipeline.
And it means fraud teams get signals fast enough
to stop damage before it turns into a headline.

Real time specialists know this better than anyone.
When your stack is built on a unified streaming foundation,
fraud detection stops being a fire drill
and becomes a predictable, measurable system.

The companies leading the way are the ones
treating real time as the default,
not the upgrade.

Understanding Apache Flink® - Advent Calendar - Day 3

🎁 The Gift Wrapping Assembly Line 🎁
Including a gift for you if you read till the end :P

You know what gift wrapping and data transformation have in common? (Sadly, following is not a dumb joke^^)

Both turn something raw into something beautiful and usable.

In Apache Flink, raw data enters (unwrapped gifts). 

Flink applies transformations; filtering, enriching, aggregating, joining, and out comes perfectly packaged data ready for analytics, ML models, or dashboards.

Here's the magic: 

Flink wraps gifts (transforms data) while they're moving. 
No stopping the conveyor belt. 
No delays.

Traditional batch systems? 
They pile up all the gifts, THEN wrap them. Hours later.

Flink? 
Wraps in milliseconds while data flows through.

What is data transformation in streaming?

Data transformation changes raw data into a usable format while it's in motion. 

Common transformations include:

> Filtering: Remove irrelevant events (e.g., filter out bot traffic)
> Mapping: Change data structure (e.g., extract fields from JSON)
> Enriching: Add context (e.g., join user ID with profile data)
> Aggregating: Calculate sums, averages, and counts over time windows
> Joining: Combine multiple data streams

Flink applies these transformations on the fly with low latency, meaning you get clean, enriched, and ready-to-use data in real-time.

The goal: Transform data in motion, not at rest.

🎁 Time for your gift: If you want to level up your Apache Flink expertise before the new year, join our online Bootcamp from 8-12 December with 30% off :) 
Find the discount code in the carousel below (click thru it) + and use it here: https://lnkd.in/dvjf73RF

Apache Flink Bootcamp - Discount Code

Understanding Apache Flink® - Advent Calendar - Day 6

The Advent Calendar = State Management

Here's a fun one: This advent calendar IS a stateful system.

Every day, you open a new door. But you don't forget which doors you already opened. That memory? That's state.

Apache Flink manages state like a pro. 
It remembers:

✔️ How many orders did a customer place this week
✔️ Running totals for fraud detection
✔️ Session data for personalization
✔️ Aggregations over time windows

And it does this across billions of events per second. With fault tolerance. At massive scale.

What is state management in streaming?

State is data that Flink remembers between events. 

Examples:
> Keyed state: Per-user shopping cart contents, account balances
> Operator state: Offsets for Kafka partitions
> Broadcast state: Rules or configurations applied to all events

Flink stores state locally (for speed) and backs it up to durable storage (for fault tolerance). This enables you to build complex, stateful applications, such as fraud detection, session analysis, or real-time recommendations, that respond to patterns across millions of events while handling failures gracefully.

The goal: Remember what matters while processing data in motion.

The Naughty/Nice List = Real-Time Analytics

Santa's naughty/nice list used to be updated once a year. (Batch.)

Now imagine it updates in real-time. Kid shares their lunch? Nice list. Kid steals a cookie? Naughty list. Instant updates.

That's real-time analytics powered by Apache Flink.

Businesses use this for:

- Fraud detection (catch bad actors immediately)
- Dynamic pricing (adjust prices based on demand NOW)
- Personalization (recommend products based on what user just clicked)

Milliseconds matter. By the time batch systems update, the moment is gone.

What is real-time analytics?

Real-time analytics refers to the process of analyzing data and producing insights immediately, typically within milliseconds to seconds of data generation. Instead of waiting for nightly batch jobs, you get:

✔️ Live dashboards that update constantly
✔️ Instant alerts when anomalies occur
✔️ Immediate actions triggered by events (block fraudulent transaction, adjust price, send notification)

Flink powers real-time analytics by processing streaming data, aggregating metrics over time windows, detecting patterns, and feeding results to visualization tools, databases, or downstream applications. You see what's happening NOW, not what happened yesterday.

The goal: Make decisions based on what's happening right now, not yesterday's report.

What decisions would you make differently with real-time data?

If you're selling to developers, Reo.Dev just launched a course specifically designed to help GTM teams navigate through the complexities of selling to a highly technical audience - definitely recommend checking it out! :)

Co-founder, Reo.Dev | Prev: Co-founder, Scholr (Acquired by BYJU'S) | IIT Kharagpur

“BUT HOW DO YOU ACTUALLY SELL TO DEVELOPERS WITHOUT ANNOYING THEM?” After 2+ years of learnings - we finally have an answer! Presenting, for the first time ever:

The DevGTM Academy - a comprehensive learning program designed to navigate the complexities of selling to technical buyers!!

Over the last two years, I’ve spoken to more than 3,500 folks in Developer GTM, DevRel leaders - and one thing is painfully obvious:

GTM teams are expected to sell to deeply technical buyers without ever being actually trained for it.

Developers evaluate differently.
They go deep.
They explore alone.
They test, break, rebuild, and form opinions long before they ever talk to a salesperson.

And most teams trying to sell to them are still using SaaS playbooks that were never designed for developer-first companies.

That’s why we created this program!

Last week in Atlanta, we talked about it for the first time - and people in the room felt so seen - because the challenges of selling to developers are as stubborn as they come!

This course brings together insights from some of the best DevTool operators, founders, and practitioners - people who’ve lived these GTM challenges for years now!

The amount of thought, iteration, and collaboration poured into this is something I’m genuinely proud of.

Inside, you can expect to learn:
-- how to identify real, time-bound buying intent
-- how to craft outreach that gets CTOs & engineering leaders to respond
-- how to use AI to speed up research, prospecting, and contextualization
-- how the best DevTool companies structure GTM motions
and frameworks already used by 100+ DevTool teams

You can start today with the self-paced modules!

And in February 2026, we're kicking off the first live cohort - limited seats, because we want this to stay practical, hands-on, and community-driven.

You can sign up by clicking on the link in the comments below.

A huge thank you to everyone who helped shape this -
Adam Frankl, Sanjeev Sisodiya, Aron Schuhmann, Luke Bivens, Ophir Prusak, Damian Sieczkowski, Fabian Wilckens

and a special shout-out to Cecilia Stallsmith - your episode “7 Go-To-Market Principles for Developer Products.” on WorkOS is a must-see!

Also, we're so grateful to everyone who's perspectives have strongly cemented our thoughts in curating these learnings! - Geertjan Wielenga, Orlando Nieves, Dima Grossman, Jakub Czakon 🍐, Henry Bassey, Karl Hughes, Rodolfo Yiu, Kanika Pandey, Peggy Rayzis, Enric Baltasar 🚀, Utpal Bhatt, Shai Alani, & so many more!

GTM teams deserve an edge when selling to technical buyers - not guesswork, not shallow signals, not “hope it sticks.”

More to come - and really excited this is finally out in the world.

Let’s raise the bar for Developer GTM!!💪 🔥

Understanding Apache Flink® - Advent Calendar - Day 5

Christmas Lights & Fault Tolerance 

Ever notice how modern Christmas lights stay on even when one bulb dies?

That's fault tolerance. And Apache Flink has it built in.

When a server crashes (and they do), Flink doesn't lose your data or stop processing. It automatically recovers, restarts from the last checkpoint, and keeps going.

No data loss. 
No manual restarts.
No panic.

It's like having backup Rudolphs ready to jump in if one gets tired.

Old batch systems? 
One failure = start over. 

Flink? 
One failure = barely notice.

How does Flink's fault tolerance work?

Flink uses checkpointing to periodically save the state of your streaming application to durable storage (like S3 or HDFS). If a node fails, Flink automatically:

✔️ Detects the failure
✔️ Restarts the failed task on another node
✔️ Restores state from the last checkpoint
✔️ Resumes processing from exactly where it left off

This happens automatically, with no manual intervention. Combined with exactly-once semantics, you get both reliability and accuracy. Your pipeline stays up, your data stays safe.

The goal: Build streaming pipelines that never go down, even when infrastructure hiccups.

Have you dealt with pipeline failures? How painful was the recovery? 😅

taplio

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