Quantexa raises 160m, Fabric Woes, Snowflake revs at $4bn #74 w/e 7 Mar 2025
Join the 5,200-strong data herd getting all you need to know about Data for your Friday roundup
There are going to be some big big things over the coming weeks. Please share and subscribe if the below are of interest
Streaming Wars V2 - the streaming wars are heating up. is this 2015 all over again? We’ll be bringing you an exclusive write-up with inside info from across the data space
PRICING INCREASES - we have uncovered that there is a concerted effort across multiple vendors to increase costs, and we think we know who is next. That’s why we’ll be putting out a GUIDE To saving csots and protecting your P&L
Orchestra deployment environments- if you’ve ever had to manage multiple airflow instances or kubernetes clusters you know how painful it is. Fortunately, Orchestra now offers a way to get around this issue; powerful, elegant.
Want to get more news like this delivered into your inbox? Subscribe now
Continuous end-to-end Integration in Orchestra
Early this week we made two big announcements
We’ve launched our git-action which enables end-to-end continuous integration and deployment on Orchestra
We announced availability of C-Data in the Portal
The Benefits to using Orchestra CI/CD vs. a legacy, monolithic framework like Airflow or Prefect is immanse:
There is only one set of infrastructure to manage, which removes the need for teams to handle dependencies and requirements in different environments. This is especially true with managed Airflow services such as MWAA, where upgrading versions can be difficult and requires careful planning.
CI/CD becomes simple. By separating Orchestration logic, when there are changes to orchestration logic CI/CD ensures only that is tested. When there are changes to dbt, you can just test the dbt aspect. When there are changes to orchestration logic, you can just test that etc.
CI/CD becomes fast. Instead of relying on slow, monolithic Airflow clusters to test rudimentary things, Data Teams get faster feedback loops and can test more quickly / not get blocked by PRs.
Cost is reduced. Engineers do not need to maintain multiple instances of the same thing, which reduces cost. The current approach in a federated “hub and spoke” model (where different teams get access to the same infrastructure to self-serve) is particularly costly here.
Risk is reduced in skillset. Configuration files are straightforward to manage, so the requirement for specialist, niche devops skills is reduced.
Bottlenecks are eliminated: somewhat unique to Orchestra, but by having a Control Plane instead of just an orchestrator, anyone can view the results of CI/CD runs in a governed manner (you don’t want to give the finance team access to an Airflow UI, but you can give them read-only access to Orchestra) which means anyone can debug and maintain. You won’t need a central platform
Meme Drop
When your boss tells you to implement AI
Medium 🧠
🧠 Data Engineer Interview Experience (3+ YOE) At Tiger Analytics (link)
🧠 Orchestra vs. Astronomer in 2025 (link)
🧠 Streaming LLM inference with structured JSON output via Snowflake Cortex (link)
🧠 How to implement CI/CD flows for Data and AI Orchestration processes (link)
🧠 How to Spot and Prevent Model Drift Before it Impacts Your Business (link)
🧠 Apache Spark Repartitioning: When And How To Optimize Performance (link)
LinkedIn🕴
🕴 Tracking Table Changes in BigQuery: Exploring Change History & CDC (link)
🕴 Munich Talk: Data Engineering & an American’s Take on the EU AI Act (link)
🕴 Monetising Data: The Path to Profit (link)
🕴 Arcee AI Turns 1! Celebrating with the Women in AI Podcast (link)
🕴 Your CI/CD is Broken—Here’s How to Fix It with Serverless Orchestration (link)
🕴 How TS Imagine Uses AI to Scale Data & App Delivery Like a FinTech Giant (link)
News 📰
Editor’s Pick
📰 The Top IPO And M&A Candidates In Cloud And AI Infrastructure For 2025 - The markets are upbeat about potential IPOs and M&A in 2025, and technology infrastructure companies should be top of the list for public offerings. Digital and AI infrastructure is where the growth is, and investors crave growth… Read More
📰 AI training data provider Turing closes $111M investment - Turing Enterprises Inc., a provider of training data for artificial intelligence models, today announced that it has closed a late-stage $111 million funding round… Read More
📰 Quantexa raises €163 million to use AI to unify siloed data - London-based Quantexa, an innovator in Decision Intelligence (DI) solutions for the public and private sectors, announced today that it has completed a €163.3 million Series F investment round… Read More
Snowflake Revenue reaches $4bn (link)
YouTube and Podcast 🎥
Editor’s Pick
🎥 How to run an e2e ELT Pipeline using Orchestra and CData (link)
🎥 Unleash Data Governance with Microsoft Purview & Microsoft Fabric (link)
Special 💫
💫 Cutter Associate’s 2025 trends for Data Platforms (link)
💫 The Cloud Judgement newsletter (link)
Jobs 💼
💼 Data Scientist - Intern at CompanyCam (link)
💼 Analytics Engineer at Infinite Lambda (link)
💼 Analytics Engineering Lead at HawkEye 360 (link)
💼 Senior Analytics Engineer at General Motors (link)
💼 Financial Analyst - GTM CompanyCam (link)
Want to save on your ingestion bills? You’ll love this
You can leverage Python for lightweight ELT integrations. Here you’re only paying for compute and not being penalised by row-based pricing models. Pretty neat right? Check it out below / head to Orchestra and start today.
The best place to run dbt?
Don’t believe us? Watch the video below.