You don't like the Spark UI?
We heard you!
We're releasing Delight - a free & cross-platform monitoring dashboard for #ApacheSpark.
https://t.co/iD9bUZ8U3i
Backed by an #opensource agent, It works on top of any Spark platform - Databricks, EMR, Dataproc, CDH/HDP, ...
🌟 Introducing Jitter for Teams!
We've supercharged Jitter with teams & collaboration features, and it is about to transform your creative process:
⚡ Real-time editing
⏱ Collaborative timeline
🔄 Shared workspace
⚙️ Admin tools
Create together 👉 https://t.co/2TGhXb4etp
By opening up Komiser to the open-source community, we gain access to the collective knowledge and expertise of developers from around the world. This enables us to tackle the complexity of supporting a vast array of services and cloud providers.
https://t.co/eKxY2qSmbB
Introducing the all-new Jitter! 💫
With a completely new UI, tons of new features, and one mission: make motion design accessible to anyone.
Check it out at https://t.co/l3bxy7VCKR
Our Data + AI summit talk on Running #Spark on #Kubernetes reliably on #spot instances is now on Youtube: https://t.co/FKp51rokcM
1) Spark driver => On-Demand, Executors => spot.
2) Avoid 80% of spot kills by optimizing spot node selection
3) Handle executor kills gracefully
Delight, our free monitoring dashboard for Apache Spark, just got a big upgrade: https://t.co/NM5acWfhGf
Delight gives you access to the Spark UI (we run the Spark History Server for you) and key system metrics (CPU, Memory, I/O) to help you understand the performance of Spark.
Data + AI Summit is a wrap! If you couldn't attend our session, we just released the same content as a blog post - How to run Spark on Kubernetes reliably on spot instances :) ! https://t.co/4ZZoHIkAL2
Met long-time online friends @JyStephan and @DumazertJulien from @DataMechanics_ finally! 💪
It's the first time when we could give a hug each other! 😉
Merci beaucoup mes amis pour notre bien accueillir ♥️
#DataAISummit
Ocean for Apache Spark, our fully managed, continuously optimized, Spark-on-Kubernetes service, is now available on Google Cloud. It's deployed on a GKE cluster in your GCP account.
Learn more:
https://t.co/2ApeajnrVR
Congrats to the team!
#AWS ✅
#GCP ✅
#Azure: coming next!
In this tutorial we show you how to run Jupyter Notebooks on our serverless Spark-on-k8s offering, Ocean for Apache Spark:
https://t.co/DHu0ZOBRSA
PS: In addition to Python notebooks, Scala notebook are now in private preview - reach out if you're interested!
Ocean for Apache Spark is now officially GA on AWS: https://t.co/s4GEsESyVE
Main benefits of running Spark on k8s with us: intuitive UI, powerful automations enabling big savings, integrations with jupyter & airflow, ...
Very proud of our team! GCP & Azure are coming next :)!
This step-by-step tutorial shows you how to schedule Spark pipelines with Airflow on our serverless, Spark-on-Kubernetes service, Ocean for Apache Spark:
https://t.co/lzhsVgcvgM
We'll be doing a live demo of this in our webinar next week, sign up at: https://t.co/c6eVcFWcua
Since the Spark 3.2 release, the S3 Magic Committer is a lot more easy-to-use, performant, and stable. It enabled a 60% performance boost in our tests!
Learn more in this technical blog post about #ApacheSpark performance tuning on #AWS#S3
https://t.co/fTf1rDjiO3
Join us at our first webinar on how to get started with Apache Spark on Kubernetes!
In this technical talk we'll show the differences between running Spark on k8s vs other architectures. We'll also share some of the secret sauce behind Ocean Spark!
https://t.co/83wyqf1JyO
6 months after our acquisition by @spot_by_netapp, we're launching the joint @DataMechanics_ + Spot product called Ocean for Apache Spark.
We make Spark on k8s more developer-friendly and cost-effective.
Proud of our global team who made this possible!
https://t.co/reQqvWke0A