Production-aware AI copilot
for Apache Spark
DataFlint reads your Spark logs and plans, pinpoints bottlenecks, and proposes IDE fixes. Monitors jobs and surfaces optimization opportunities and cost savings—so teams ship faster with .Big data is complex, and bottlenecks slow your team.
Overwhelming interfaces
Spark web UI and related tools have complex, unintuitive interfaces.
AI tools that create noise
AI code editors lack the data context to offer accurate suggestions.
Overloaded Data Teams
Your data experts have limited resources to support those who need help.
From Spark Complexity to Automated Fixes
Those overwhelming Spark UIs, context-free AI tools, and overloaded teams kill velocity and burn budget. DataFlint's production-aware AI understands your actual workloads and infrastructure to automate the fixes you'd normally spend days implementing.
Key Capabilities
Current State
(Manual process)
DataFlint OSS
(Free & open source)
DataFlint SaaS
(Enterprise solution)
Time to root cause (MTTR)
Manual log diving & guesswork
Enhanced single job analysis
Apache Spark AI copilot
Real-time monitoring & observability
Limited single job visibility
Rich metrics & visualizations
Full pipeline observability
Transform hours of manual debugging into minutes of precision optimization with DataFlint's 100x compressed production logs and AI-powered insights.
DataFlint transforms every team member into a big data expert
"DataFlint has been a game changer in Spark observability for Intel Granulate and I`m glad to see it`s the case for Amazon Web Services (AWS) as well"
"great job Meni Shmueli and Daniel A. ! Proactively monitoring spark metrics to derive actionable insights is super important and often overlooked "
"If you`re managing Spark clusters — whether on-prem, in Kubernetes, or in the cloud — DataFlint makes it significantly easier to monitor, troubleshoot, and optimize workloads. Lightweight, open-source, and productivity-focused."
"I was using Dataflint a lot in the last few weeks for the optimization of aggregate tokens job. Combining my ideas for optimization and Dataflint suggestions the time went from 1:50 to 1:30 and the cost of the job went from 260 dollars daily to 110. If the costs continue like this then yearly savings is around 55000 dollars!"
"Amazing product!—we`re using it widely at Wix"
“We`re deploying the new version...”
“That`s great news! This is such a great replacement for the Spark UI. Seamless to setup and packed with data that actually makes sense.”
“Great news! When you have a hunch about the performance of your spark job, it is great that DataFlint backs your hunch with all the metrics and alerts. Much more easier to pinpoint room for inprovements with DataFlint now!”
“Will start experimenting with the new version ASAP. From my past experience the ability to view realtime and post execution is so much better than regular spark UI it`s comfortable and faster with great insights ”
“Super helpful for our DE team 💪🏻”
"DataFlint is a must-have if you are running Apache Spark!"
"DataFlint is really a game changer to me. When we are working on Lakehouse project with Apache Spark, it had been a pain to debug, but DataFlint has improved our experience with it. Super amazed with it!"
Key Features: Optimizing Your Spark Lifecycle
See how teams like SimilarWeb achieved dramatic performance improvements and cost reductions with DataFlint
Single job optimization: Results shown represent one critical Spark job out of many in SimilarWeb's data pipeline
DataFlint has been instrumental in helping us achieve engineering excellence in our Apache Spark workloads. Using their platform, we were able to perform deep diagnostics on our Spark jobs, uncovering inefficiencies such as skewed joins, underutilized executors, and suboptimal shuffle operations. Their automated insights and recommendations enabled us to fine-tune resource allocation, optimize Spark configurations, and reduce job runtimes significantly.
Trusted by
industry Leaders
Works everywhere you run Spark
DataFlint seamlessly integrates with all major Spark platforms, from cloud services to on-premises deployments.
Enterprise-ready deployment in minutes
AWS EMR
Amazon Elastic MapReduce
Databricks
Unified Analytics Platform
Google Dataproc
Managed Spark and Hadoop
Microsoft Fabric
Unified Data Platform
Kubernetes
Container Orchestration
On-Premises
Self-Managed Clusters
Product FAQ’s
Because ChatGPT writes code in isolation, DataFlint's AI copilot has context and writes code that actually runs fast on your cluster.
- Production aware intelligence - Every suggestion is informed by your live DAG, performance logs, and cost metrics, so advice isn't just "valid Spark," it's fast Spark for your workload.
- Continuous optimisation loop - DataFlint stays attached after deployment, learning from run-time performance and automatically surfacing new tweaks as data grows and patterns shift.
- Full observability - Streams real-time Spark metrics and costs into a single dashboard, flagging slowdowns and anomalies the moment they appear.
Use DataFlint when you need Spark code that's production ready and cost efficient, all from your IDE.
DataFlint offers several key benefits:
- Faster Issue Resolution: Instantly performs root cause analysis for failing Spark pipelines.
- Optimized Performance: Provides code suggestions to optimize join strategies, resource allocation, and more, leading to significantly faster execution times (e.g., 13x faster in our Similarweb case study).
- Reduced Costs: Helps cut infrastructure costs dramatically by identifying inefficiencies (e.g., 100x cost reduction in the case study).
- Increased Team Velocity: Empowers your data team to ship data pipelines faster and more reliably, boosting overall velocity and impact (aiming for 10x improvement).
- Enhanced Observability: Offers a control center for immediate visibility into failing jobs, performance bottlenecks, and cost metrics.
DataFlint is designed for broad compatibility. It integrates with:
- Spark Platforms: AWS EMR, Databricks, Google Dataproc, Microsoft Fabric, Kubernetes, and on-premises clusters.
- Storage: S3, Azure Blob Storage, Hadoop HDFS, Google object storage.
- Orchestration: Airflow, Databricks Jobs.
- IDEs & Tools: VScode, Cursor, IntelliJ for code suggestions.
- Observability: DataFlint Provides a SaaS UI dashboard and integrates with Slack and Managed Spark History Server.
DataFlint prioritizes your data security and privacy. We monitor and analyze Spark logs, which are performance logs detailing job execution metrics and system events, not your underlying business data. This focus on operational metadata means there are minimal privacy concerns related to sensitive information. Furthermore, DataFlint is AICPA SOC 2 compliant, demonstrating our commitment to robust security controls and practices.
