definity

🚀 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐂𝐈/𝐂𝐃 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 🚀 Validating data code changes and platform upgrades/migrations to ensure data reliability and performance has always been a massive challenge for data engineering and platform teams, especially in Spark: ❌ Manual setup for testing/staging? Time-consuming and risky. ❌ Static code analysis? Limited, missing real-world issues. ❌ Small-scale tests? Insufficient for uncovering actual degradations. The result? Risky code changes leading to incidents and degradations, and lengthy platform upgrades/migrations delaying savings and business growth. Today, we’re excited to announce the newest addition to definity – 𝐂𝐈/𝐂𝐃 𝐓𝐞𝐬𝐭𝐢𝐧𝐠, enabling you to: ✅ Test pipeline changes in CI using real data – to emulate real-life scenarios. ✅ Seamlessly simulate pipeline runs before deployment – with no manual setup. ✅ Automatically profile data quality, execution health, and performance behavior. ✅ Proactively detect issues and root-cause them in 3-clicks – before they hit production. If you’re building Spark data pipelines or managing a Spark-heavy platform at-scale – comprehensive & seamless validation in CI can help you 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐫𝐞𝐝𝐮𝐜𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧𝐜𝐢𝐝𝐞𝐧𝐭𝐬. Combined with definity's ongoing, real-time, full-stack observability, you can now achieve dynamic 360 protection of your data & pipelines. 💡 See how it works at https://lnkd.in/gCXe4mJr or check our our blog for more details (link in the comments). #DataEngineering #CICD #DataObservability #Spark #definity

What the heck is full-stack data observability? 🧐 In the recent 2024 Market Guide for Data Observability Tools, Gartner calls out that data engineers need data observability that provides *holistic coverage throughout the “data stack” across five dimensions* – data quality, pipeline operation, usage, infra performance, and costs. These are not standalone dimensions – they are heavily coupled in the data operation. So for effective issue detection, root-cause, and remediation, as well as performance and cost optimization – it’s crucial to have a unified view of them all. But one of Gartner’s key insights is that data observability tools today only focus on monitoring data quality (in modern cloud data env). How can the solution space be so off with the market need? Well, the first generation of data observability is heavily indexed on connecting *outside-in* to cloud data warehouses and examining the data there at-rest - "understanding the system's state by examining its outputs". How can we expect it to provide coverage of internal and inherently dynamic dimensions that continuously change in run-time? To get holistic coverage we need *inside-out & in-motion visibility all throughout the stack* – from query through pipeline to environment level, so we can see how data quality, pipeline operation, usage, infra performance, and costs all operate together, in real-time. We need a change in approach. We need a new generation of data observability. We need full-stack data observability. P.S that’s exactly what we’re building at definity #DataEngineering #DataObservability #PipelineObservability

🕵♂️ An Agent in Every Pipeline 🕵♂️ Spark data applications (pipelines) are complex, with countless moving parts, and issues that can easily slip through the cracks, leading to major headaches and business risks. 🤕 We’ve all been there - traditional tools promise solutions but often fall short – requiring endless integrations and always leaving coverage gaps; monitoring pipeline as a black-box and at-rest; and not allowing you to actually control or fix your pipelines. But what if you could seamlessly have complete visibility and control over every pipeline – in-motion, with zero code changes? 🌟 Our latest blog post dives into how definity’s unique agent-based architecture revolutionizes data application observability. Ready to take your data engineering game to the next level? Read this (link in first comment) 🚀 #PipelineObservability #DataObservability #DataEngineering #Spark #definity

Spark-heavy data engineering teams all deal with one major problem: Spark is hard ! 🚨 Spark workloads are typically heavier and more complex, Spark pipelines often break and consume a lot of resources, and getting anything meaningful from Spark UI is like pulling teeth. The result? Continuous data reliability issues, pipeline breakages, and inefficient performance, leading to business loss, engineering time waste, and rising infrastructure costs. So why is it still incredibly challenging to maintain pipeline reliability and minimize downtime in Spark? 1️⃣ Lacking visibility into pipelines and data operation ❌ 2️⃣ Testing data is manual and takes a lot of effort ❌ 3️⃣ Issues are always caught too late ❌ 4️⃣ It takes too long to root cause ❌ 5️⃣ There are always wasted runs and resources ❌ What can Spark teams do? Software Engineers, SREs, and DevOps have a plethora of APM (application performance monitoring) tools to monitor anything around their applications, services, and infrastructure. But Data Engineers and Data Platform Engineers today lack similar tools to observe their software - the data pipelines! Sure, there are many data quality monitoring tools out there, but Spark teams rarely use them because these tools are designed for monitoring data at-rest in the warehouse, which is great for BI teams but not for a deep in-motion observability in Spark that helps keep data + pipelines + performance in check at a high scale and complexity. 🌟 Enter definity’s pipeline-native observability. The unique agent-based architecture lets you monitor and control everything your data pipelines do, *in-motion* and with zero code changes, across Spark, DBT, and more. If your team is struggling with Spark data reliability, pipeline breakages, and inefficient performance - let’s chat! 💪 Check out the full post at our blog - link in the first comment. #Spark #PipelineObservability #DataReliability #SparkPerformance

🏔 Looking forward to our f2f meetings with some very smart and forward-thinking data leaders at the #SnowflakeSummit and #DatabricksSummit over the next couple of weeks! ☕ Will you be there too? Let's connect in person! DM me or schedule a time here definity.click/y10ftm1 #DataCloudSummit #DataAISummit #DataPipelineObservability