Telmai

𝟮 𝘄𝗲𝗲𝗸𝘀 to deploy, 𝟯 𝗵𝗼𝘂𝗿𝘀 to profile and validate complex critical data assets, and less than 𝟭 𝗱𝗮𝘆 to get actionable insights as well as generate tickets to address DQ issues. These figures represent the key success metrics ZoomInfo achieved after integrating Telmai’s data quality framework into their workflows. ZoomInfo leveraged Telmai to build a scalable architecture with automated data quality monitoring across 1 billion data records with 268 nested JSON attributes spread over a heterogeneous data architecture that includes open table formats like Apache Iceberg as well as systems like Snowflake and Google Bigquery. Record-level anomaly detection ensured their pipelines remained reliable, accelerating ZoomInfo's time-to-value. Interested to know more? Click on the link in the comments section to access the complete case study. #Dataquality #Dataobservability #Opentableformats #Apacheiceberg #Snowflake #ZoomInfo

We are incredibly excited to announce our new partnership with Amazon Web Services (AWS) 🎉 Telmai is officially now available on Amazon Web Services (AWS) Marketplace, making it easier than ever to embed advanced data observability and ML-based data quality workflows into your AWS ecosystem. Why use Telmai on AWS? ✅ Avoid lengthy procurement processes ✅ No need to request additional budget ✅ Consolidated spending using existing AWS account, credits, and billing set-up With faster procurement and deployment within just a few clicks, Telmai makes ensuring the reliability and integrity of that data easier than ever. Click on the link below to learn more about the Telmai and AWS partnership from the official press release: https://prn.to/3CUj3bz A massive thank you to Rishabh Dwivedi from the AWS team, as well as Ankita Sharma and Aditya Patil from the Clazar team for leaning in with their amazing high-touch support.Special mentions to our investors and our committed customers for believing in us and making this collaboration possible. We're excited about the journey ahead! #awsmarketplace #AWSstartups #DataGovernance #DataQuality #Partnerships 

Assessing data quality in various business areas can be challenging, particularly with the complexities introduced by AI adoption. Data inaccuracies and inconsistencies often remain unnoticed until it’s too late, putting your AI investments at risk. Join Maxim Lukichev, co-founder and CTO at Telmai, and Eric von der Linden, head of Sales Engineering, for our webinar, "𝗠𝗮𝗸𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗜-𝗥𝗲𝗮𝗱𝘆" and understand how to optimize your data quality workflows and ensure your data is ready for AI-driven success. Key points to be covered: ✅ The impact of silent data quality issues on AI initiatives ✅ Implement automated data quality across business-critical datasets ✅ Proven strategies for scaling data quality for AI workloads Learn how to identify and address data quality issues proactively before they derail your AI projects. Click on the link below to save a spot. https://bit.ly/4gLYYCd

We are incredibly excited to announce our new partnership with Amazon Web Services (AWS) 🎉 Telmai is officially now available on Amazon Web Services (AWS) Marketplace, making it easier than ever to embed advanced data observability and ML-based data quality workflows into your AWS ecosystem. Why use Telmai on AWS? ✅ Avoid lengthy procurement processes ✅ No need to request additional budget ✅ Consolidated spending using existing AWS account, credits, and billing set-up With faster procurement and deployment within just a few clicks, Telmai makes ensuring the reliability and integrity of that data easier than ever. Click on the link below to learn more about the Telmai and AWS partnership from the official press release: https://prn.to/3CUj3bz A massive thank you to Rishabh Dwivedi from the AWS team, as well as Ankita Sharma and Aditya Patil from the Clazar team for leaning in with their amazing high-touch support.Special mentions to our investors and our committed customers for believing in us and making this collaboration possible. We're excited about the journey ahead! #awsmarketplace #AWSstartups #DataGovernance #DataQuality #Partnerships 

Roses are red, Violets are blue, We can handle your bad data—but today, it’s about you 💕 Sending love, laughs, and maybe a few data puns your way this Valentine’s Day!💘 If you forgot a card, we’ve got your back ( and your heart) ❤️ #datahumour #dataengineer #dataquality #valentinesday #data #dataobservability

With Gartner Data & Analytics Summit less than a month away, now is the time to rethink whether your data is AI-ready AI success depends on reliable, high-quality data, yet data inconsistencies can silently impact your AI workloads. Swing by our booth #921, and let's chat about how Telmai can accelerate success for your AI initiatives and walk away with more than reliable data. Want to book a meeting? Head here: https://bit.ly/4aXCqgq See you there! ☀️ #GartnerDA #dataobservability #data #AI #dataanalytics #dataquality #gartner

Data lineage and data observability are two terms often used together but not always well understood. Is lineage just a map of where data flows? How does it fit into data observability, and how does it ensure data reliability? Many data teams struggle to define their boundaries and dependencies. However, with modern data ecosystems getting more complex, understanding this relationship has become crucial for data reliability. In this article, Mona Rakibe, CEO & Co-founder of Telmai, highlights how data lineage is a crucial pillar of data observability. She explains why lineage is more than just a passive record of data movement or a troubleshooting tool. She discusses how lineage helps data teams strengthen observability, accelerate root cause analysis, and ensure data reliability. Click on the link below to learn how modern data teams use lineage not only for reactive fixes but also to prevent data quality issues proactively. https://bit.ly/41aGyGx #datalineage #dataquality #dataobservability #data #datapipeline #datareliability #datatrust

Data is a multi-purpose asset, monetized and repurposed across multiple functions. But when it comes to data quality, who is responsible for it? 🤔 As AI takes center stage, data becomes a competitive differentiator, yet data quality ownership remains a gray area in most enterprises. The scale and performance of your selected foundation model are irrelevant if the underlying data is flawed, your insights will be, too. With multiple domain layers and hundreds of interconnected data products, defining data quality ownership often feels like drawing straws. Should engineers be solely responsible? Or do business teams also play a role? In this deep dive, we explore how traditional ownership models are breaking down and why organizations are transitioning to 𝗗𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗲𝗱 𝗗𝗮𝘁𝗮 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽, where everyone contributes with domain-aware approaches, turning data quality into an asset rather than a problem. 💡 Who is responsible for data quality in your organization? If the answer isn’t clear, this article is for you. Check out the link in the comments section to access the full article. #dataquality #dataengineering #datatrust #data #dataobservability #dataownership #dq

Measuring data quality across different business domains isn't always straightforward, especially when AI adoption adds layers of complexity. Data quality issues such as data drift, inaccurate or outdated data, and schema inconsistencies lurk silently and often emerge too late, putting your AI investments at risk. Join Maxim Lukichev, co-founder and CTO of Telmai, alongside Eric von der Linden, head of Sales Engineering, for a live webinar on February 20th focused on making your data AI-ready. They'll explore how Telmai can help data teams adopt a proactive, rather than reactive, approach to identifying data quality issues and orchestrating data quality workflows to prevent these issues from impacting your AI initiatives. Key points to be covered: ✅The impact of silent data quality issues on AI initiatives ✅Implementing automated data quality across business-critical datasets ✅Proven strategies for scaling data quality for AI workloads And there’s more! Click on the link in the comments section to save your spot! #dataquality #dataobservability #datatrust #dataprofiling #datavalidation #dq4ai #ai

Data architectures have evolved depending on the 'type' of workload they need to serve. Today, Data lakehouses are becoming the de facto standard for modern data architectures, with Enterprises rapidly adopting them to to unify vast, diverse datasets and bridge the gap between lakes and warehouses. However, not all data that enters a lakehouse is created equal. Without proper quality checks at every stage, lakehouses risk becoming data swamps. How do you ensure your lakehouse doesn’t turn into an expensive, ungoverned mess? Maxim Lukichev, co-founder and CTO of Telmai, has written a prescriptive guide on ensuring data quality for data lakehouses with the medallion architecture by structuring the lakehouse into Bronze, Silver, and Gold layers. He outlines the challenges at each stage, from pre-ingestion to analytics-ready stages, and explains the various data quality checks tailored for each stage to ensure high-quality, analytics-ready data. Don’t let poor-quality data slow down your data-driven initiatives. Click the link in the comments to read the full article and turn your lakehouse into a reliable source for decision-making. #dataquality #datalakehouse #data #dataobservability #lakehouse #deltalake #dataengineering